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SOMAIYA

VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program Manual

(Prepared based on the Guidelines for AICTE and University of Mumbai)

For

1. Computer Engineering
2. Information Technology
3. Electronics & Telecommunication Engineering
4. Artificial Intelligence & Data Science

(with effect from AY 2022-2023)

SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

ACADEMIC YEAR 2022-23
Honours Degree Programs in
Engineering and Technology Manual

Introduction:

As per the AICTE’s Approval Process Handbook-2020-21: Chapter VII- clause 7.3.2 (Page
99-101) and APH 2021-22, all branches of Engineering and Technology shall offer Elective
Courses in the EMERGING AREAS viz., Artificial Intelligence (AI), Internet of Things (IoT),
Blockchain, Robotics, Quantum Computing, Data Sciences, Cyber Security, 3D Printing and
Design, Augmented Reality/ Virtual Reality (AR/VR), as specified in Annexure 1 of the
Approval Process Handbook.

a) Under Graduate Degree Courses in EMERGING AREAS shall be allowed as

specialization from the same Department. The minimum additional Credits for such
Courses shall be in the range of 18-20 and the same shall be mentioned in the degree, as
specialization in that particular area. For example, doing extra credits for Robotics in
Mechanical Engineering shall earn B.E./ B.Tech. (Hons.) Mechanical Engineering with
specialization in Robotics
b) Minor specialization in EMERGING AREAS in Under Graduate Degree Courses may be
allowed where a student of another Department shall take the minimum additional Credits
in the range of 18-20 and get a degree with minor from another Department.

It is also made very clear by AICTE that areas in which Honours Degree may be offered are
numerous. It is up to the Universities with the help of their Academic Board/Council to decide
whether Honours. degree is to be offered or not in any particular area, which is not mentioned
above. The criteria for “Honours. Degree will cumulatively require additional 18 to 20 credits in
the specified area in addition to the credits essential for obtaining the Under Graduate Degree
in Major Discipline (i.e. 169 credits of KJSIEIT)”

1. Honours Degree under KJSIEIT:

Honours degree program is introduced in order to facilitate the students to choose additionally
the specialized courses in the emerging areas of their choice and build their competence in
such domains. Based on AICTE guidelines, KJSIEIT has proposed to offer following Honours
degree program corresponding to each engineering program as shown in Table 1.
Table 1: Honours Degree Programs

Sr. Honours degree programs

No
1 Artificial Intelligence and Machine Learning
2 Blockchain
3 Cyber Security
4 Augmented and Virtual Reality
5 Data Science
6 Internet of Things (IoT)

2. Honours Degree Eligibility Criteria for Students:

In view of the above-mentioned guidelines issued by AICTE in APH 2020-21 and APH 2021-22 for
offering Honours degree in the various engineering programs, the following recommendations are
proposed on the eligibility criteria for students opting for same;

i) Eligibility criteria for opting the Honours Degree program:

a. Students with no backlog in semester I, II, and III
b. The CGPI (based on semester I, II, and III) of the students must be 6.75 and above
c. For direct second year (DSE) admitted students - No backlog in semester III and CGPI
must be 6.75 and above

ii) Each eligible student can opt for maximum one Honour’s Programs at any time.

iii) Students registered for Honours Degree Program need to complete (clear/pass) Honours
Degree along with regular B Tech degree to get benefit of Award of Honours along with B
Tech Degree. Students with clear pass out in regular B Tech program and having ATKT in
Honours program; will only be awarded with regular B Tech degree.

iv) However it is optional ( not the compulsion) for eligible students to take
additional honours degree program.

v) Student shall complete Honors degree program in the stipulated four semesters only.

3. Examination and Evaluation of Honours Degree Courses:

Hons degrees courses will be offered in Third and Final Year of engineering as specialisation
in emerging areas. Modalities for Examination and Evaluation will be,

a. The continuous assessment (CA= Average of 2 tests+ Internal Assessment (IA)) and End
Sem. Examination (ESE) evaluation shall follow the same pattern as adopted for
corresponding semester stated by the University/ Autonomous Institute.
b. End semester Assessment will be done as per the laid down practices by following all
applicable ordinances and regulations of University of Mumbai/Rules stated in Manual of
KJSIEIT.
c. Hons. degree courses can be treated as Audit type of courses, wherein passing marks set
will be 40. If any student scored equal or more than passing marks in particular course can
be declared as pass.
d. Grading of courses offered under Honours degree shall be avoided and also not included
in overall CUMMULATIVE GRADE POINT AVERAGE, to bring parity with all students
admitted for the basic program.
e. Hons. degree shall be conferred in addition to basic degree only after successfully
completion of all courses.
f. Institute can make provision for entering pass or fail in course offered under Honours
degree.

4. Award of Honours Degree Program:

The students successfully completing the Honours Degree shall be awarded with the
degree designated as: “B. Tech. ( __________Engineering) (Hons. - Specialization)”

Example 1: Students s successfully completing B Tech in Computer Engineering with

specialization (Honours) in Cyber Security shall get a degree as “ B Tech (Computer
Engineering) ( Hons.- Cyber Security) ”

Example 2: Students successfully completing B Tech in Electronics and Telecommunication

Engineering with specialization in Internet of Things (IoT ) shall get a degree as
“B Tech ( Electronics and Telecommunication Engineering ) ( Hons.- Internet of
Things )”
5. Honours Degree Program Scheme and Structure:
Honours degree program be offered from academic year 2022-23 onwards along with KJSIEITs Autonomous Scheme II syllabus.
Honours credit courses will be offered from Semester V onwards to Semester VIII as shown in Table 2.

Table 2: Honours degree Program credit and Examination Scheme

Honours in ----- (With effect from 2022-23)

Teaching Scheme (Hours / Credits
Examination Scheme and Marks
Year Course Code Week)
& and Course Average of Internal End Sem. Term
Sem Title Seminar Oral/
Theory Pract. Test 1 & Assessment Exam Work Total Credits
/Tutorial Pract
Test 2
HXXC501:
04 -- -- 30 10 60 -- -- 100 04
TY TH Subject 1
Sem. V
Total Marks & Credits = 100 04

TY HXXC601:
04 -- -- 30 10 60 -- -- 100 04
Sem. VI TH Subject 2

Total Marks & Credits = 100 04

HXXC701:
04 -- -- 30 10 60 -- -- 100 04
LY TH Subject 3
Sem. VII HXXL701: Lab-1 -- -- 04 -- -- 50 50 100 02

Total Marks & Credits = 200 06

LY HXXC801:
04 - -- 30 10 60 -- -- 100 04
Sem. TH Subject 4
VIII
Total Marks & Credits = 100 04
Total Marks for Semesters V,VI, VII &VIII =100+100+200+100 = 500
Total Credits for Semesters V,VI, VII &VIII = 04+04+06+04 = 18
6. Honours Degree Programs offered for KJSIEIT:
Mapping with existing Engineering/Technology Programs of KJSIEIT- Honour’s degree programs
are conducted as per AICTE guidelines. Each eligible student can opt for maximum one Honour’s
Degree Programs at any time as shown in Table 3.
Table 3: Honours Programs offered for KJSIEITs Branches

Sr. No Honours Degree Programs who can offer this Honours Degree
Programs Program
1 Artificial Intelligence and 1. Computer Engineering
Machine Learning 2. Electronics and Telecommunication
Engineering
3. Information Technology
2 Blockchain 1. Computer Engineering
2. Electronics and Telecommunication
Engineering
3. Information Technology
4. Artificial Intelligence and Data Science
3 Cyber Security 1. Computer Engineering
2. Electronics and Telecommunication
Engineering
3. Information Technology
4. Artificial Intelligence and Data Science
4 Augmented and Virtual 1. Computer Engineering
Reality 2. Electronics and Telecommunication
Engineering
3. Information Technology
4. Artificial Intelligence and Data Science
5 Data Science 1. Computer Engineering
2. Electronics and Telecommunication
Engineering
3. Information Technology
6 IoT 1. Computer Engineering
2. Electronics and Telecommunication
Engineering
3. Information Technology
4. Artificial Intelligence and Data Science

Additional 4 Theory & One Lab courses to be cleared and evaluated under each Honours program
for total 18 credits and 500 marks, are as given under table 4 to 9 respectively.
Table 4: Honours Degree Program in Artificial Intelligence and Machine Learning

Honours Degree Sem Additional Subjects to be learnt and Credits Marks

Program passed through the examination
Artificial Intelligence and Sem V HXXC501: Mathematics for AI & ML 4 100
Machine Learning

Sem VI HXXC601: Game Theory using AI & 4 100

Sem VII HXXC701: AI&ML in Healthcare 4 100

Sem VII HXXL701: AI&ML in Healthcare lab 2 100

Sem VIII HXXC801: Text, Web and Social 4 100

Media Analytics

Total 4 Theory +1 Lab 18 500

Table 5: Honours Degree Program in Block chain

Honours Degree Sem Additional Subjects to be learnt and Credits Marks

Program passed through the examination
BlockChain Sem V HXXC501: Bit coin and Crypto 4 100
currency

Sem VI HXXC601: Block chain Platform 4 100

Sem VII HXXC701: Block chain Development 4 100

Sem VII HXXL701: Block chain Setup Lab 2 100

Sem VIII HXXC801: DeFi (Decentralized 4 100

Finance)

Total 4 Theory +1 Lab 18 500

Table 6: Honours Degree Program in Cyber Security

Honours Degree Sem Additional Subjects to be Credits Marks

Program learnt and passed through
the examination
Sem V HXXC501: Ethical Hacking 4 100

Sem VI HXXC601: Digital Forensic 4 100

Cyber Security Sem VII HXXC701: Security 4 100

Information Management

Sem VII HXXL701: Vulnerability 2 100

Assessment Penetration
Testing (VAPT) Lab
Sem VIII HXXC801: Application 4 100
Security

Total 4 Theory +1 Lab 18 500

Table 7: Honours Degree Program in Data Science

Honours Degree Sem Additional Subjects to be Credits Marks

Program learnt and passed through the
examination
Data Science Sem V HXXC501: Mathematics for 4 100
Data Science

Sem VI HXXC601: Statistical Learning 4 100

for Data Science

Sem VII HXXC701: Data Science for 4 100

Health and Social Care

Sem VII HXXL701: Data Science for 2 100

Health and Social Care Lab

Sem VIII HXXC801: Text, Web and 4 100

Social Media Analytics

Total 4 Theory +1 Lab 18 500

Table 8: Honours Degree Program in Augmented and Virtual Reality

Honours Degree Sem Additional Subjects to be Credits Marks

Program learnt and passed through
the examination
Sem V HXXC501: Virtual Reality 4 100

Sem VI HXXC601: AR and Mix 4 100

Reality
Augmented and Virtual
Reality Sem VII HXXC701: ARVR 4 100
Application-I

Sem VII HXXL701: ARVR Lab 2 100

Sem VIII HXXC801: Game 4 100

Development with VR

Total 4 Theory +1 Lab 18 500

Table 9: Honours Degree Program in Internet of Things

Honours Degree Sem Additional Subjects to be Credits Marks

Program learnt and passed through
the examination
Sem V HXXC501: IoT Sensor 4 100
Technologies

Sem VI HXXC601: IoT System 4 100

Design
Internet
of Sem VII HXXC701: Dynamic 4 100
Paradigm in IoT
Things
Sem VII HXXL701: Interfacing & 2 100
Programming with IoT Lab

Sem VIII HXXC801: Industrial IoT 4 100

Total 4 Theory +1 Lab 18 500

Dr. Suresh Ukarande

Principal
SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program

(Prepared based on the Guidelines for AICTE and University of Mumbai)

Artificial Intelligence
and Machine Learning
(with effect from AY 2022-2023)
K J Somaiya Institute of Engineering and Information Technology
An Autonomous Institute affiliated to University of Mumbai
Accredited by NAAC and NBA, Approved by AICTE, New Delhi
Bachelor of Technology in IT/CE/ET/ Engineering
(With effect from 2022-23)
Honours* in Artificial Intelligence and Machine Learning (AI&ML)
Teaching Scheme Hrs / Week Examination Scheme and Credit
Marks Scheme
Course Code &

Practic Av
Course Title

Assessment
Year & Sem

Term Work
Theo al Test Test era

End Sem

Practical
Internal
ge

Credits
ry -1 -2

Oral /
Exam

Total
TE HAIMLC5
Se 01:
m Mathemati 04 -- 30 30 30 10 60 -- -- 100 04
V cs for AI &
ML
Total 04 - -- 100 - - 100 04
Total Credits = 04

TE HAIMLC6
Se 01:
m Game 30
VI 04 -- 30 30 10 60 -- -- 100 04
Theory
using AI &
ML
Total 100
04 - - - 100 04
-
Total Credits = 04

BE HAIMLC7
Se 01:
m
04 -- 30 30 10 60 -- -- 100 04
AI&ML in 30
VII Healthcare
HAIMLSB
L701:
AI&ML in -- 04 -- -- 50 50 100 02
Healthcare
Lab
Total 04 04 100 50 50 200 06
Total Credits = 06

BE HAIMLC8
Se 01: 04 - 30 30 10 60 -- -- 100 04
m Text, Web 30
VII and Social
I Media
Analytics
Total 04 - - 100 - - 100 04
Total Credits = 04

Total Credits for Semesters V,VI, VII &VIII = 04+04+06+04 = 18

Course Course Teaching Scheme (Contact Credits Assigned
Code Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HAIMLC501 Mathematics
04 -- -- 04 -- -- 04
for AI&ML

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HAIMLC50 Mathematics
1 for AI&ML 30 30 30 10 -- -- -- 100
60

Course Prerequisites:
Applied Mathematics, Discrete mathematics
Course Objectives:
1 To build an intuitive understanding of Mathematics and relating it to Artificial
Intelligence, Machine Learning and Data Science.
2 To provide a strong foundation for probabilistic and statistical analysis mostly used in
varied applications in Engineering.
3 To focus on exploring the data with the help of graphical representation and drawing
conclusions.
4 To explore optimization and dimensionality reduction techniques.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Use linear algebra concepts to model, solve, and analyze real-world problems.
2 Apply probability distributions and sampling distributions to various business
problems.
3 Select an appropriate graph representation for the given data.
4 Apply exploratory data analysis to some real data sets and provide interpretations via
relevant visualization
5 Analyze various optimization techniques.
6 Describe Dimension Reduction Algorithms

Module
Topics Hrs.
No.
1.0 Linear Algebra 05
1.1 Vectors and Matrices, Solving Linear equations, The four Fundamental Subspaces,
Eigenvalues and Eigen Vectors, The Singular Value Decomposition (SVD).
2.0 Probability and Statistics 09
2.1 Introduction, Random Variables and their probability Distribution, Random
Sampling, Sample Characteristics and their Distributions, Chi-Square, t-, and F-
Distributions: Exact Sampling Distributions, Sampling from a Bivariate Normal
Distribution, The Central Limit Theorem.
3.0 Introduction to Graphs 10
3.1 Quantitative vs. Qualitative data, Types of Quantitative data: Continuous data,
Discrete data, Types of Qualitative data: Categorical data, Binary data, Ordinary
data, Plotting data using Bar graph, Pie chart, Histogram, Stem and Leaf plot, Dot
plot, Scatter plot, Time-series graph, Exponential graph, Logarithmic graph,
Trigonometric graph, Frequency distribution graph.
4.0 Exploratory Data Analysis 09
4.1 Need of exploratory data analysis, cleaning and preparing data, Feature engineering,
Missing values, understand dataset through various plots and graphs, draw
conclusions, deciding appropriate machine learning models.
5.0 Optimization Techniques 10
5.1 Types of optimization-Constrained and Unconstrained optimization, Methods of
Optimization-Numerical Optimization, Bracketing Methods-Bisection Method, False
Position Method, Newton‘s Method, Steepest Descent Method, Penalty Function
Method.
6.0 Dimension Reduction Algorithms 05
6.1 Introduction to Dimension Reduction Algorithms, Linear Dimensionality Reduction:
Principal component analysis, Factor Analysis, Linear discriminant analysis.
6.2 Non-Linear Dimensionality Reduction: Multidimensional Scaling, Isometric Feature
Mapping. Minimal polynomial
Total 48

Text Books:
1 Linear Algebra for Everyone,
2 Gilbert Strang, Wellesley Cambridge Press.
3 An Introduction to Probability and Statistics, Vijay Rohatgi, Wiley Publication
4 An introduction to Optimization, Second Edition, Wiley-Edwin Chong, Stainslaw Zak.
5 Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong,
Cambridge University Press.
6 Exploratory Data Analysis, John Tukey, Princeton University and Bell Laboratories.
References:
1 Introduction to Linear Algebra, Gilbert Strang.
2 Advanced Engineering Mathematics, Erwin Kreyszig
3 Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT
Press, 2018.
4 Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to
Algorithms. Cambridge University Press, 2014
5 Last updated on Sep 9, 2018.
6 Mathematics and Programming for Machine Learning with R, William B. Claster, CRC Press,2020
Useful Links:
1 https://math.mit.edu/~gs/linearalgebra/
2 https://www.coursera.org/learn/probability-theory-statistics
3 https://nptel.ac.in/courses/111/105/111105090/
4 https://onlinecourses.nptel.ac.in/noc21_ma01/preview
5 https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based on
remaining contents (approximately 40% syllabus but excluding contents covered in Test-1). Duration
of each test shall be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Course Teaching Scheme (Contact Credits Assigned
Code Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HAIMLC601 Game
Theory
04 -- -- 04 -- -- 04
using AI &
ML

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HAIMLC60 Game
1 Theory
30 30 30 10 60 -- -- -- 100
using AI &
ML

Course Prerequisites:
Knowledge of probability theory, discrete mathematics, and algorithm design is required.
Course Objectives:
1 To acquire the knowledge of game theory.
2 To understand the basic concept of AI, strength and weakness of problem solving and
search
3 To study about various heuristic and game search algorithms
4 To optimize the different linear methods of regression and classification
5 To interpret the different supervised classification methods of support vector machine.
6 To acquire the knowledge of different generative models through unsupervised
learning
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Understand basic concept of game theory.
2 Evaluate Artificial Intelligence (AI) methods and describe their foundations
3 Analyze and illustrate how search algorithms play vital role in problem solving,
inference, perception, knowledge representation and learning
4 Demonstrate knowledge of reasoning and knowledge representation for solving real
world problems
5 Recognize the characteristics of machine learning that makes it useful to realworld
problems and apply different dimensionality reduction techniques
6 Apply the different supervised learning methods of support vector machine and tree
based models

Module
Topics Hrs.
No.
1.0 Introduction to Game Theory 05
1.1 Introduction, The theory of rational choice, Games with Perfect Information, Nash
Equilibrium: Theory, Prisoner‘s Dilemma, Stag Hunt, Matching pennies, BOS, Multi
NE, Cooperative and Competitive Games, Strict and Non Strict NE, Best response
functions for NE.
1.2 Nash Equilibrium: Illustrations, Cournot‘s model of oligopoly, Bertrand‘s model of
oligopoly, Electoral competition, The War of Attrition, Auctions, Mixed Strategy
Equilibrium, Strategic games in which players may randomize, Dominated actions,
Extensive Games with Perfect Information
2.0 Games with Imperfect Information 09
2.1 Bayesian Games, Introduction, Motivational examples, General definitions, two
examples concerning information, Strictly Competitive Games and
Maxminimization, Rationalizability
2.2 Evolutionary Equilibrium, Monomorphic pure strategy equilibrium, Mixed strategies
and polymorphic equilibrium, Repeated games: The Prisoner‘s Dilemma, Infinitely
repeated games, Strategies, General Results,
3.0 Introduction to AI & Problem Solving 10
3.1 Definitions – Foundation and History of AI, Evolution of AI - Applications of AI,
Classification of AI systems with respect to environment. Artificial Intelligence vs
Machine learning,
3.2 Heuristic Search Techniques: Generate-and-Test; Hill Climbing; Properties of A*
algorithm, Best first Search; Problem Reduction.
3.3 Beyond Classical Search: Local search algorithms and optimization problem, local
search in continuous spaces, searching with nondeterministic action and partial
observation, online search agent and unknown environments
4.0 Knowledge and Reasoning 09
4.1 Knowledge and Reasoning: Building a Knowledge Base: Propositional logic, first order
Logic, situation calculus. Theorem Proving in First Order Logic, Planning, partial order
planning. Uncertain Knowledge and Reasoning, Probabilities,
4.2 Bayesian Networks. Probabilistic reasoning over time: time and uncertainty, hidden
Markova models, Kalman filter, dynamic bayesian network, keeping track of many objects
5.0 Introduction to ML 10
5.1 Introduction to Machine Learning, Examples of Machine Learning Applications, Learning
Types, Supervised Learning -Learning a Class from Examples, Vapnik- Chervonenkis
(VC) Dimension, Probably Approximately Correct (PAC) Learning, Noise, Learning
Multiple Classes, Regression, Model Selection and Generalization, Dimensions of a
Supervised Machine Learning Algorithm
5.2 Introduction, Linear Regression Models and Least Squares, Subset Selection, Shrinkage
Methods, Logistic Regression- Fitting Logistic Regression Models,
Quadratic Approximations and Inference, L1 Regularized Logistic Regression,
SVM-Introduction to SVM, The Support Vector Classifier, Support Vector Machines and
Kernels- Computing the SVM for Classification
6.0 Unsupervised Learning 05
6.1 Introduction, Association Rules-Market Basket Analysis, The Apriori Algorithm,
Unsupervised as Supervised Learning, Generalized Association Rules, Cluster Analysis
Proximity Matrices,
Clustering Algorithms-K-mean, Gaussian Mixtures as Soft K-means Clustering, Example:
Human Tumor Microarray Data, Vector Quantization, K-medoids, Hierarchical Clustering,
Self-Organizing Maps, PCA-Spectral Clustering
6.2 Hidden Markov Models-Introduction, Discrete Markov Processes, Hidden Markov Models,
Three Basic Problems of HMMs, Evaluation Problem, Finding the State Sequence, Learning
Model Parameters, Continuous Observations, The HMM with Input, Model Selection in
HMM
Total 48
Text Books:
1 Martin Osborne, An Introduction to Game Theory, Oxford University Press.
2 Russell, S. and Norvig, P. 2015. Artificial Intelligence - A Modern Approach, 3rd
edition,Prentice Hall
3 Introduction to Machine Learning Edition 2, by Ethem Alpaydin
References:
1 Thomas Ferguson, Game Theory, World Scientific, 2018.
2 Stef Tijs. Introduction to Game Theory, Hindustan Book Agency
3 J. Gabriel, Artificial Intelligence: Artificial Intelligence for Humans (Artificial Intelligence,
Machine Learning), Create Space Independent Publishing Platform, First edition , 2016
4 Introduction to Artificial Intelligence & Expert Systems, Dan W Patterson, PHI.,2010 2. S
Kaushik, Artificial Intelligence, Cengage Learning, 1st ed.2011
5 Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Code Course Teaching Scheme (Contact Credits Assigned
Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HAIMLC701 AI&ML in
04 -- -- 04 -- -- 04
Healthcare

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HAIML AI&ML in
C701 Healthcare 30 30 30 10 -- -- -- 100
60

Course Prerequisites:
Artificial Intelligence, Machine Learning
Course Objectives: The course aims
1 To understand the need and significance of AI and ML for Healthcare.
2 To study advanced AI algorithms for Healthcare.
3 To learn Computational Intelligence techniques .
4 To understand evaluation metrics and ethics in intelligence for Healthcare systems,
5 To learn various NLP algorithms and their application in Healthcare,
6 To investigate the current scope, implications of AI and ML for developing futuristic
Healthcare Applications.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Understand the role of AI and ML for handling Healthcare data.
2 Apply Advanced AI algorithms for Healthcare Problems.
3 Learn and Apply various Computational Intelligence techniques for Healthcare
Application.
4 Use evaluation metrics for evaluating healthcare systems.
5 Develop NLP applications for healthcare using various NLP Techniques..
6 Apply AI and ML algorithms for building Healthcare Applications

Module
Topics Hrs.
No.
1.0 Introduction 04
1.1 Overview of AI and ML,A Multifaceted Discipline, Applications of AI in Healthcare -
Prediction, Diagnosis, personalized treatment and behavior modification, drug
discovery, followup care etc,
1.2 Realizing potential of AI and ML in healthcare, Healthcare Data - Use Cases.
2.0 AI, ML, Deep Learning and Data Mining Methods for Healthcare 10
2.1 Knowledge discovery and Data Mining, ML, Multi classifier Decision Fusion, Ensemble
Learning, Meta-Learning and other Abstract Methods.
2.2 Evolutionary Algorithms, Illustrative Medical Application-Multiagent Infectious Disease
Propagation and Outbreak Prediction, Automated Amblyopia Screening System etc.
2.3 Computational Intelligence Techniques, Deep Learning, Unsupervised learning,
dimensionality reduction algorithms.
3.0 Evaluating learning for Intelligence 06
3.1 Model development and workflow, evaluation metrics, Parameters and Hyperparameters,
Hyperparameter tuning algorithms, multivariate testing, Ethics of Intelligence.
4.0 Natural Language Processing in Healthcare 08
4.1 NLP tasks in Medicine, Low-level NLP components, High level NLP components, NLP
Methods.
4.2 Clinical NLP resources and Tools, NLP Applications in Healthcare. Model Interpretability
using Explainable AI for NLP applications.
5.0 Intelligent personal Health Record 04
5.1 Introduction, Guided Search for Disease Information, Recommending SCA's.
Recommending HHP's , Continuous User Monitoring.
6.0 Future of Healthcare using AI and ML 07
6.1 Evidence based medicine, Personalized Medicine, Connected Medicine, Digital Health and
Therapeutics, Conversational AI, Virtual and Augmented Reality, Blockchain for verifying
supply chain, patient record access, Robot - Assisted Surgery, Smart Hospitals, Case Studies
on use of AI and ML for Disease Risk Diagnosis from patient data, Augmented reality
applications for Junior doctors.
6.2 Blockchain for verifying supply chain, patient record access, Robot - Assisted Surgery,
Smart Hospitals, Case Studies on use of AI and ML for Disease Risk Diagnosis from patient
data, Augmented reality applications for Junior doctors.
Total 48

Textbooks:
1 Arjun Panesar, "Machine Learning and AI for Healthcare”, A Press.
2 Arvin Agah, "Medical applications of Artificial Systems ", CRC Press
References:
1 Erik R. Ranschaert Sergey Morozov Paul R. Algra, “Artificial Intelligence in medical Imaging-
Opportunities, Applications and Risks”, Springer
2 Sergio Consoli Diego Reforgiato Recupero Milan Petkovid,“Data Science for Healthcare-Methodologies
and Applications”, Springer
3 Dac-Nhuong Le, Chung Van Le, Jolanda G. Tromp, Gia Nhu Nguyen, “Emerging technologies for health
and medicine”, Wiley.
4 Ton J. Cleophas • Aeilko H. Zwinderman, “Machine Learning in Medicine- Complete Overview”, Springer

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HAIML Text, Web
C801 and Social
30 30 30 10 60 -- -- -- 100
Media
Analytics

Course Prerequisites:
Python, Data Mining
Course Objectives: The course aims
1 To have a strong foundation on text, web and social media analytics.
2 To understand the complexities of extracting the text from different data sources and
analysing it.
3 To enable students to solve complex real-world problems using sentiment analysis and
Recommendation systems.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Extract Information from the text and perform data pre-processing
2 Apply clustering and classification algorithms on textual data and perform prediction.
3 Apply various web mining techniques to perform mining, searching and spamming of web
data.
4 Provide solutions to the emerging problems with social media using behaviour analytics and
Recommendation systems.
5 Apply machine learning techniques to perform Sentiment Analysis on data from social media.

Module
Topics Hrs.
No.
1.0 Introduction 06
1.1 Introduction to Text Mining: Introduction, Algorithms for Text Mining, Future Directions
1.2 Information Extraction from Text: Named Entity Recognition, Relation Extraction,
Unsupervised Information Extraction
1.3 Text Representation: tokenization, stemming, stop words, NER, N-gram modelling
2.0 Clustering and Classification 10
2.1 Text Clustering: Feature Selection and Transformation Methods, distance based Clustering
Algorithms, Word and Phrase based Clustering, Probabilistic document Clustering
2.2 Text Classification: Feature Selection, Decision tree Classifiers, Rule-based Classifiers,
Probabilistic based Classifiers, Proximity based Classifiers.
2.3 Text Modelling: Bayesian Networks, Hidden Markovian Models, Markov random Fields,
Conditional Random Fields
3.0 Web-Mining: 05
3.1 Introduction to Web-Mining: Inverted indices and Compression, Latent Semantic Indexing,
Web Search,
3.2 Meta Search: Using Similarity Scores, Rank Positons
3.3 Web Spamming: Content Spamming, Link Spamming, hiding Techniques, and Combating
Spam
4.0 Web Usage Mining: 05
4.1 Data Collection and Pre-processing, Sources and types of Data, Data Modelling, Session and
Visitor Analysis, Cluster Analysis and Visitor segmentation, Association and Correlation
Analysis, Analysis of Sequential and Navigational Patterns, Classification and Prediction
based on Web User Transactions.
5.0 Social Media Mining: 05
5.1 Introduction, Challenges, Types of social Network Graphs
5.2 Mining Social Media: Influence and Homophily, Behaviour Analytics, Recommendation in
Social Media: Challenges, Classical recommendation Algorithms, Recommendation using
Social Context, Evaluating recommendations.
6.0 Opinion Mining and Sentiment Analysis: 08
6.1 The problem of opinion mining,
6.2 Document Sentiment Classification: Supervised, Unsupervised
6.3 Opinion Lexicon Expansion: Dictionary based, Corpus based
6.4 Opinion Spam Detection: Supervised Learning, Abnormal Behaviours, Group Spam
Detection.

Total 48

Textbooks:
1 Daniel Jurafsky and James H. Martin, “Speech and Language Processing,” 3rd edition, 2020
2 Charu. C. Aggarwal, Cheng Xiang Zhai, Mining Text Data, Springer Science and Business Media, 2012.
3 BingLiu, “Web Data Mining-Exploring Hyperlinks, Contents, and Usage Data”, Springer, Second Edition, 2011.
4 Reza Zafarani, Mohammad Ali Abbasiand Huan Liu, “Social Media Mining- An Introduction”, Cambridge
University Press, 2014

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Course Code Course Examination Scheme

Name Theory Marks Exam Term Practical Total
Internal Assessment End Duration Work and
Sem. Oral
Exam.
Test1 Test2 Avg.
HXXSBL701 AI&ML in
Healthcare: -- --
Lab

Course Prerequisites:
Python
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Students will be able to understand computational models of AI and ML.
2 Students will be able to develop healthcare applications using appropriate
computational tools.
3 Students will be able to apply appropriate models to solve specific healthcare
problems.
4 Students will be able to analyze and justify the performance of specific models as
applied to healthcare problems.
5 Students will be able to design and implement AI and ML-based healthcare
applications.

Suggested Experiments:
Sr.
Name of the Experiment
No.
Introduction
1 Collect, Clean, Integrate and Transform Healthcare Data based on specific disease.
2 Perform Exploratory data analysis of Healthcare Data.
3 AI for medical diagnosis based on MRI/X-ray data.
4 AI for medical prognosis .
5 Natural language Entity Extraction from medical reports.
6 Predict disease risk from Patient data.
7 Medical Reviews Analysis from social media data.
8 Explainable AI in healthcare for model interpretation.
Mini Project-Design and implement innovative web/mobile based AI application using Healthcare
9
Data.
10 Documentation and Presentation of Mini Project.

Useful Links:
1 https://www.coursera.org/learn/introduction-tensorflow?specialization=tensorflow-in-practice
2 https://www.coursera.org/learn/convolutional-neural-networks-tensorflow?specialization=tensorflow-in-practice
3 https://datarade.ai/data-categories/electronic-health-record-ehr-data
4 https://www.cms.gov/Medicare/E-Health/EHealthRecords
5 https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction?specialization=tensorflow-in-practice

Term Work:
1 Term work should consist of 8 experiments and a Mini Project.
2 The final certification and acceptance of term work ensures satisfactory performance of laboratory
work and minimum passing marks in term work.
3 Total 25 Marks (Experiments: 10-Marks, Mini Project-10 Marks, Attendance Theory & Practical: 05-
marks)
Oral & Practical exam
1 Based on the entire syllabus of AI ML for Healthcare
SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program

(Prepared based on the Guidelines for AICTE and University of Mumbai)

Block Chain

(with effect from AY 2022-2023)

K J Somaiya Institute of Engineering and Information Technology
An Autonomous Institute affiliated to University of Mumbai
Accredited by NAAC and NBA, Approved by AICTE, New Delhi
Bachelor of Technology in IT/CE/ET/AI&DS Engineering
(With effect from 2022-23)

Honours* in Blockchain

Teaching Credit
Course Scheme Hours / Week Examination Scheme and Marks Schem
Year Code and e
& Course Practi Ave Oral
Internal End
Sem Title cal Test- rage Term /
Theory Test-2 Assess Sem Total Credits
1 Work Prac
ment Exam
t
HBCC501:
TE Bit coin
04 30 30 10 60 -- -- 100 04
Sem and Crypto 30
V currency
Total 04 - -- 100 - - 100 04
Total Credits = 04

TE HBCC601:
Sem. Blockchain 04 30 30 30 10 60 -- -- 100 04
VI Platform
Total 04 - - 100 - - 100 04
Total Credits = 04

HBCC701:
BE Block chain 30
04 30 30 10 60 -- -- 100 04
Sem. Developme
VII nt
HBCSBL6
01:
Private -- -- - -- -- 50 50 100 02
Blockchain 04
Setup Lab
Total 04 04 - 100 50 50 200 06
Total Credits = 06

BE HBCC801:
Sem. DeFi 30
VIII (Decentrali 04 30 30 10 60 -- -- 100 04
zed
Finance)
Total 04 - - 100 - - 100 04

Total Credits = 04

Total Credits for Semesters V,VI, VII &VIII = 04+04+06+04=18

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HBCC501 Bit coin and 04 -- -- 04 -- -- 04
Crypto
currency

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HBCC50 Bit coin and
1 Crypto 30 30 30 10 -- -- -- 100
60
currency

Course Objectives:
Course Objectives
The course aims:
1 To get acquainted with the concept of Block and Blockchain.
2 To learn the concepts of consensus and mining in Blockchain.
3 To get familiar with the bitcoin currency and its history.
4 To understand and apply the concepts of keys, wallets and transactions in the Bitcoin
Network.
5 To acquire the knowledge of Bitcoin network, nodes and their roles.
6 To analyze the applications& case studies of Blockchain.

Course Outcomes:
Course Outcomes Cognitive levels
of attainment as
per Bloom’s
Taxonomy Level
On successful completion, of course, learner/student will be able to:
1 Describe the basic concept of Block chain. L1,L2
2 Associate knowledge of consensus and mining in Block chain. L1,L2
3 Summarize the bit coin crypto currency at an abstract level. L1,L2
4 Apply the concepts of keys, wallets and transactions in the Bit coin network. L3
5 Interpret the knowledge of Bit coin network, nodes and their roles. L1,L2
6 Illustrate the applications of Block chain and analyze case studies. L3
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO Mapping
No.

0 Prerequisite Introduction to Cryptography: Hash 2 --

functions, Public key cryptography, Digital
Signature (ECDSA).
I Introduction to Structure of a Block, Block Header, Block 6 CO1
Block chain Identifiers: Block Header Hash and Block
Height, The Genesis Block, Linking Blocks in
the Block chain, Merkle Trees and Simplified
Payment Verification (SPV).
Self-learning Topics: Block chain Demo.
II Consensus and Decentralized Consensus, Byzantine General‘s 12 CO2
Mining Problem, Independent Verification of
Transactions, Mining Nodes, Aggregating
Transactions into Blocks, Constructing the
Block header, Mining the Block, Successfully
Mining the Block, Validating a New Block,
Assembling and Selecting Chains of Blocks,
Block chain Forks
Self-learning Topics: Study different
consensus algorithms
III Introduction to What is Bit coin and the history of Bit coin, 4 CO3
Bit coin Getting the first bit coin, finding the current
price of bit coin and sending and receiving bit
coin, Bit coin Transactions.
Self-learning Topics: Study the website
coinmarketcap.com/
IV Concepts of Bit Keys and addresses, Wallets and 13 CO4
coin Transactions: Public Key Cryptography and
Crypto currency, Private and Public Keys, Bit
coin Addresses, Base58 and Base58Check
Encoding, Nondeterministic (Random) Wallets,
Deterministic (Seeded) Wallets, HD Wallets
(BIP-32/BIP-44), Wallet Best Practices, Using
a Bit coin Wallets, Transaction Outputs and
Inputs, Transaction Fees, Transaction Scripts
and Script Language, Turing Incompleteness,
Stateless Verification, Script Construction
(Lock + Unlock), Pay-to-Public-Key-Hash
(P2PKH), Bitcoin Addresses, Balances, and
Other Abstractions
Self-learning Topics: Visit and use
https://bitcoin.org/en/
V Bit coin Peer-to-Peer Network Architecture, Node 7 CO5
Networks Types and Roles, Incentive based Engineering
The Extended Bitcoin Network, Bitcoin Relay
Networks, Network Discovery, Full Nodes,
Exchanging ―Inventory‖, Simplified Payment
Verification (SPV) Nodes, Bloom Filters, SPV
Nodes and Privacy, Encrypted and
Authenticated Connections, Transaction Pools
Self-learning Topics: Study technical papers
based on bitcoin security
VI Blockchain Domain-Specific Applications: FinTech, 8 CO6
Applications & Internet of Things, Industrial and
case studies Manufacturing, Energy, Supply chain &
Logistics, Records & Identities, Healthcare
Case studies related to cryptocurrencies
Concept of Altcoin
Self-learning Topics: Read Technical papers
on blockchain applications

Text Books:
1. ―Mastering Bitcoin, PROGRAMMING THE OPEN BLOCKCHAIN‖ , 2nd Edition by
Andreas M. Antonopoulos, June 2017, Publisher(s): O'Reilly Media, Inc. ISBN:
9781491954386.
2. ―Blockchain Applications: A Hands-On Approach‖, by ArshdeepBahga, Vijay Madisetti,
Paperback – 31 January 2017.
3. ―Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction‖, July 19,
2016, by Arvind Narayanan, Joseph Bonneau, Edwa rdFelten, Andrew Miller, Steven
Goldfeder, Princeton University Press.

Reference Books:
1. “Mastering Blockchain‖, by Imran Bashir, Third Edition,Packt Publishing
2. “Mastering Ethereum: Building Smart Contracts and Dapps Paperback‖ byAndreas Antonopoulos, Gavin
Wood, Publisher(s): O'Reilly Media
3. ―Blockchain revolution: how the technology behind bitcoin is changing money, business and the world $
don tapscott and alex tapscot, portfolio penguin, 856157449

Online References:
Sr. No. Website Name
1. https://andersbrownworth.com/blockchain/
2. https://andersbrownworth.com/blockchain/public-private-keys/
3. https://www.coursera.org/learn/cryptocurrency
4. https://coinmarketcap.com/
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HBCC601 Block chain 04 -- -- 04 -- -- 04
Platform

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HBCC60 Block chain
1 Platform 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 Understand the blockchain platform and its terminologies.
2 Understand smart contracts, wallets, and consensus protocols.
3 Design and develop decentralized applications using Ethereum, and Hyperledger.
4 Creating blockchain networks using Hyperledger Fabric deployment.
5 Understand the considerations for creating blockchain applications.
6 Analyze various Blockchain Platforms.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Explain the Blockchain platform and its types. L1,L2
2 Create Public Blockchain using Ethereum. L3,L4,L5, L6
3 Develop Smart Contracts using REMIX IDE. L3,L4,L5
4 Apply the concept of private blockchain using Hyperledger. L3
5 Analyze different types of blockchain platforms. L3,L4
6 Deploy Enterprise Applications on Blockchain. L3,L4,L5
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO Mapping
No.

0 Prerequisite Introduction to Block chain and Bit coin, 2 --

I Introduction to Why Blockchain Platform: Platform types, 6 CO1

Block chain Public, Private, technology requirements for
Platforms implementation.
Introduction to Ethereum, Hyperledger and
Smart Contracts. Case study of blockchain
Application.
Self-learning Topics: Study different
applications of block chain.
II Public Block Introduction, Characteristics of Public 8 CO2, CO3
chain Blockchain, Advantages.
Examples of Public Blockchain-Bitcoin:
Terminologies and Transaction, Ethereum:
Smart contract, Comparison of Bitcoin and
Ethereum, Other public Blockchain platforms.
Self-learning Topics: Study any one case
study on public block chain.
III Ethereum Introduction, Ethereum and Its Components: 12 CO2, CO3,
Blockchain Mining, Gas, Ethereum, Ether, Ethereum CO6
Virtual Machine, Transaction, Accounts.
Architecture of ethereum, Smart Contract:
Remix IDE, Developing smart contract for
ethereum blockchain, e-voting applications
using smart contract, Dapp Architecture.
Types of test-networks used in ethereum,
Transferring Ethers Using MetaMask, Mist
Wallet, Ethereum Frameworks, Case study of
Ganache for ethereum blockchain. Deploying
e-voting applications on Ganache framework.
Ethereum 2., Concept of Beacon chain, POS
(Proof of Stake), Shading of Chain.

Self-learning Topics: Study case study on any

ethereum blockchain.
IV Private Introduction, Key Characteristics, Need of 8 CO4
Blockchain Private Blockchain.
Consensus Algorithm for private Blockchain
(Ex. RAFT and PAXOS), Smart Contract in
Private Blockchain, Case Study of E-commerce
Website, Design Limitations.

Self-learning Topics: Case study on private

block chain.
V Hyperledger Introduction to Hyperledger, tools and 12 CO5, CO6
Blockchain frameworks, Hyperledger Fabric, Comparison
between Hyperledger Fabric & Other
Technologies, Distributed Ledgers.
Hyperledger Fabric Architecture, Components
of Hyperledger Fabric: MSP, Chain Codes
etc.,Transaction Flow, Advantages of
Hyperledger Fabric Blockchain, working of
Hyperledger Fabric, Creating Hyperlegder
network, Case Study of Supply chain
management using Hyperledger

Self-learning Topics: Case study on

Hyperledger blockchain.
VI Other 4 CO5
Blockchain Corda, Ripple, Quorum and other emerging
platforms blockchain platforms, Case Study on any of the
blockchain platforms.
Developing Blockchain application on
Cloud(AWS/Azure)
Self-learning Topics: Compare different
blockchain platforms.
Text Book:
1) Blockchain Technology, Chandramouli Subramanian, Asha A George, Abhillash K. A and
MeenaKarthikeyen, Universities press.
2) Mastering Ethereum, Building Smart Contract and Dapps, Andreas M. Antonopoulos Dr. Gavin
Wood, O'reilly.
Reference Books:

1) Blockchain for Beginners, Yathish R and Tejaswini N, SPD

2) Blockchain Basics, A non Technical Introduction in 25 Steps, Daniel Drescher, Apress.
3) Blockchain with Hyperledger Fabric,LucDesrosiers, Nitin Gaur, Salman A. Baset,
VenkatramanRamakrishna,Packt Publishing

E Books:

1) Blockchain By Example, BellajBadr, Richard Horrocks, Xun (Brian) Wu, November 2018,
Implement decentralized blockchain applications to build scalable Dapps.
2) Blockchain for Business, https://www.ibm.com/downloads/cas/3EGWKGX7.

Online References:
Sr. No. Website Name
1. https://www.hyperledger.org/use/fabric
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HBCC701 Block chain 04 -- -- 04 -- -- 04
Development

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HBCC70 Block chain
1 Developmen 30 30 30 10 -- -- -- 100
60
t

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To understand Ethereum Ecosystem.
2 To understand aspects of different programming languages.
3 To explain how to use the solidity programming language to develop a smart contract for
blockchain.
4 To demonstrate deployment of smart contracts using frameworks.
5 To understand principles of Hyperledger fabric.
6 To understand challenges to apply blockchain in emerging areas.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 To use Ethereum Components. L1,L2
2 To Analyse different blockchain programming languages. L3
3 To implement smat contract in Ethereum using solidity. L4,L5
4 To analyse different developement frameworks. L4
5 To implement private blockchin network with Hyperledger fabric. L4,L5
6 To illustrate blockchain integration with emerging technologies and security L1,L2
issues.
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Blockchain cryptocurrency, 2 --

Blockchain platform
I Ethereum Ethereum components: miner and 4 CO1
Ecosystem mining node,Ethereum virtual
machine,Ether,Gas,Transactions,
accounts,swarm and
whisper,Ethash, end to end
transaction in Ethereum,
architecture of Ethereum
Self-learning Topics: Emerging
blockchain platforms
II Blockchain Types of Blockchain Programming, 8 CO2
Programming Solidity, GoLang,Vyper, Java,
Simplicity, Rholang, Game Theory
and Cryptonomics, Comparative
study of different blockchain
programming languages
Decentralized file system-IPFS.
Self-learning Topics: Emerging
blockchain programming languages
III Smart Contract Solidity programming, Smart 10 CO3
Contract programming using
solidity, mapper function, ERC20
and ERC721 Tokens,comparison
between ERC20 & ERC721, ICO,
STOMetamask (Ethereum Wallet),
setting up development
environment, use cases of smart
contract, smart Contracts:
Opportunities, Risks
Self-learning Topics:
Cryptocurrencies and their security
issues, Consensus mechanisms,
Digital Signatures
IV Blockchain Ethereum client, Ethereum 10 CO4
Deployment Network, Introduction to Go
Ethereum(Geth), Geth Installation
and Geth CLI, Setting up a Private
Ethereum Blockchain. Introduction
to Truffle, Smart Contract
deployment on a Private
Blockchain.Introduction to
Ganache
Introduction to Dapp,Dapp
architecture, Daaps
Scalability,testing
Connecting to the Blockchain and
Smart Contract, Web3js,
Deployment

Self-learning Topics: Smart

Contract deployment using
Ganache.
V Hyperledger Installing Hyperledger Fabric, 12 CO5
Application Hyperledger Fabric Network
Development ,Building Your First
Network,Hyperledger Fabric
Demo,Hyperledger Fabric Network
Configuration, Certificate
Authorities,Chaincode
Development and Invocation,
Deployment and testing of
chaincode on development
network, Hyperledger Fabric
Transactions
Self-learning Topics: Hyperledger
sawtooth,Hyperledger caliper
VI Blockchain Integrating Blockchain with cloud, 6 CO6
integration and IoT, AI, ERP, End to end
Research blockchain integration, Risks and
challenges Limitations of Blockchain: Privacy
& Security. Criminal Use of
Payment Blockchains, The ―Dark‖
Side of Blockchain
Research challenges in blockchain,
Self-learning Topics:Use Cases:
Blockchain for Health
Insurance,Blockchain in Supply
chain management, Blockchain
&PropTech, Blockchain in Banking

Text Books:
1. Mastering Ethereum, Building Smart Contract and Dapps, Andreas M. Antonopoulos Dr.
Gavin Wood, O'reilly.
2. Blockchain Technology, Chandramouli Subramanian, Asha A George, Abhillash K. A and
Meena Karthikeyen, Universities press

References:
1. Blockchin enabled Applications,Vikram Dhillon,,DevidMetcalf,Max Hooper,Apress
2. Building Blockchain Projects,NarayanPrusty,Packt
Online References:
Sr. No. Website Name
1. https://ethereum.org/en/
2. https://www.trufflesuite.com/tutorials
3. https://hyperledger-fabric.readthedocs.io/en/release-2.2/whatis.html
4. https://www.blockchain.com/
5. https://docs.soliditylang.org/en/v0.7.4/

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Teaching Scheme
(Contact Hours) Credits Assigned
Course Code Course Title Theory Practical Tutorial Theory Practical Tutorial Total
& Oral
HBCSBL601 Private -- 4 -- -- 2 -- 02
Blockchain
Setup
Lab(SBL)

Examination Scheme

Theory Marks
Course Code Course Title Practical/
Internal assessment End Term
Total
Sem. Work Oral
Avg. of 2
Test1 Test 2 Exam
Tests
HBCSBL601 Private
Blockchain -- -- -- -- 50 50 100
Setup Lab

Lab Objectives:
Sr. No. Lab Objectives
The Lab aims:
1 To build and test Private Ethereum Blockchain.
2 To learn the concept of the genesis block and Account in the Blockchain.
3 To get familiar with the mining blocks to create a ether.
4 To understand and apply the concepts of keys, wallets.
5 To acquire the knowledge of gateway and desktop application.
6 To analyze the applications & case studies of Blockchain.

Lab Outcomes:
Sr. Lab Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of lab, learner/student will be able to:
1 To understand how blockchain systems (mainly Etherum) work . L1,L2
2 To create the genesis block using Puppeth, a CLI tool and account using L6
Smart Contract.
3 To create mining blocks, check the account and PoW. L6
4 To use cryptocurrency exchanges and wallets safely. L1,L2,L3
5 To create Gateway to Blockchain Apps. L6
6 To use Blockchain on Mobile App and on Cloud. L1,L2,L3

Prerequisite: Expertise in Programming, Basic knowledge of Computer Security, Networking.

Hardware & Software Requirements:

Hardware Requirements Software Requirements Other Requirements

PC With Following 1. NodeJs 1. Internet Connection.
Configuration
2. Ethereum
1. PC i3/i5/i7 Processor or
3.Geth
above.
4. Solidity
2. 4 GB RAM
3. 500 GB Harddisk
4. Network interface card
DETAILED SYLLABUS:

Note: All practical are to be conducted on Linux platform its Compulsory for this entire practical

Sr. Module . Description Hours LO

No Mapping

1 Build and Test Install Ethereum network to create a 4 LO1

private EthereumBlockchain Self- learning
topic :Hyperledger

2 Build and Test Installation of geth 5 LO1

3 Create the Create the genesis block using Puppeth, a 5 LO2

Genesis block CLI tool

4 Create Account Smart contract 6 LO2

in the blockchain

5 Mining Blocks to Mine blocks, check account balance, 6 LO3

create Ether PoWvsPoA

6 Gateway to Metamask 5 LO4

Blockchain Apps

7 Web and Solidity programming on remix 6 LO4

Desktop
Application

8 Application Crypto Exchange and Wallet 4 LO5

Development

9 Application Blockchain Mobile App or Web 6 LO6

Development Application using Dapp

10 Application Hosting of a private blockchain on 5 LO6

Development cloud(AWS/Azure)

Text Books:

1. Mastering Ethereum: Building Smart Contracts and Dapps, Andreas Antonopoulos,

Gavin Wood, O‘Reilly Publication
2. Mastering Blockchain, Second Edition: Distributed ledger technology, decentralization,
and smart contracts explained, 2nd Edition, Imran Bashir
3. Solidity Programming Essentials: A beginner's Guide to Build Smart Contracts for
Ethereum and Blockchain, RiteshModi, Packt publication
4. Mastering Blockchain, Imran Bashir, Second Edition ,Packt Publication.

References Books:

1. Mastering Bitcoin, PROGRAMMING THE OPEN BLOCKCHAIN , 2nd Edition by Andreas M.

Antonopoulos, June 2017, Publisher(s): O'Reilly Media, Inc. ISBN: 9781491954386.
2. Blockchain Applications: A Hands-On Approach, by ArshdeepBahga, Vijay Madisetti, Paperback –
31 January 2017.
3. Mastering Blockchain, Imran Bashir, Packt Publication.

Online References:

Sr. No. Website Name

1. https://geth.ethereum.org/downloads/
2. https://medium.com/@agrawalmanas09/how-to-setup-private-ethereum-
blockchain-on-windows-10-machine-ab497e03d6b8
3. https://geth.ethereum.org/docs/dapp/
4. https://www.edureka.co/blog/ethereum-private-network-tutorial
5. https://docs.soliditylang.org/en/develop/index.html
6. https://metamask.io
7. https://medium.com/publicaio/a-complete-guide-to-using-metamask-updated-
version-cd0d6f8c338f
8. https://docs.aws.amazon.com/blockchain-
templates/latest/developerguide/blockchain-templates-create-stack.html

Term Work:
The Term work shall consist of at least 10 to 12 practical based on the above syllabus. The term work
Journal must include at least 2 assignments. The assignments should be based on real world applications
which cover concepts from all above syllabus.

Term Work Marks: 50 Marks (Total marks) = 40 Marks (Experiment) + 5 Marks

(Assignments/tutorial/write up) + 5 Marks (Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.
Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HBCC801 DeFi 04 -- -- 04 -- -- 04
(Decentralize
d Finance)

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HBCC80 DeFi
1 (Decentraliz 30 30 30 10 -- -- -- 100
60
ed Finance)

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 The basic concepts of Centralized and Decentralized Finance and compare them.
2 The DeFi System and its key categories.
3 The DeFi components,primitives,incentives,metrics and major business models where they are
used.
4 The DeFi Architecture and EcoSystem.
5 The DeFi protocols.
6 The real time use cases of DeFi.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Explain the basic concepts of Centralized and Decentralized Finance and L1, L2
compare them.
2 Describe the the DeFi System and its key categories. L1
3 Discuss the DeFi components, primitives, incentives, metrics and major L1, L2
business models where they are used.
4 Explain the DeFi Architecture and EcoSystem. L1, L2
5 Illustrate the DeFi protocols. L1
6 Discuss the real time use cases of DeFi. L1,L2

DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Blockchain & Cryptocurrency,Blockchain 02 -

Platform, Blockchain Development

I Introduction: Difference between Centralized and 06 CO1

Decentralized Finance,Traditional Financial
Centralized and Institution- Banks: 1. Payment and
decentralized finance Clearance systems, 2.Accessibility,3.
Centralization and
Transparency,Decentralized Finance Vs
Traditional Finance

Self-learning Topics:
The Potential Impact of Decentralized
Finance
II What is decentralized The DeFi Ecosystem, Problems that DeFi 06 CO2
finance (defi)? Solves How Decentralized is DeFi? Defi key
Categories:-Stablecoins, Stable coin and
pegging,Lending and
Borrowing,Exchanges,Derivations, Fund
Management, Lottery,Payments,Insurance

Self-learning Topics:
How Decentralized Finance Could Make
Investing More Accessible.
III DeFi Primitives and 3.1 DeFi Components: Blockchain 10 CO3
Business Models Cryptocurrency The Smart Contract
Platform Oracles Stablecoins Decentralized
Applications
3.2 DeFi Primitives:Transactions Fungible
Token: Equity Tokens, Utility Tokens and
Governance TokensNFT: NFT Standard,
Multi-token standard Custody Supply
Adjustment: Burn-Reduce Supply, Mint-
Increase Supply, Bonding Curve-Pricing
Supply
Incentives: Staking Rewards, Slashing,
Direct Rewards and Keepers, Fees
Swap: Order Book Matching, Automated
Market Makers
Collaterlized Loans Flash Loans
(Uncollaterlized Loans)
3.3 DeFi Key Metrics:Total Value
Locked,Daily Active Users,Market Cap
3.4 DeFi Major Business
Models:Decentralized Currencies
,Decentralized Payment
Services,Decentralized
fundraising,Decentralized Contracting
Self-learning Topics: Study any real time
Business model.

IV DeFi Architecture and 4.1DeFi Architecture:Consumer Layer: 10 CO4

EcoSystem Blockchains, Cross-Blockchain networks,
Oracles,Digital Asset Layer:
Cryptocurrencies,Infrastructure Layer:
Wallets and Asset Management, DEXes and
Liquidity, Lending and Borrowing,
Prediction Markets, Synthetic Assets,
Insurance

4.2 DeFi EcoSystem and Protocols:On-chain

Asset Exchange,Loanable Fund Markets
on-chain assets,Stablecoins,Portfolio
Management,Derivatives,Privacy-preserving
mixers

4.3 DeFi Risk and Challenges:

Technical Risks,Usability Risks,
Centralization Risks,Liquidity
Risks,Regulation Risk
Self-learning Topics:Study of the Problems
which are holding DeFi adoption back
V DeFi Deep Dive 5.1.Maker DAO:Maker Protocol: Dai 10 CO5
Stablecoins, Maker Vaults, Maker Protocol
Auctions
Maker Actors: Keepers, Price Oracles,
Emergency Oracles, DAO Teams,Dai
Savings Rate
Dai Use case Benefits and Examples
5.2.UniSwap:UniSwap Protocol Overview:
How UniSwap Works, EcoSystem
Participants, Smart Contracts
UniSwap Core Concepts: Swaps, Pools,
Flash Swaps, Oracles
5.3. Compound:Compound Protocol:
Supplying Assets, Borrowing Assets,
Interest Rate Model
Compound Implementation and
Architecture: cToken Contracts, Interest
Rate Mechanics, Borrowing, Liquidation,
Price Feeds, Comptroller, Governance
5.4. wBTC:Need for wBTC: Tokenization
and common Issues
wBTC Implementation and Technology:
Users, Custodian Wallet Setup, Minting,
Burning
wBTC Governance, wBTC vs Atomic
Swaps, Fees, Legal Binding, Trust Model
and Transparency

Self-learning Topics:
MakerDAO Governance,UniSwap
GovernanceProtocol Math,Compound
Protocol Math
VI Use Cases 6.1Decentralized Exchanges 08 CO6
6.2Decentralized Stablecoins
6.3Decentralized Money Markets
6.4Decentralized Synthetix
6.5Decentralized Insurance
6.6Decentralized Autonomous Organization
(DAO),
Self-learning Topics:
Stock Exchange Operations,
Derivatives,Tether, Ampleforth, How to get
stablecoins,Synthetix Network, Token,The
Ongoing Impact of The DAO‘s Rise and
Fall, DAO Projects
Text Books:

1. How to DeFi,Darren Lau, Daryl Lau, Teh Sze Jin,Kristian Kho, Erina Azmi, TM Lee,Bobby Ong-1st
Edition, March 2020
2. DeFi and the Future of Finance-Campbell R. Harvey
3. DeFi Adoption 2020 A Definitive Guide to Entering the Industry

Reference Books/White Papers:

1. Blockchain disruption and decentralized finance: The rise of decentralized business models-Yan
Chen,Cristiano Bellavitis
2. SoK: Decentralized Finance (DeFi)-Sam M. Werner, Daniel Perez, Lewis Gudgeon,Ariah Klages-
Mundt,Dominik Harz∗‡, William J. Knottenbelt,Imperial College London, † Cornell University,
Interlay
4. Decentralized Finance (DeFi) –A new Fintech Revolution?
5. https://makerdao.com/da/whitepaper/
6. https://uniswap.org/
7. https://compound.finance/documents/Compound.Whitepaper.pdf
8. https://wbtc.network/assets/wrapped-tokens-whitepaper.pdf
9. https://defiprime.com/exchanges
10. https://defirate.com/stablecoins/
11. https://academy.ivanontech.com/blog/decentralized-money-markets-and-makerdao
12. https://www.gemini.com/cryptopedia/nexus-mutual-blockchain-insurance-nxm-crypto
13. https://consensys.net/blockchain-use-cases/decentralized-finance/
14. https://tokenlon.zendesk.com/hc/en-us/articles/360041114431-DeFi-Explained-Synthetic-Assets,
https://www.blockchain-council.org/synthetix/synthetix-snx-the-biggest-ecosystem-in-decentralized-
finance/

Online References:

Sr. No. Website Name

1. https://www.udemy.com/
2. https://www.coursera.org/

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program

(Prepared based on the Guidelines for AICTE and University of Mumbai)

Cyber Security

(with effect from AY 2022-2023)

Honours Program in Cyber Security

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai
Accredited by NAAC and NBA, Approved by AICTE, New Delhi

Bachelor of Technology in IT/CE/ET/AI&DS Engineering

(With effect from 2022-23)
Honours* in Cyber Security
Teaching Credit
Examination Scheme and
Course Code Scheme Hours / Week Sche
Marks
and Course me
Year
Title Intern End Ora
&Sem Term
Theo Practi Test Avera al Sem l/ Credit
Test-2 Wor Total
ry cal -1 ge Assess Exa Pra s
k
ment m ct
HCSC501:
TE Ethical 04 -- 30 30 30 10 60 -- -- 100 04
Sem Hacking
V Total 04 - -- 100 - - 100 04

Total Credits = 04

TE HCSC601:
Sem. Digital 04 -- 30 30 30 10 60 -- -- 100 04
VI Forensic
Total 04 - - 100 - - 100 04

Total Credits = 04

HCSC701:
BE Security 30 30
04 -- 30 10 60 -- -- 100 04
Sem. Information
VII Management
HCSSBL601:
Vulnerability
Assessment
-- 04 -- -- 50 50 100 02
Penetration
Testing
(VAPT) Lab
Total 04 04 100 50 50 200 06

Total Credits = 06

BE HCSC801:
Sem. Application 04 - 30 30 30 10 60 -- -- 100 04
VIII Security
Total 04 - - 100 - - 100 04
Total Credits = 04

Total Credits for Semesters V,VI, VII &VIII = 04+04+06+04=18

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HCSC501 Ethical 04 -- -- 04 -- -- 04
Hacking

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HCSC50 Ethical
1 Hacking 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To describe Ethical hacking and fundamentals of computer Network.
2 To understand about Network security threats, vulnerabilities assessment and social
engineering.
3 To discuss cryptography and its applications.
4 To implement the methodologies and techniques of Sniffing techniques, tools, and ethical
issues.
5 To implement the methodologies and techniques of hardware security.
6 To demonstrate systems using various case studies.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Articulate the fundamentals of Computer Networks, IP Routing and core L1,L2
concepts of ethical hacking in real world scenarios.
2 Apply the knowledge of information gathering to perform penetration testing L3
and social engineering attacks.
3 Demonstrate the core concepts of Cryptography, Cryptographic checksums L1,L2
and evaluate the various biometric authentication mechanisms.
4 Apply the knowledge of network reconnaissance to perform Network and L3
web application-based attacks.
5 Apply the concepts of hardware elements and endpoint security to provide L3
security to physical devices.
6 Simulate various attack scenarios and evaluate the results. L4,L5
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Computer Networks, Databases, system security 2 -

I Introduction to Fundamentals of Computer Networks/IP 10 CO1

Ethical protocol stack, IP addressing and routing,
Hacking Routing protocol, Protocol vulnerabilities, Steps
of ethical hacking, Demonstration of Routing
Protocols using Cisco Packet Tracer
Self-learning Topics:TCP/IP model, OSI
model
II Introduction to Private-key encryption, public key-encryption, 08 CO3
Cryptography key Exchange Protocols, Cryptographic Hash
Functions & applications, steganography,
biometric authentication, lightweight
cryptographic algorithms.Demonstration of
various cryptographic tools and hashing
algorithms
Self-learning Topics: Quantum cryptography,
Elliptic curve cryptography
III Introduction to Information gathering, reconnaissance, 12 CO2
network scanning, vulnerability assessment, Open VAS,
security Nessus, System hacking: Password cracking,
penetration testing, Social engineering attacks,
Malware threats, hacking wireless networks
(WEP, WPA, WPA-2), Proxy network, VPN
security, Study of various tools for Network
Security such as Wireshark, John the Ripper,
Metasploit, etc.
Self-learning Topics: Ransomware(Wannacry),
Botnets, Rootkits, Mobile device security
IV Introduction to OWASP, Web Security Considerations, User 10 CO4
web security Authentication, Cookies, SSL, HTTPS, Privacy
and Attacks on Web, Account Harvesting, Web Bugs,
Sniffing, ARP poisoning, Denial of service
attacks, Hacking Web Applications,
Clickjacking, Cross-Site scripting and Request
Forgery, Session Hijacking and Management,
Phishing and Pharming Techniques, SSO,
Vulnerability assessments, SQL injection, Web
Service Security, OAuth 2.0, Demonstration of
hacking tools on Kali Linux such as SQLMap,
HTTrack, hping, burp suite,Wireshark etc.
Self-learning Topics: Format string attacks
V Elements of Side channel attacks, physical unclonable 6 CO5
Hardware functions, Firewalls,Backdoors and trapdoors,
Security Demonstration of Side Channel Attacks on
RSA, IDS and Honeypots.
Self-learning Topics: IoT security
VI Case Studies Various attacks scenarios and their remedies. 4 CO6
Demonstration of attacks using DVWA.
Self-learning Topics: Session hijacking and
man-in-middle attacks
Text Books:
1. Computer Security Principles and Practice --William Stallings, Seventh Edition, Pearson Education, 2017
2. Security in Computing -- Charles P. Pfleeger, Fifth Edition, Pearson Education, 2015
3. Network Security and Cryptography -- Bernard Menezes, Cengage Learning, 2014
4. Network Security Bible -- Eric Cole, Second Edition, Wiley, 2011
5. Mark Stamp's Information Security: Principles and Practice --Deven Shah, Wiley, 2009

References:
1.UNIX Network Programming –Richard Steven,Addison Wesley, 2003
2. Cryptography and Network Security -- Atul Kahate, 3rd edition, Tata Mc Graw Hill, 2013
3.TCP/IP Protocol Suite -- B. A. Forouzan, 4th Edition, Tata Mc Graw Hill, 2017
4. Applied Cryptography, Protocols Algorithms and Source Code in C -- Bruce Schneier, 2nd
Edition / 20th Anniversary Edition, Wiley, 2015

Online Resources:
Sr. No. Website Name
3. https://www.owasp.org/index.php/Category:OWASP_Top_Ten_Project
4. https://dvwa.co.uk/
3. http://testphp.vulnweb.com/

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

End Semester Theory Examination will be of 60-Marks.

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HCSC601 Digital 04 -- -- 04 -- -- 04
Forensic

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HCSC60 Digital
1 Forensic 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To understand the various computer and cyber-crimes in the digital world.
2 To understand a significance of digital forensics life cycle, underlying forensics principles and
investigation process.
3 To understand the importance of File system management with respect to computer forensics.
4 To be able to identify the live data in case of any incident handling and application of
appropriate tools and practices for the same.
5 To Develop the skills in application of various tools and investigation report writing with
suitable evidences.
6 To be able to identify the network and mobile related threats and recommendation of suitable
forensics procedures for the same.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Identify and define the class for various computer and cyber-crimes in the digital L1,L2
world.
2 Understand the need of digital forensic and the role of digital evidence. L1,L2
3 Understand and analyze the role of File systems in computer forensics. L1,L2,L3
4 Demonstrate the incident response methodology with the best practices for L3
incidence response with the application of forensics tools.
5 Generate/Write the report on application of appropriate computer forensic tools L5
for investigation of any computer security incident .
6 Identify and investigate threats in network and mobile. L4
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Computer Hardware: Motherboard, 2 --

CPU, Memory: RAM, Hard Disk
Drive (HDD), Solid State Drive
(SSD), Optical drive
Computer Networks: Introduction
CN Terminology: Router, Gateway,
OSI and TCP/IP Layers
Operating Systems: Role of OS in
file management, Memory
management utilities, Fundamentals of
file systems used in Windows and
Linux.
I Introduction to 1.1 Definition and classification of 4 CO1
Cybercrime and cybercrimes: Definition, Hacking,
Computer-crime DoS Attacks, Trojan Attacks, Credit
Card Frauds, Cyber Terrorism, Cyber
Stalking.
1.2 Definition and classification of
computer crimes: Computer Viruses,
Computer Worms.
1.3 Prevention of Cybercrime: Steps
that can be followed to prevent
cybercrime, Hackers, Crackers,
Phreakers.
Self-learning Topics: Steps
performed by Hacker

II Introduction to 2.1 Introduction to Digital 5 CO2

Digital Forensics Forensics: Introduction to Digital
and Digital Forensics and lifecycle, Principles of
Evidences Digital Forensic.
2.2 Introduction to Digital
Evidences: Challenging Aspects of
Digital Evidence, Scientific Evidence,
Presenting Digital Evidence.
2.3 Digital Investigation Process
Models: Physical Model, Staircase
Model, Evidence Flow Model.

Self-learning Topics: Digital

Investigation Process Models
comparison and its application, Rules
of Digital Evidence.
III Computer 3.1 OS File Systems Review: 7 CO3
Forensics Windows Systems- FAT32 and NTFS,
UNIX File Systems, MAC File
Systems
3.2 Windows OS Artifacts: Registry,
Event Logs
3.3 Memory Forensics : RAM
Forensic Analysis, Creating a RAM
Memory Image, Volatility framework,
Extracting Information
3.4 Computer Forensic Tools: Need
of Computer Forensic Tools, Types of
Computer Forensic Tools, Tasks
performed by Computer Forensic
Tools
Self-learning Topics: Study of ‗The
Sleuth Kit‘ Autopsy tool for Digital
Forensics

IV Incident 4.1 Incidence Response 10 CO4

Response Methodology: Goals of Incident
Management, Response, Finding and Hiring IR
Live Data Talent
Collection and
4.2 IR Process: Initial Response,
Forensic
Investigation, Remediation, Tracking
Duplication
of Significant Investigative
Information.
4.3 Live Data Collection: Live Data
Collection on Microsoft Windows,
4.4 Forensic Duplication: Forensic
Duplicates as Admissible Evidence,
Forensic Duplication Tools: Creating a
Forensic evidence,
Duplicate/Qualified Forensic
Duplicate of a Hard Drive.
Self-learning Topics: Live Data
Collection on Unix-Based Systems

V Forensic Tools 5.1 Forensic Image Acquisition in 10 CO5

and Report Linux : Acquire an Image with dd
Writing Tools, Acquire an Image with
Forensic Formats, Preserve Digital
Evidence with Cryptography, Image
Acquisition over a Network, Acquire
Removable Media
5.2 Forensic Investigation Report
Writing: Reporting Standards, Report
Style and Formatting, Report Content
and Organization.
Self-learning Topics: Case study on
Report Writing
VI Network 6.1 Network Forensics: Sources of 14 CO6
Forensics and Network-Based Evidence, Principles
Mobile Forensics of Internetworking, Internet Protocol
Suite, Evidence Acquisition,
Analyzing Network Traffic: Packet
Flow and Statistical Flow, Network
Intrusion Detection and Analysis,
Investigation of Routers, Investigation
of Firewalls
6.2 Mobile Forensics: Mobile Phone
Challenges, Mobile phone evidence
extraction process, Android OS
Architecture, Android File Systems
basics, Types of Investigation,
Procedure for Handling an Android
Device, Imaging Android USB Mass
Storage Devices.
Self-learning Topic: Elcomsoft iOS
Forensic Toolkit, Remo Recover tool
for Android Data recovery

Text Books:
1. Digital Forensics by Dr. Dhananjay R. Kalbande Dr. Nilakshi Jain, Wiley Publications,
First Edition, 2019.
2. Digital Evidence and Computer Crime by Eoghan Casey, Elsevier Academic Press, Third
Edition, 2011.
3. Incident Response & Computer Forensics by Jason T. Luttgens, Matthew Pepe and Kevin
Mandia, McGraw-Hill Education, Third Edition (2014).
4. Network Forensics : Tracking Hackers through Cyberspace by Sherri Davidoff and
Jonathan Ham, Pearson Edu,2012
5. Practical Mobile Forensic by Satish Bommisetty, Rohit Tamma, Heather Mahalik,
PACKT publication, Open source publication, 2014 ISBN 978-1-78328-831-1
6. The Art of Memory Forensics: Detecting Malware and Threats in Windows, Linux, and
Mac Memory by Michael Hale Ligh (Author), Andrew Case (Author), Jamie Levy
(Author), AAron Walters (Author), Publisher : Wiley; 1st edition (3 October 2014),

References:
1. Scene of the Cybercrime: Computer Forensics by Debra Littlejohn Shinder, Syngress
Publication, First Edition, 2002.
2. Digital Forensics with Open Source Tools by Cory Altheide and Harlan Carvey, Syngress
Publication, First Edition, 2011.
3. Practical Forensic Imaging Securing Digital Evidence with Linux Tools by Bruce
Nikkel,NoStarch Press, San Francisco,(2016)
4. Android Forensics : Investigation, Analysis, and Mobile Security for Google Android by
Andrew Hogg, Elsevier Publication,2011
Online References:
Sr. Website Name
No.
1. https://www.pearsonitcertification.com/articles/article.aspx?p=462199&seqNum=2
2. https://flylib.com/books/en/3.394.1.51/1/
3. https://www.sleuthkit.org/autopsy/
4. http://md5deep.sourceforge.net/md5deep.html
5. https://tools.kali.org/
6. https://kalilinuxtutorials.com/
7. https://accessdata.com/product-download/ftk-imager-version-4-3-0
8. https://www.amazon.in/Art-Memory-Forensics-Detecting-Malware/dp/1118825098

Research Papers: Mobile Forensics/Guidelines on Cell Phone Forensics

1. Computer Forensics Resource Center: NIST Draft Special Publication 800-101 :
https://csrc.nist.gov/publications/detail/sp/800-101/rev-1/final
2. https://cyberforensicator.com/category/white-papers
3. https://www.magnetforensics.com/resources/ios-11-parsing-whitepaper/
4. Samarjeet Yadav , Satya Prakash , Neelam Dayal and Vrijendra Singh, "Forensics Analysis
WhatsApp in Android Mobile Phone", Electronic copy available
at: https://ssrn.com/abstract=3576379
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

End Semester Theory Examination will be of 60-Marks.

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HCSC701 Security 04 -- -- 04 -- -- 04
Information
Management

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HCSC70 Security
1 Information
30 30 30 10 60 -- -- -- 100
Managemen
t

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 The course is aimed to focus on cybercrime and need to protect information.
2 Understand the types of attacks and how to tackle the amount of risk involved.
3 Discuss the role of industry standards and legal requirements with respect to compliance.
4 Distinguish between different types of access control models, techniques and policy.
5 Awareness about Business Continuity and Disaster Recovery.
6 Awareness about Incident Management and its life cycle.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the scope of policies and measures of information security to L1,L2
people.
2 Interpret various standards available for Information security. L1,L2
3 Apply risk assessment methodology. L3
4 Apply the role of access control to Identity management. L3
5 Understand the concept of incident management, disaster recovery and L1,L2
business continuity.
6 Identify common issues in web application and server security. L3
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Vulnerability Assessment for Operating Systems, 2 --

Network (Wired and Wireless). Tools for
conducting Reconnaissance.
I Basics of 1.1 What is Information Security & Why do you 6 CO1,
Information need it? – CO2
Security 1.2 Basics Principles of Confidentiality, Integrity
1.3 Availability Concepts, Policies, procedures,
Guidelines, Standards
1.4 Administrative Measures and Technical
Measures, People, Process, Technology, IT ACT
2000, IT ACT 2008
Self-learning Topics: Impact of IT on
organizations, Importance of IS to Society
II Current Trends 2.1 Cloud Computing: benefits and Issues related to 8 CO2
in Information information Security.
Security 2.2 Standards available for InfoSec: Cobit, Cadbury,
ISO 27001, OWASP, OSSTMM.
2.3 An Overview, Certifiable Standards: How,
What, When, Who.
Self-learning Topics: Cloud Threats, Impact of
cloud computing on users, examples of cloud
service providers: Amazon, Google, Microsoft,
Salesforce etc.
III Threat & Risk 3.1 Threat Modelling: Threat, Threat-Source, 8 CO3
Management Vulnerability, Attacks.
3.2 Risk Assessment Frameworks: ISO 31010,
NIST-SP-800-30, OCTAVE
3.3 Risk Assessment and Analysis: Risk Team
Formation, Information and Asset Value, Identifying
Threat and Vulnerability, Risk Assessment
Methodologies
3.4 Quantification of Risk, Identification of
Monitoring mechanism, Calculating Total Risk and
Residual Risk.

Self-learning Topics: Risk management trends

today and tomorrow.
IV Identity and 4.1 Concepts of Identification, Authentication, 10 CO4
Access Authorization and Accountability.
Management 4.2 Access Control Models: Discretionary,
Mandatory, Role based and Rule-based.
4.3 Access Control Techniques: Constrained
User, Access control Matrix, Content-dependent,
Context – dependent
4.4 Access Control Methods: Administrative,
Physical, Technical, Layering of Access control
4.5 Access Control Monitoring: IDS and IPS and
anomaly detection.
4.6 Accountability: Event-Monitoring and log
reviews. Log Protection
4.7 Threats to Access Control: Various Attacks
on the Authentication systems.

Self-learning Topics: challenges and solutions in

identity and access management
V Operational 5.1 Concept of Availability, High Availability, 10 CO5
Security Redundancy and Backup.
5.2 Calculating Availability, Mean Time
Between Failure (MTBF), Mean Time to Repair
(MTTR)
5.3 Incident Management: Detection, Response,
Mitigation, Reporting, Recovery and Remediation
5.4 Disaster Recovery:
Metric for Disaster Recovery, Recovery Time
Objective (RTO), Recovery Point Objective (RPO),
Work Recovery Time (WRT), Maximum Tolerable
Downtime (MTD), Business Process Recovery,
Facility Recovery (Hot site, Warm site, Cold site,
Redundant site), Backup & Restoration

Self-learning Topics: Challenges and Opportunities

of Having an IT Disaster Recovery Plan

VI Web 6.1 Types of Audits in Windows Environment 8 CO6

Application, 6.2 Server Security, Active Directory (Group
Windows, and Policy), Anti-Virus, Mails, Malware
Linux security 6.3 Endpoint protection, Shadow Passwords,
SUDO users, etc.
6.4 Web Application Security: OWASP, Common
Issues in Web Apps, what is XSS, SQL
injection, CSRF, Password Vulnerabilities, SSL,
CAPTCHA, Session Hijacking, Local and
Remote File Inclusion, Audit Trails, Web Server
Issues, etc.
Self-learning Topics:, Network firewall
protection,Choosing the Right Web Vulnerability
Scanner

Textbooks:
1. Shon Harris, Fernando Maymi, CISSP All-in-One Exam Guide, McGraw Hill Education,
7th Edition, 2016.
2. Andrei Miroshnikov, Introduction to Information Security - I, Wiley, 2018
3. Ron Lepofsky, The Manager‘s Guide to Web Application Security, Apress; 1st ed. edition,
2014

References:
1. Rich-Schiesser, IT Systems Management: Designing, Implementing and Managing World
- Class Infrastructures, Prentice Hall; 2 edition, January 2010.
2. NPTEL Course: - Introduction to Information Security – I (URL:
https://nptel.ac.in/noc/courses/noc15/SEM1/noc15-cs03/)
3. Dr. David Lanter – ISACA COBIT – 2019 Framework - Introduction and Methodology
4. Pete Herzog, OSSTMM 3, ISECOM
5. NIST Special Publication 800-30, Guide for Conducting Risk Assessments, September
2012

Online References:
Sr. Website Name
No.
1. https://www.ultimatewindowssecurity.com/securitylog/book/Default.aspx
2. http://www.ala.org/acrl/resources/policies/chapter14
3. https://advisera.com/27001academy/what-is-iso-27001/
4. https://nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-30r1.pdf
5. http://www.diva-portal.org/smash/get/diva2:1117263/FULLTEXT01.pdf

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

End Semester Theory Examination will be of 60-Marks.

Teaching Scheme
(Contact Hours) Credits Assigned
Course Code Course Title Theory Practical Tutorial Theory Practical Tutorial Total
& Oral
HCSSBL601 Vulnerability -- 4 -- -- 2 -- 02
Assessment
Penetration
Testing
(VAPT) Lab

Examination Scheme

Theory Marks
Course Code Course Title Practical/
Internal assessment End Term
Total
Sem. Work Oral
Avg. of 2
Test1 Test 2 Exam
Tests
HCSSBL601 Vulnerability
Assessment
Penetration
-- -- -- -- 50 50 100
Testing
(VAPT) Lab
(SBL)

Lab Objectives:
Sr. No. Lab Objectives
The Lab aims:
1 To identify security vulnerabilities and weaknesses in the target applications.
2 To discover potential vulnerabilities which are present in the system in network using
vulnerability assessment tools.
3 To identify threats by exploiting them using penetration test attempt by utilizing the
vulnerabilities in a system
4 To recognize how security controls can be improved to prevent hackers gaining access controls
to database.
5 To test and exploit systems using various tools and understands the impact in system logs.
6 To write a report with a full understanding of current security posture and what work is
necessary to both fix the potential threat and to mitigate the same source of vulnerabilities in the
future

Lab Outcomes:
Sr. Lab Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of lab, learner/student will be able to:
1 Understand the structure where vulnerability assessment is to be performed. L1,L2
2 Apply assessment tools to identify vulnerabilities present in the system in L3
network.
3 Evaluate attacks by executing penetration tests on the system or network. L4
4 Analyse a secure environment by improving security controls and applying L5
prevention mechanisms for unauthorised access to database.
5 Create security by testing and exploit systems using various tools and L6
remove the impact of hacking in system.
6 Formation of documents as per applying the steps of vulnerabilities of L3, L4, L5
assessment and penetration testing.

Prerequisite: Computer Networks, Basic of Network Security.

Hardware & Software Requirements:

Hardware Requirements Software Requirements Other Requirements

PC With Following 1. Windows or Linux Desktop OS 1. Internet Connection.

Configuration
2. Security Software and tools
1. Intel PIV Processor

2. 4 GB RAM

3. 500 GB Harddisk

4. Network interface card

DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Computer Network, Basics of Network Security, 2

Ethical Hacking, Digital Forensics
I Human Security Visibility Audit: Collecting information through 8 LO1
(Social social media and internet. Collecting contact details
Engineering) (like phone number, email ID, What‘s App ID, etc)
Assessment Active Detection Verification: Test if the phone
number, email id etc are real by test message. Test
whether the information is filtered at point of
reception. Test if operator / another person assistance
can be obtained.
Device Information: IP Address, Port details,
Accessibility, Permissions, Role in business
Trust Verification: Test whether the information can
be planted in form of note / email / Message
(Phishing)
Test Subjects: College Staff, Reception, PA to
Director / Principal.
To conduct information gathering to conduct social
engineering audit on various sections in your college.
Self-Learning Topics: Networking Commands
II Network & Network Discovery: Using various tools to discover 8 LO2
Wireless Security the various connected devices, to get device name, IP
Assessment Address, relation of the device in network, Detection
of Active port, OS Fingerprinting, Network port and
active service discovery
Tools: IP Scanner, Nmap etc
Network Packet Sniffing: Packet Sniffing to detect
the traffic pattern, Packet capturing to detect protocol
specific traffic pattern, Packet capturing to reassemble
packet to reveal unencrypted password
Tools: Wireshark
Self-Learning Topics: Learning the CVE database for
vulnerabilities detected.
III Setting up Including an attacker machine preferably Kali and in 9 LO3
Pentester lab the same subnet victim machines either DVWA/
SEEDlabs/ multiple VULNHUB machines as and
when required. Understanding Categories of pentest
and legalities/ ethics.
Installed Kali machine on VM environment with some
VULNHUB machines and we can find out
vulnerability of Level 1-VULNHUB machine like
deleted system files, permissions of files.
Self learning Topics: Vulnerability exploitation for
acquire root access of the Kioptrx machine
IV Database and Database Password Audit: Tool based audit has to 9 LO4
Access Control be performed for strength of password and hashes.
Security Tools: DBPw Audit
Assessment Blind SQL Injection: Test the security of the
Database for SQL Injection
Tools: BSQL Hacker
Password Audit: Perform the password audit on the
Linux / Windows based system
Tools: Cain & Able, John the ripper, LCP Password
Auditing tools for Windows.
Active Directory and Privileges Audit: Conduct a
review of the Active Directory and the Group Policy
to assess the level of access privileges allocated.
Tools: SolarWinds
Self-Learning Topics: Federated Database security
challenges and solutions.

V Log Analysis Conduct a log analysis on Server Event Log / Firewall 6 LO5
Logs / Server Security Log to review and obtain
insights
Tools: graylog, Open Audit Module.
Self-Learning Topics: Python and R-Programming
scripts

VI Compliance and License Inventory Compliance: 10 LO6

Observation Identify the number of licenses and its deployment in
Reporting your organization.
Tools: Belarc Advisor, Open Audit Report
Writing: NESSUS tool
Report should contain:

a. Vulnerability discovered
b. The date of discovery
c. Common Vulnerabilities and Exposure (CVE)
database reference and score; those vulnerabilities
found with a medium or high CVE score should be
addressed immediately
d. A list of systems and devices found vulnerable
e. Detailed steps to correct the vulnerability, which
can include patching and/or reconfiguration of
operating systems or applications
f. Mitigation steps (like putting automatic OS updates
in place) to keep the same type of issue from
happening again
Purpose of Reporting: Reporting provides an
organization with a full understanding of their current
security posture and what work is necessary to both fix
the potential threat and to mitigate the same source of
vulnerabilities in the future.
Self-Learning Topics: Study of OpenVAS, Nikto,
etc.

Text & Reference Books and Links:

1. The Web Application Hacker's Handbook: Finding and Exploiting Security Flaws Paperback –
Illustrated, 7 October 2011 by Dafydd Stuttard
2. Hacking: The Art of Exploitation, 2nd Edition 2nd Edition by Jon Erickson
3. Important links of Vulnhub: Vulnhub Kioptrix
Download Link: https://www.vulnhub.com/entry/basic-pentesting-1,216/
https://www.vulnhub.com/entry/kioptrix-level-1-1,22/
Installation Video: https://youtu.be/JupQRHtfZmw
Walkthrough/solutions Video: https://youtu.be/Qn2cKYZ6kBI
4. OWASP Broken Web Application Projects
https://sourceforge.net/projects/owaspbwa/
5. Mastering Modern Web Penetration Testing By Prakhar Prasad, October 2016, Packt Publishing.
6. Kali Linux Revealed: Mastering the Penetration Testing Distribution – June 5, 2017 by Raphael
Hertzog (Author), Jim O'Gorman (Author), Offsec Press Publisher

Term Work:

The Term work shall consist of at least 10 to 12 practical based on the above syllabus. The term work
Journal must include at least 2 assignments. The assignments should be based on real world applications
which cover concepts from all above syllabus.

Term Work Marks: 50 Marks (Total marks) = 40 Marks (Experiment) + 5 Marks (Assignments/tutorial/write
up) + 5 Marks (Attendance)

Practical & Oral Exam: An Oral & Practical exam will be held based on the above syllabus.
Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HCSC801 Application 04 -- -- 04 -- -- 04
Security

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HCSC80 Application
1 Security 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 The terms and concepts of application Security, Threats, and Attacks
2 The countermeasures for the threats wrt Application security.
3 The Secure Coding Practices
4 The Secure Application Design and Architecture
5 The different Security Scanning and testing techniques
6 The threat modeling approaches

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Enumerate the terms of application Security, Threats, and Attacks L1
2 Describe the countermeasures for the threats with respect to Application L1
security.
3 Discuss the Secure Coding Practices. L2
4 Explain the Secure Application Design and Architecture. L2
5 Review the different Security Scanning and testing techniques. L2
6 Discuss the threat modeling approaches. L2
DETAILED SYLLABUS:

Sr. Module Detailed Content Hours CO

No. Mapping

0 Prerequisite Operating System, DBMS, Computer 02 -

Network, Web Programming, OOP
Introduction to Web Application 05 CO1
Reconnaissance, Finding Subdomains, API
I Introduction to
Analysis, Identifying Weak Points in
Application
Application Architecture
Security,
Threats, and Offense: Cross-Site Scripting (XSS), Cross-
Attacks Site Request Forgery (CSRF), XML External
Entity (XXE) Injection, Injection Attacks,
Denial of Service (DoS), Cross-Origin
Resource Sharing Vulnerabilities

Self-learning Topics: Simulate the attacks

using open-source tools in virtual
environment
09 CO2
II Defence and Securing Modern Web Applications, Secure
tools Application Architecture, Reviewing Code for
Security, Vulnerability Discovery, Defending
Against XSS Attacks, Defending Against
CSRF Attacks, Defending Against XXE,
Defending Against Injection attacks,
Defending Against DoS, Defending against
CORS based attacks

Self-learning Topics: Implement the

countermeasures to the attacks using open-
source tools

Security Requirements, Encryption, Never 09 CO3

Trust System Input, Encoding and Escaping,
III Secure Coding
Third-Party Components, Security Headers:
Practices
Seatbelts for Web Apps, Securing Your
Cookies, Passwords, Storage, and Other
Important Decisions, HTTPS Everywhere,
Framework Security Features, File Uploads,
Errors and Logging, Input Validation and
Sanitization, Authorization and
Authentication, Parameterized Queries, Least
Privilege, Requirements Checklist
Self-learning Topics: OWASP Secure
Coding Practices
09 CO4
IV Secure Secure Software Development Lifecycle
Application
Averting Disaster Before It Starts, Team
Design and
Roles for Security, Security in the Software
Architecture
Development Lifecycle,
Design Flaw vs. Security Bug,
Secure Design Concepts,
Segregation of Production Data,
Application Security Activities

Self-learning Topics: Secure Hardware

architecture
09 CO5
V Security Testing Your Code, Testing Your
Scanning and Application, Testing Your Infrastructure,
testing Testing Your Database, Testing Your APIs
and Web Services, Testing Your Integrations,
Testing Your Network, Dynamic Web
Application Profiling

Self-learning Topics: Open-source

Application Security Tools, IAST, RASP
and WAF, Selenium
09 CO6
VI Threat Objectives and Benefits of Threat
Modeling Modeling,
Defining a Risk Mitigation Strategy,
Improving Application Security, Building
Security in the Software Development Life
Cycle
Existing Threat Modeling Approaches
Security, Software, Risk-Based Variants
Threat Modeling Within the SDLC
Building Security in SDLC with Threat
Modeling, Integrating Threat Modeling
Within the Different Types of SDLCs,
Self-learning Topics: The Common
Vulnerability Scoring System (CVSS)
Text Books:
1.Alice and Bob Learn Application Security, by Tanya Janca Wiley; 1st edition (4 December
2020)
2. Web Application Security, A Beginner's Guide by Bryan Sullivan McGraw-Hill
Education; 1st edition (16 January 2012)
3. Web Application Security: Exploitation and Countermeasures for Modern Web
Applications by Andrew Hoffman Shroff/O'Reilly; First edition (11 March 2020)
4. The Security Development Lifecycle by Michael Howard Microsoft Press US; 1st edition
(31 May 2006)
5. Risk Centric Threat Modeling Process for Attack Simulation And Threat Analysis, Tony
Ucedavélez and Marco m. Morana, Wiley
6. Iron-Clad Java: Building Secure Web Applications (Oracle Press) 1st Edition by Jim
Manico

References:
1. Software Security: Building Security In by Gary McGraw Addison-Wesley Professional; 1st edition
(January 23, 2006)
2. A Guide to Securing Modern Web Applications by Michal Zalewski
3. Threat Modeling: A Practical Guide for Development Teams by Izar Tarandach and Matthew J.
Coles Dec 8, 2020
Online References:
Sr. Website Name
No.
1. https://owasp.org/www-project-top-ten/
2. https://owasp.org/www-pdf-archive/OWASP_SCP_Quick_Reference_Guide_v2.pdf
3. https://pentesterlab.com/
4. https://app.cybrary.it/browse/course/advanced-penetration-testing
5. https://www.udemy.com/
6. https://www.coursera.org/
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

End Semester Theory Examination will be of 60-Marks.

SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program

(Prepared based on the Guidelines for AICTE and University of Mumbai)

Data Science

(with effect from AY 2022-2023)

Honors* in Data Science

Teaching Scheme Hrs / Week Examination Scheme and Credit
Marks Schem
Course Code & Course

Practic Avera

Assessment
Year & Sem

Term Work
Theor al Test Test ge

End Sem

Practical
Internal

Credits
y -1 -2

Oral /
Exam

Total
Title

TE HDSC501
Se :
m Mathemat 30 1 6
V 04 -- 30 30 -- -- 100 04
ics for 0 0
Data
Science
100
-
Total 04 - -- - 100 04

Total Credits = 04

TE HDSC601
Se :
m Statistical 30 1 6 10
VI 04 -- 30 30 -- -- 04
Learning 0 0 0
for Data
Science
Total 100
-
04 - - - 100 04

Total Credits = 04

BE HDSC701
Se :
m Data 30
VII 1 6 10
Science 04 -- 30 30 -- -- 04
0 0 0
for Health
and Social
Care
HDSC701
: Data
Science 5 10
-- 04 -- -- 50 02
for Health 0 0
and Social
Care Lab
Total 5 20
04 04 100 50 06
0 0
Total Credits = 06

BE HDSC801
Se :
m Text, Web 30 1 6 10
VII 04 - 30 30 -- -- 04
and Social 0 0 0
I Media
Analytics
100 10
Total 04 - - - - 04
0
Total Credits = 04

Total Credits for Semesters V,VI, VII &VIII = 04+04+06+04 = 18

Course Course Teaching Scheme (Contact Credits Assigned
Code Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HDSC501 Mathematics
for Data 04 -- -- 04 -- -- 04
Science

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HDSC501 Mathematics
for Data 30 30 30 10 -- -- -- 100
Science 60

Course Prerequisites:
1 Applied Mathematics, Discrete Mathematics
Course Objectives:
1 To build an intuitive understanding of Mathematics and relating it to Data Analytics.
2 To provide a strong foundation for probabilistic and statistical analysis mostly used in
varied applications in Engineering.
3 To focus on exploring the data with the help of graphical representation and drawing
conclusions.
4 To explore optimization and dimensionality reduction techniques.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Use linear algebra concepts to model, solve, and analyze real-world problems.
2 Apply probability distributions and sampling distributions to various business
problems.
3 Select an appropriate graph representation for the given data analysis.
4 Apply exploratory data analysis to some real data sets and provide interpretations via
relevant visualization
5 Analyze various optimization techniques for data analysis.
6 Describe Dimension Reduction Algorithms in analytics

Module
Topics Hrs.
No.
1.0 Linear Algebra 05
1.1 Vectors and Matrices, Solving Linear equations, The four Fundamental Subspaces,
Eigenvalues and Eigen Vectors, The Singular Value Decomposition (SVD).
2.0 Probability and Statistics 09
2.1 Introduction, Random Variables and their probability Distribution, Random
Sampling, Sample Characteristics and their Distributions, Chi-Square, t-, and F-
Distributions: Exact Sampling Distributions, Sampling from a Bivariate Normal
Distribution, The Central Limit Theorem.
3.0 Introduction to Graphs 10
3.1 Quantitative vs. Qualitative data, Types of Quantitative data: Continuous data,
Discrete data, Types of Qualitative data: Categorical data, Binary data, Ordinary data,
Plotting data using Bar graph, Pie chart, Histogram, Stem and Leaf plot, Dot plot,
Scatter plot, Time-series graph, Exponential graph, Logarithmic graph, Trigonometric
graph, Frequency distribution graph.
4.0 Exploratory Data Analysis 09
4.1 Need of exploratory data analysis, cleaning and preparing data, Feature engineering,
Missing values, understand dataset through various plots and graphs, draw
conclusions, deciding appropriate machine learning models.
5.0 Optimization Techniques 10
5.1 Types of optimization-Constrained and Unconstrained optimization, Methods of
Optimization-Numerical Optimization, Bracketing Methods-Bisection Method, False
Position Method, Newton‘s Method, Steepest Descent Method, Penalty Function
Method.
6.0 Dimension Reduction Algorithms 05
6.1 Introduction to Dimension Reduction Algorithms, Linear Dimensionality Reduction:
Principal component analysis, Factor Analysis, Linear discriminant analysis.
6.2 Non-Linear Dimensionality Reduction: Multidimensional Scaling, Isometric Feature
Mapping. Minimal polynomial
Total 48

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each. Test-1
is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based on remaining
contents (approximately 40% syllabus but excluding contents covered in Test-1). Duration of each test shall
be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Course Teaching Scheme (Contact Credits Assigned
Code Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HDSC601 Statistical
Learning
04 -- -- 04 -- -- 04
for Data
Science

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HDSC601 Statistical
Learning
30 30 30 10 60 -- -- -- 100
for Data
Science

Course Prerequisites:
1 Engineering Mathematics, Probability and Statistics
Course Objectives:
1 To understand basic statistical foundations for roles of Data Scientist.
2 To develop problem-solving skills.
3 To infer about the population parameters using sample data and perform hypothesis
testing.
4 To understand importance and techniques of predicting a relationship between data and
determine the goodness of model fit.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Develop various visualizations of the data in hand.
2 Analyze a real-world problem and solve it with the knowledge gained from sampling
and probability distributions.
3 Analyze large data sets and perform data analysis to extract meaningful insights.
4 Develop and test a hypothesis about the population parameters to draw meaningful
conclusions.
5 Fit a regression model to data and use it for prediction.

Module
Topics Hrs.
No.
1.0 Introduction 08
1.1 Data and Statistics: Elements, Variables, and Observations, Scales of Measurement,
Categorical and Quantitative Data, Cross-Sectional and Time Series Data,
Descriptive Statistics, Statistical Inference, Descriptive Statistics: Tabular and
Graphical Summarizing Categorical Data, Summarizing Quantitative Data, Cross
Tabulations and Scatter Diagram.
1.2 Descriptive Statistics: Numerical Measures: Measures of Location, Measures of
Variability, Measures of Distribution Shape, Relative Location, and Detecting
Outliers, Box Plot, Measures of Association Between Two Variables
2.0 Probability 08
2.1 Probability : Experiments, Counting Rules, and Assigning Probabilities, Events
and Their Probabilities, Complement of an Event, Addition Law
Independent Events, Multiplication Law, Baye‘s theorem
2.2 Discrete Probability Distributions
Random Variables, Discrete Probability Distributions, Expected Value and
Variance, Binomial Probability Distribution, Poisson Probability Distribution
2.3 Continuous Probability Distributions: Uniform Probability Distribution, Normal
Curve, Standard Normal Probability Distribution, Computing Probabilities for Any
Normal Probability Distribution
3.0 Sampling and Sampling Distributions 05
3.1 Sampling from a Finite Population, Sampling from an Infinite Population, Other
Sampling Methods, Stratified Random Sampling, Cluster Sampling, Systematic
Sampling, Convenience Sampling, Judgment Sampling
3.2 Interval Estimation: Population Mean: Known, Population Mean: Unknown,
Determining the Sample Size, Population Proportion
4.0 Hypothesis Tests 05
4.1 Developing Null and Alternative Hypotheses, Type I and Type II Errors, Population
Mean: Known Population Mean: Unknown Inference About Means and Proportions
with Two Populations-Inferences About Population Variances, Inferences About a
Population Variance, Inferences About Two Population Variances
4.2 Tests of Goodness of Fit and Independence, Goodness of Fit Test: A Multinomial
Population, Test of Independence
5.0 Regression 08
5.1 Simple Linear Regression: Simple Linear Regression Model, Regression Model
and Regression Equation, Estimated Regression Equation, Least Squares Method,
Coefficient of Determination, Correlation Coefficient, Model Assumptions, testing
for Significance, Using the Estimated Regression Equation for Estimation and
Prediction Residual Analysis: Validating Model Assumptions, Residual Analysis:
Outliers and Influential Observations
5.2 Multiple Regression: Multiple Regression Model, Least Squares Method, Multiple
Coefficient of Determination, Model Assumptions, Testing for Significance,
Categorical Independent Variables, Residual Analysis
6.0 Time Series Analysis and Forecasting 05
6.1 Time Series Patterns, Forecast Accuracy, Moving Averages and Exponential
Smoothing, Trend Projection, Seasonality and Trend and Time Series
Decomposition
6.2 Nonparametric Methods
Sign Test, Wilcoxon Signed-Rank Test, Mann-Whitney-Wilcoxon Test, Kruskal-
Wallis Test, Rank Correlation
Total 48

Text Books:
1 https://static1.squarespace.com/static/5ff2adbe3fe4fe33db902812/t/6009dd9fa7bc363aa822d2c7
/1611259312432/ISLR+Seventh+Printing.pdf
2 Data Science from Scratch, FIRST PRINCIPLES WITH PYTHON, O‘Reilly, Joel Grus,
3 Data Science from Scratch (oreillystatic.com)
4 Practical Time Series Analysis, Prediction with statistics and Machine Learning, O‘Reilly,
Aileen Nielsen [DOWNLOAD] O'Reilly Practical Time Series Analysis PDF (lunaticai.com)
5 R for data science: Import, Tidy, Transform, Visualize, And Model Data, O‘Reilly , Garrett
Grolemund, Hadley Wickham
6 Python for Data Analysis, 2nd Edition, O'Reilly Media, Wes McKinney.
7 https://static1.squarespace.com/static/5ff2adbe3fe4fe33db902812/t/6009dd9fa7bc363aa822d2c7
/1611259312432/ISLR+Seventh+Printing.pdf
References:
1 Data Science for Dummies Paperback, Wiley Publications, Lillian Pierson
2 Storytelling with Data: A Data Visualization, Guide for Business Professionals, Wiley
Publications, Cole Nussbaumer Knaflic
3 Probability and Statistics for Engineering and the Sciences, Cengage Publications Jay L. Devore.

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each. Test-1
is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based on remaining
contents (approximately 40% syllabus but excluding contents covered in Test-1). Duration of each test shall
be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Course Teaching Scheme (Contact Credits Assigned
Code Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HDSC701 Data
Science
for
Health 04 -- -- 04 -- -- 04
and
Social
Care

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HDSC70 Data
1 Science for
30 30 30 10 60 -- -- -- 100
Health and
Social Care

Course Prerequisites:
Artificial Intelligence, Machine Learning
Course Objectives: The course aims
1 To gain perspective of Data Science for Health and Social Care.
2 To understand different techniques of Biomedical Image Analysis.
3 To learn NLP techniques for processing Clinical text.
4 To understand the role of social media analytics for Healthcare data .
5 To learn advanced analytics techniques for Healthcare Data.
6 To investigate the current scope, potential, limitations, and implications of data science
and its applications for healthcare.
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Identify sources and structure of healthcare data.
2 Apply structured lifecycle approach for handling Healthcare data science projects.
3 Analyze the data, create models, and identify insights from Healthcare data.
4 Apply various data analysis and visualization techniques for Healthcare and social
media data.
5 Apply various algorithms and develop models for Healthcare data science projects.
6 To Provide data science solutions for solving problems of Health and Social Care.

Module
Topics Hrs.
No.
1.0 Data Science for Healthcare 05
1.1 Introduction, Healthcare Data Sources and Data Analytics for Healthcare,
Applications and Practical Systems for Healthcare.
1.2 Electronic Health Records(EHR), Components of EHR, Benefits of EHR, Barriers
to Adopting EHR, Challenges of using EHR data, Phenotyping Algorithms
2.0 Biomedical Image Analysis 06
2.1 Biomedical Imaging Modalities, Object detection ,Image segmentation, Image
Registration, Feature Extraction
2.2 Mining of Sensor data in Healthcare, Challenges in Healthcare Data Analysis
2.3 Biomedical Signal Analysis, Genomic Data Analysis for Personalized Medicine.
3.0 Data Science and Natural Language Processing for Clinical Text 06
3.1 NLP, Mining information from Clinical Text, Information Extraction, Rule Based
Approaches, Pattern based algorithms, Machine Learning Algorithms.
3.2 Clinical Text Corpora and evaluation metrics, challenges in processing clinical
reports, Clinical Applications.
4.0 Social Media Analytics for Healthcare 06
4.1 Social Media analysis for detection and tracking of Infectious Disease outbreaks.
4.2 Outbreak detection, Social Media Analysis for Public Health Research, Analysis of
Social Media Use in Healthcare.
5.0 Advanced Data Analytics for Healthcare 08
5.1 Review of Clinical Prediction Models, Temporal Data Mining for Healthcare Data
5.2 Visual Analytics for Healthcare Data, Information Retrieval for Healthcare- Data
Publishing Methods in Healthcare.
6.0 Data Science Practical Systems for Healthcare 08
6.1 Data Analytics for Pervasive Health, Fraud Detection in Healthcare
6.2 Data Analytics for Pharmaceutical discoveries, Clinical Decision Support Systems
6.3 Computer-Assisted Medical Image Analysis Systems- Mobile Imaging and
Analytics for Biomedical Data.
Total 48

Textbooks:
1 Chandan K. Reddy and Charu C Aggarwal, ―Healthcare data analytics‖, Taylor & Francis, 2015.
2 Hui Yang and Eva K. Lee, ―Healthcare Analytics: From Data to Knowledge to Healthcare Improvement,
Wiley, 2016.
References:
1 Madsen, L. B. (2015). Data-driven healthcare: how analytics and BI are transforming the industry. Wiley
India Private Limited
2 Strome, T. L., & Liefer, A. (2013). Healthcare analytics for quality and performance improvement.
Hoboken, NJ, USA: Wiley
3 McNeill, D., & Davenport, T. H. (2013). Analytics in Healthcare and the Life Sciences: Strategies,
Implementation Methods, and Best Practices. Pearson Education.
4 Rachel Schutt and Cathy O‘Neil, ―Doing Data Science‖, O‘Reilly Media
5 Joel Grus, Data Science from Scratch: First Principles with Python, O'Reilly Media
6 EMC Education Services,‖Data Science and Big Data Analytics‖,Wiley

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each. Test-1
is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based on remaining
contents (approximately 40% syllabus but excluding contents covered in Test-1). Duration of each test shall
be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Course Teaching Scheme (Contact Credits Assigned
Code Name Hours)
Theory Practical Tutorial Theory Practical Tutorial Total
HDSC801 Text, Web
and Social
04 -- -- 04 -- -- 04
Media
Analytics

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HDSC80 Text, Web
1 and Social
30 30 30 10 60 -- -- -- 100
Media
Analytics

Total 48

Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each. Test-1
is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based on remaining
contents (approximately 40% syllabus but excluding contents covered in Test-1). Duration of each test shall
be one hour.
Internal Assessment(IA) (10 marks):
Marks will be allotted as per designed rubrics.

Course Code Course Examination Scheme

Name Theory Marks Exam Term Practical Total
Internal Assessment End Duration Work and
Sem. Oral
Exam.
Test1 Test2 Avg.
HXXSBL701 Data
Science for
Health 50 50 100
and Social
Care: Lab

Course Prerequisites:
Python
Course Outcomes:
After successful completion of the course, the student will be able to:
1 Students will be able to, Identify sources of data, suggest methods for collecting,
sharing and analyzing Healthcare data.
2 Students will be able to Clean, integrate and transform healthcare data.
3 Students will be able to apply various data analysis and visualization techniques
on healthcare data.
4 Students will be able to apply various algorithms and develop models for healthcare
data Analytics .
5 Students will be able to implement data science solutions for solving healthcare
problems.

Suggested Experiments:
Sr. No. Name of the Experiment
Introduction
1 Clean, Integrate and Transform Electronic Healthcare Records.
2 Apply various data analysis and visualization techniques on EHR.
3 Bio Medical Image Preprocessing, Segmentation.
4 Bio Medical Image Analytics.
5 Text Analytics for Clinical Text Data.
6 Diagnose disease risk from Patient data.
7 Social Media Analytics for outbreak prediction/ Drug review analytics.
8 Visual Analytics for Healthcare Data.
9 Implement an innovative Data Science application based on Healthcare Data.
10 Documentation and Presentation of Mini Project.

Useful Links:
1 http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning
2 http://www.cse.wustl.edu/~kilian/cse517a2010/
3 https://datarade.ai/data-categories/electronic-health-record-ehr-data
4 https://www.cms.gov/Medicare/E-Health/EHealthRecords
5 https://onlinecourses.nptel.ac.in/noc20_ee40

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program

(Prepared based on the Guidelines for AICTE and University of Mumbai)

Augmented and Virtual Reality

(with effect from AY 2022-2023)

K J Somaiya Institute of Engineering and Information Technology
An Autonomous Institute affiliated to University of Mumbai
Accredited by NAAC and NBA, Approved by AICTE,
New Delhi
Bachelor of Technology in IT/CE/ET/AI&DS Engineering
(With effect from 2022-23)

Honours* in Augmented and Virtual Reality

Teaching Examination Scheme and Credit

Course Code Scheme Hours / Week Marks Scheme
Yea
and Course Intern End Ora
r & Term
Title Theo Test Avera al Sem l/
Sem Test-2 Wor Total Credits
ry Practi -1 ge Assess Exa Pra
k
cal ment m ct
HVARC501:
TE Virtual 04 -- 30 30 30 10 60 -- -- 100 04
Sem Reality
V Total 04 - -- 100 - - 100 04
Total
Credits = 04

TE HVARC601:
Sem. AR and Mix 04 -- 30 30 30 10 60 -- -- 100 04
VI Reality
Total 04 - - 100 - - 100 04
Total
Credits = 04

HVARC701:
BE ARVR 04 -- 30 30 30 10 60 -- -- 100 04
Sem. Application-I
VII HVARSBL60
1: -- 04 -- -- 50 50 100 02
ARVR Lab
Total 04 04 100 50 50 200 06
Total Credits = 06

BE HVARC801:
Sem. Game 30 30
04 - 30 10 60 -- -- 100 04
VIII Development
with VR
Total 04 - - 100 - - 100 04

Total Credits = 04

Total Credits for Semesters V,VI, VII

&VIII = 04+04+06+04 = 18
Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HVARC501 Virtual 04 -- -- 04 -- -- 04
Reality

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HVARC5 Virtual
01 Reality 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To understand primitives of computer graphics fundamental.
2 To analyze various Hardware devices suitable for VR.
3 To analyze visual physiology and issues related to it.
4 To apply the knowledge of Visual rendering.
5 To evaluate problems faced due to audio scattering in VR.
6 To create different interface in VR environment.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Solve Computer Graphics Problems. L1
2 Analyze application of VR hardware and software components. L1, L2, L3
3 Identify issues related to visual physiology. L1, L2
4 Integrate various shading and rendering techniques. L6
5 Solve problems due to Audio distortions. L5
6 Create User Interface for VR. L6

Prerequisite:
Basic C programming
DETAILED SYLLABUS:

Sr. Module Detailed Content Hours CO

No. Mapping

0 Prerequisite Functioning of human sensory organs 02 --

– EYE, Ear, Touch etc.
Light and Lenses
Basic functioning of camera
Matrix multiplication
I Geometry of Geometric Modeling, 2D 10 CO1
Virtual World transformations, Homogenous
coordinate system, 3D rotation and 6
degree of freedom, Viewport
Transformation
Self: Eye Transformation, demo of 2D
transformation
II Introduction to VR Introduction to VR and definitions and 07 CO2
its components.,
Hardware components: Display
devices: LCD, OLED
Audio: Speakers, Earphones, Bone
conduction
Touch: Haptic Device
GPU and CPU, Input devices like
game controller, data glows, Joysticks
Tracking Hardware: Industrial
measurement Unit-IMU, Gyroscope,
accelerometer
Software component: Java3D, VRML
Self: Feedback mechanisms in
VR environment

III Visual Physiology, Functioning of Eye with 08 CO3

perception and photoreceptors, Resolution for VR,
tracking Eye movements and issues with it in
VR, Neuroscience of vision, Depth and
motion perception, Frame rates and
display, Orientation tracking, tilt and
yaw drift correction, Tracking with
camera
Self: Light House approach
IV Visual Rendering Overview, shading models, rendering 09 CO4
pipelines, rasterization, pixel shading,
Distortion shading, post rendering
image wrap
Self: Rendering for VR application
V Audio Physics of Audio, Auditory Perception, 10 CO5
localization, rendering, Problems due
to scattering of audio
Self: Study reaction of audio and other
senses for VR environment
VI Interfaces Locomotion, Manipulation, system 06 CO6
control, social interaction using open-
source tool like Gopro VR etc.
Self: Explore tools for UI in VR

Text Books:
1. Hearn and Baker, ―Computer Graphics- C version‖, 2nd edition, Pearson, 2002.
2. R. K Maurya, ―Computer Graphics with Virtual Reality‖, 3rd Edition, Wiley India, 2018.
3. Steven M. LaVelle,‖ Virtual Reality‖, Cambridge University press, 2019
4. Grigore Burdea, Philippe Coiffet, ―Virtual Reality Technology‖, 2nd Edition, Wiley India,
2003
5. Vince, ―Virtual Reality Systems‖, 1st Edition, Pearson Education, 2002

References:
1. George Mather, ―Foundations of Sensation and Perception‖, Psychology Press book; 3rd
Edition, 2016
2. Tony Parisi, ― Learning Virtual Reality‖, 1st edition, O‘Reilly, 2015
3. Alan Craig and William Sherman,‖ Understanding virtual reality: Interface, application and
design‖, 2nd Edition, Morgan Kaufmann Publisher, 2019
4. Peter Shirley, Michael Ashikhmin, and Steve Marschner, ―Fundamentals of Computer
Graphics‖ ,A K Peters/CRC Press; 4th Edition, 2016.

Online Resources:
Sr. No. Website Name
5. https://nptel.ac.in/courses/121/106/121106013/#
6. http://msl.cs.uiuc.edu/vr/
3. http://lavalle.pl/vr/
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks)
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks.

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HVARC601 AR and Mix 04 -- -- 04 -- -- 04
Reality

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HVARC6 AR and Mix
01 Reality 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To understand the concepts of Augmented Reality and related technologies.
2 To understand the AR tracking system and use of computer vision in AR/MR.
3 To describe the technology for multimodal user interaction and authoring in AR.
4 To use different AR toolkits and apply them to develop AR applications.
5 To demonstrate AR Applications using Mobile AR Toolkits and SDKs.
6 To understand the use of AR/MR in interdisciplinary immersive applications.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Identify and compare different Augmented Reality and Mixed Reality L1, L2
Technologies.
2 Apply concepts of Computer Vision for tracking in AR and MR Systems. L3
3 Model different interfaces and authoring in AR/MR. L3
4 Design AR/MR applications using open source platforms and toolkits. L6
5 Design Mobile based AR Applications. L6
6 Apply insights of AR/MR in different applications. L3

Prerequisite: Programming Language, Computer Graphics, Virtual Reality

DETAILED SYLLABUS:
Module Title Description Hours CO
0 Pre-requisite Basics of Computer Graphics, Coordinate 02 --
Systems, VR Introduction, Tracking in VR

I Introduction to Definition and Scope, A Brief History of 06 CO1

Augmented Reality Augmented Reality, AR Architecture, Related
and Mixed Reality Fields of AR (like Mixed Reality, Virtual
Reality, Immersive Reality, Extended Reality)
and Their comparison, General Architecture of
Mixed Reality System, Algorithm Steps in
Mixed Reality

Self-Learning Topics: How AR/MR are related

to Ubiquitous Computing, Multidimensional
Systems.

II Tracking and Multimodal Displays; Visual Perception; Spatial 07 CO2

Computer Vision for Display Model; Visual Displays; Tracking,
AR and MR Calibration and Registration; Coordinate
Systems; Characteristics of Tracking
Technology; Stationary Tracking Systems;
Mobile Sensors; Optical Tracking; Sensor
Fusion; Marker Tracking; Multiple Camera
Infrared Tracking; Natural Feature Tracking by
Detection; Incremental Tracking; Simultaneous
Localization and Tracking; Outdoor Tracking

Self-Learning Topics: Indoor Tracking, Full

Body Tracking

III Interaction, Output Modalities, Input Modalities, Tangible 08 CO3

Modeling and Interfaces, Virtual User Interfaces on Real
Annotation and Surfaces, Multi-view Interfaces, Haptic
Authoring Interaction, Multimodal Interaction, Specifying
Geometry, Specifying Appearance, Semi-
automatic Reconstruction, Free-form Modeling,
Annotation, Requirement of AR Authoring,
Elements of Authoring, Stand-alone Authoring
Solutions, Plug-in Approaches, Web
Technology

Self-Learning Topics: Case Study on Object

Annotation in Real Time, Avatar Modeling.
IV Software AR Application Requirements, Software 10 CO4
Architecture in AR Engineering Requirements, Distributed Object
and AR Systems, Data Flow, Scene Graphs; Developer
Development Support: Parameter Configuration, Declarative
Toolkits Scripting, Procedural Scripting, Mixed
Language Programming, Runtime
Reconfiguration, Choosing an AR Platforms and
Toolkits; AR Non-programming Frameworks,
AR Programming Frameworks, Programming
AR using ARToolkit.

Self-Learning Topics: Commercial AR

Frameworks, AR Related Markup Languages

V Mobile AR Types of Mobile Apps, AR Browsers for 10 CO5

Smartphones, Point of Interests (POI) in Mobile
AR, POI Authoring and Publishing Tools, AR
Applications for Android, AR Games for
Android,
Mobile AR Toolkits and SDKs, Developing
Mobile AR Applications, AR Application
Development for Android Smartphone

Self-Learning Topics: AR Applications for

iOS, AR Games for iOS, AR Application
Development for iOS Smartphone

VI Applications of Applications of AR/MR in: Edutainment, 07 CO6

AR/MR and Human Medical, Military, Production and
Factors, Legal and Manufacturing, Navigation, Astronomical
Social Observation, E-commerce; What are Human
Considerations Factors, Physical Side Effects, Visual Side
Effects, Legal Considerations, Moral and
Ethical Considerations.

Self-Learning Topics: Applications of AR/MR

in Civil Construction and Architecture,
Collaboration, Information Control and Big
Data Visualization

Textbooks:
1. Dieter Schmalsteig and Tobias Hollerer, ―Augmented Reality- Principles and Practice‖, Pearson
Education, Inc. 2016 Edition.
2. Chetankumar G Shetty, ―Augmented Reality- Theory, Design and Development‖, Mc Graw Hill,
2020 Edition.
3. Alan B. Craig, ―Understanding Augmented Reality – Concepts and Applications‖, Morgan
Kaufmann, Elsevier, 2013 Edition.
References:
1. Borko Furht, ―Handbook of Augmented Reality‖, Springer, 2011 Edition.
2. Erin Pangilinan, Steve Lukas, and Vasanth Mohan, ―Creating Augmented and Virtual Realities-
Theory and Practice for Next-Generation Spatial Computing‖, O‘Reilly Media, Inc., 2019 Edition.
3. Jens Grubert, Dr. Raphael Grasset, ―Augmented Reality for Android Application Development‖,
PACKT Publishing, 2013 Edition.
Online Resources:
Sr. No. Website Name
1. www.nptel.ac.in
2. www.coursera.org

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks)
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks.

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HVARC701 ARVR 04 -- -- 04 -- -- 04
Application-I

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HVARC7 ARVR
01 Application- 30 30 30 10 -- -- -- 100
60
I

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To learn the underlying concepts of Virtual Reality, Augmented Reality and related
technologies.
2 To analyse the principles of VR design, prototype.
3 To analyse the principles of AR design, prototype.
4 To design Graphical User interface using VR
5 To identify trends in XR, key issues in XR and XR Tools.
6 To analyse privacy, ethical, social concern on AR/VR problem.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Apply modelling techniques on Augmented Reality applications.. L1, L2, L3
2 Gets an overview of guidelines, methods, tools and pick design problems in L1, L2
Virtual Reality.
3 Gets an overview of guidelines, methods, tools and pick design problems in L1, L2
Augmented Reality.
4 Evaluate designs based on theoretical frameworks and build Graphical User L3, L4
interface using VR, Tools
5 Apply the appropriate XR development Approach on problem L3
6 Analyse main concerns with respect to designed solutions and discuss the L3, L4
privacy, ethical, social concerns.
Prerequisite: Programming Language, Computer Graphics, Virtual Reality
DETAILED SYLLABUS:
Module Title Description Hours CO
0 Prerequisite Fundamental Concept and Components of 02 --
Virtual Reality,Augmented Reality and Mixed
Reality Technologie,Authoring in AR
I AR/VR Concepts Difference between AR and VR , Rendering 08 CO1
and Technologies for VR/AR, Challenges with AR,AR systems
and functionality

Augmented Reality Application

Development :Types of Augmented Reality
Application (Location Based AR Apps
Marker-Based AR Applications), three-
dimensional modeling and computer vision
,displays & tracking technologies

Self-learning Topic: Case study on Retail

shopping using AR

II VR Design Principles of VR design, Overview of 09 CO2

Overview guidelines, methods, tools & design problem,
Physical Prototyping for VR- Physical
prototype of potential solution, Digital
Prototyping for VR- tool choices, digital
prototype of (key aspects of) solution
Self-learning Topic: Study of 3D navigation ,
layout and contents
III AR Design Principles of AR design, Overview of 09 CO3
Overview guidelines, methods, tools & design problem,
Physical Prototyping for AR - Physical
prototype of potential solution, Digital
Prototyping for AR- tool choices, digital
prototype of (key aspects of) solution.
Self-learning Topic: Use of Anchors in AR
IV 3 D interaction 3 D interaction Overview and types, 10 CO4
with VR Navigation in VR, Object interaction,
Graphical User interface using VR, Challenges
in VR interaction, Tools
Self-learning Topic: Case study of Mobile
applications using 3D interface
V XR Application XR overview, XR development Approach, XR 10 CO5
Development design process, Trends in XR, key issues in
XR, Tools
Self-learning Topic: Difference between, AR,
VR, MR and XR
VI Privacy and Privacy, Ethical, and Social Implications, and 04 CO6
security the Future of AR/VR
Self-learning Topic: Case study on Privacy
and security issues using AR and VR

Textbooks:
1. John Vince, ― Virtual Reality Systems‖, Pearson publication
2. Tony Parisi, ― Learning Virtual Reality‖, O‘REILLY‘
3. Dieter Schmalsteig and Tobias Hollerer, ―Augmented Reality- Principles and Practice‖, Pearson
Education, Inc. 2016 Edition.
4. Chetankumar G Shetty, ―Augmented Reality- Theory, Design and Development‖, Mc Graw Hill,
2020 Edition.
5. Alan B. Craig, ―Understanding Augmented Reality – Concepts and Applications‖, Morgan
Kaufmann, Elsevier, 2013 Edition.
References:
1. Borko Furht, ―Handbook of Augmented Reality‖, Springer.
2. Erin Pangilinan, Steve Lukas, and Vasanth Mohan, ―Creating Augmented and Virtual Realities-
Theory and Practice for Next-Generation Spatial Computing‖, O‘Reilly Media, Inc., 2019 Edition.
3. Jens Grubert, Dr. Raphael Grasset, ―Augmented Reality for Android Application Development‖,
PACKT Publishing.

Online Resources:
Sr. No. Website Name
3. www.nptel.ac.in
4. www.coursera.org

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks)
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks.

Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
Course
Course Code /
Title
Oral
HVARSBL601 ARVR Lab -- 2 -- -- 1 -- 01
(SBL)

Examination Scheme

Theory Marks
Course
Course Title Internal assessment Practical/
Code End Term
Total
Te Sem. Work Oral
Avg. of 2
st Test2 Exam
Tests
1
HVARSB ARVR Lab
L601 (SBL) -- -- -- -- 50 50 50

Lab Objectives:
Sr. No. Lab Objectives
The lab course aims:
1 To Understand the definition and significance of the VR,AR and MR.
2 To Design various applications in VR .
3 To Examine various audio tools for audio embedded in scene
4 To Explore AR and MR applications in real world
5 To develop interface for VR and AR applications
6 To Explore the interconnection and integration of the physical world and able to design &
develop Mobile applications.

Lab Outcomes
Sr. Lab Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Adapt different tools to implement VR,AR and MR. L1,L2
2 Demonstrate the working of VR background design. L1,L2
3 Apply audio tools and developed real world application. L1,L2,L3
4 Adapt different techniques for Integrating AR and MR concepts in L5
applications.
5 Create interface for selected application L6
6 Create application and interface for mobile application /desktop version L6
Hardware & Software Requirements:
Hardware Requirements Software Requirements Other Requirements
PC With Following 1. Unity 1. Internet Connection.
Configuration
2. Python
1. PC i3/i5/i7 Processor or
3.OpenCV
above.
4. Solidity
2. 4 GB RAM
3. 500 GB Harddisk
4. Network interface card

Prerequisite: VR,AR and MR concepts

Suggested List of Experiments
ARVR lab will describe the Designing of VR and AR applications using different Tools. It starts with
installation of software and then learner learn how to design background of various application. Now a day‘s
audio implementation in VR scenes is also getting lots of attention so this aspect is also covered in the lab
experiments. AR and MR are important concepts where learner design the applications for desktop as well
as mobile environment.

Sr. No. Detailed Content LO Mapping

1 To install Open source software /Unity with its functionality LO1

2 Select real world application and design background for the same LO2
3 To add sound in the selected application using Open source LO3
software /Unity software
4 LO3
To study interface requirements and apply for the selected
application

5 Creating Your Digital Prototype of your objects/environment – LO6

(WebVR/ Sketchup / Blender/Unity/Keynote/Figma)
6 To implement a depth map with Python and OpenCV and using LO5
Unity
7 LO3
Identify multiple surfaces and move objects between them using
ARCore

8 To study Interact with AR objects and detect collisions. LO2

9 Marker less Object Placement - WebAR LO4
10 In a group of three to five students develop one real world LO6
application in VR/ AR or MR with object details and sound with
good user interface
Text Books/ References:
1. 1. Hearn and Baker, ―Computer Graphics- C version‖, 2nd edition, Pearson, 2002.
2. 2. R. K Maurya, ―Computer Graphics with Virtual Reality‖, 3rd Edition, Wiley India, 2018.
3. Dieter Schmalsteig and Tobias Hollerer, ―Augmented Reality- Principles and Practice‖, Pearson
Education, Inc. 2016 Edition.
4. Chetankumar G Shetty, ―Augmented Reality- Theory, Design and Development‖, Mc Graw Hill,
2020 Edition.
5. Alan B. Craig, ―Understanding Augmented Reality – Concepts and Applications‖, Morgan
Kaufmann, Elsevier, 2013 Edition.

Online Resources:
Sr. No. Website Name
1. https://nptel.ac.in/courses/121/106/121106013/#

2. http://msl.cs.uiuc.edu/vr/

3. http://lavalle.pl/vr

4. http://nptel.ac.in
5. www.coursera.org
Term Work:
The Term work shall consist of at least 10 to 12 practical based on the above syllabus. The term work
Journal must include at least 2 assignments. The assignments should be based on real world applications
which cover concepts from all above syllabus.

Term Work Marks: 50 Marks (Total marks) = 40 Marks (Experiment) + 5 Marks

(Assignments/tutorial/write up) + 5 Marks (Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.
Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HVARC801 Game 04 -- -- 04 -- -- 04
Development
with VR

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HVARC8 Game
01 Developmen 30 30 30 10 -- -- -- 100
60
t with VR

Course Objectives
Sr. No. Course Objectives
The course aims:
1 The different genres of game and explain the Unity UI Basics.
2 The use of navigation and cursor control to create a game environment.
3 How to import assets, interact with them using action objects and manage object states.
4 To build transitions by scripting events ,using physics, particle systems, and other Unity
functionality action sequences with UnityGUI design.
5 To build the game project together by handling mecanim ,using dialogue trees,creating and
setting up the game environment and menus for the game.
6 The VR development in Unity.

Course Outcomes

Sr. Course Outcomes Cognitive

No. levels of
attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Identify the different genres of game and explain the Unity UI L1,L2
Basics
2 Make use of navigation and cursor control to create a game L3
environment
3 Apply how to import assets ,interact with them using action L3
objects and manage object states
4 Build transitions by scripting events ,using physics, particle L3
systems, and other Unity functionality action sequences with
UnityGUI design
5 Build the game project together by handling mecanim ,using L3
dialogue trees,creating and setting up the game environment and
menus for the game
6 Explain VR development in Unity L2

Prerequisite: Basics of VR
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite VR Basic concepts 02 -

I Game The Adventure Genre, Fast Forward to Real-Time, 08 CO1

Development and What Draws People to This Genre? Designing
Unity UI Basics Your Game: Defining a Style, Compartmentalizing
Environments, First-Person or Third? Animation,
Basic Human Characteristics Make for Fun?
Managing Your Project, Tips for Completing the
Game,Real Time vs. Pre-render.AI in Gaming-AI
Guidelines, a simple workflow.

Unity UI:The Layout,Toolbar,Menus,Creating

Simple Objects,Selecting and
Focusing,Transforming Objects In 3D,Snaps,Scene
Gizmo.Lights,3D Objects,Materials
Scripting:What is a script?Components of a
Script,Picking an Object in the Game,Conditionals
and State,Order of Evaluation

Self-learning Topics:Understanding the role of AI

in gaming

II Navigation and Creating Environments,Navigation-Arrow 06 CO2

Cursor Control Navigation and Input,Fun with Platforms,Collision
Walls,Cursor visibility,Custom cursors, GUI
Texture Cursor,Hardware Cursor,UnityGUI
Cursor,Object-to-Object
Communication,Mouseover Cursor Changes,Object
Reaction to Mouseover
Self-learning Topics:Multimodal Gaming for
Navigation Skills in Players Who Are Blind
III Imported Assets, Imported Assets:3D Art Assets,Setting Up 09 CO3
Objects & Materials,Shadows.
Managing states Action Objects:Colliders,Triggering
Animation,Adding Sound F/X,
Managing States:Identifying the Action
Objects,Developing a State Machine,Lookup
Table,Scripting in Unity,Picking a script
Editor,Fundamentals of scripting in Unity.The
Object Lookup Script,Action-Related Messages
Self-learning Topics:Study the new Asset Import
Pipeline: Solid foundation for speeding up asset
imports,Effects of scripting on dialogues.

IV Transitions, Text Processing the Auxiliary Objects,Handling Object 09 CO4

Management Visibility,Ensuring Player Focus,
Adding New Assets,Physics,Combining Physics
and Keyframe Animation,Particle systems,

GUI Skin,Text Visibility,Using Layers,Creating the

Inventory Screen,Adding Inventory
Icons,Managing the inventory.
Self-learning Topics:Importance of effective Text
management in Gaming

V Game Dialogue Trees,The Scenario,Starting a 09 CO5

Deployment Conversation,Mecanim and Characters,Game
Environment,Setting up the game,Menus and levels
Self-learning Topics: Branching dialogue trees
and its effect in Gaming.Study of different UI
designs for Menus in Games.

VI XR development Unity platform and services, XR Getting started 09 CO6

in Unity with AR development in Unity, Getting started
with VR development in Unity, XR Plug-in
Framework, Configuring your Unity Project for
XR, Universal Render Pipeline compatibility in
XR, XR API reference, Single Pass Stereo
rendering (Double-Wide rendering), VR Audio
Spatializers, VR frame timing ,Unity XR SDK,
Open-source repositories using Bitbucket, Asset
Store Publishing, use of unity as library in other
application.
Self-learning Topics: Study any open source tool
for VR Development
Text Books:
1. Beginning 3D Game Development with Unity 4 All-in-one Multi-platform Game development, 2nd
Edition, Apress,Sue Backman

2. Game Development with Unity 2nd Edition,Michelle Menard and Bryan Wagstaff

3. Unity Game development Essentials,Will Goldstone,PACKT Publishing

4. Unity Game Development Cookbook-Essentials for every Game,O‘reilly,Paris Buttfield-

Addison,Jon Manning-Tim Nugent.

Reference Books:
1. Introduction to Gam Development,Second Edition,Steve Rabin,CENGAGE Learning
2. Sams Teach Yourself Unity Game Development in 24 Hours-Mike Geig

Online References:

Sr. No. Website Name

1. https://docs.unity3d.com/Manual/VROverview.html
2. https://www.coursera.org/
3. https://www.udemy.com/

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA) (10 marks)
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks.

SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Honours Degree Program

(Prepared based on the Guidelines for AICTE and University of Mumbai)

Internet of Things

(with effect from AY 2022-2023)

Internet of Things for Honours Degree Program

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai
Accredited by NAAC and NBA, Approved by AICTE, New Delhi
Bachelor of Technology in IT/CE/ET/AI&DS Engineering
(With effect from 2022-23)

Honours* in Internet of Things

Teaching Examination Scheme and Credit

Course Code Scheme Hours / Week Marks Scheme
Year and Course Intern End
Ter Ora
& Title Practi Avera al Se
Theo Tes m l/
Sem cal Test-2 ge Asses m Total Credits
ry t-1 Wor Pra
s Exa
k ct
ment m
HIoTC501:
TE IoT Sensor 04 -- 30 30 30 10 60 -- -- 100 04
Sem Technologies
V Total 04 - -- 100 - - 100 04

Total Credits = 04

TE HIoTC601:
Sem. IoT System 04 -- 30 30 30 10 60 -- -- 100 04
VI Design
Total 04 - - 100 - - 100 04
Total Credits = 04

HIoTC701:
BE Dynamic 30 30
04 -- 30 10 60 -- -- 100 04
Sem. Paradigm in
VII IoT
HIoTSBL60
1:
Interfacing
& -- 04 -- -- 50 50 100 02
Programmin
g with
IoTLab
Total 04 04 100 50 50 200 06
Total Credits = 06

BE HIoTC801:
Sem. Industrial 04 - 30 30 30 10 60 -- -- 100 04
VIII IoT
Total 04 - - 100 - - 100 04
Total Credits = 04

Total Credits for Semesters V,VI, VII &VIII = 04+04+06+04=18

Course Code Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
HIoTC501 IoT Sensor 04 -- -- 04 -- -- 04
Technologies

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HIoTC50 IoT Sensor
1 Technologie 30 30 30 10 -- -- -- 100
s 60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To provide in depth knowledge about the sensing mechanism.
2 To make students understand about the use of sensors in design of IoT based systems.
3 To familiarize students various types of sensors used to measure the physical quantities.
4 To develop reasonable level of competence in the design, construction and development of
sensor suitable to the system requirements.
5 To Introduce students the current state of the art in sensor technology.
6 To familiarize students with electronics used to interface with sensors.
Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the sensing mechanism and structural details of sensors. L1, L2

2 Explain principles and working of the sensors. L1,L2

3 Evaluate the performance of various types of sensors. L5
4 Select the sensor suitable to system requirements. L5

5 Interface the sensors with microcontrollers and Arduino L6

6 Understand the current state of the art in sensor technology. L2

DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO Mapping
No
.
0 Prerequisite1. Basics of Electrical and Electronics Engineering 2 CO 1, CO2, CO3,
2. Applied Mechanics CO4, CO5
3. Applied Physics, Applied Chemistry
I Sensor Sensor Fundamentals and Properties: Introduction 8 CO1, CO2
Fundamental to IoT, Need for sensors in IoT, Data Acquisition –
s and sensor characteristics – electric charges, fields,
Properties potentials – capacitance – magnetism – inductance –
resistance – piezoelectric – pyroelectric – Hall effect
thermoelectric effects – sound waves – heat transfer
– light – dynamic models of sensors. Need of
actuators, all types of actuators and their working.
Identification of sensor and actuator for real-time
application
Self-learning Topics: IoT Systems, Transfer
function and modelling of sensors
II Optical, 8 CO1, CO2, CO3,
radiation and Optical, radiation and Displacement sensors CO4
Displacement Photosensors: Photodiode, phototransistor and photo
sensors resistor, imaging sensors, UV detectors, Basic
Characteristics of radiation sensors, Thermal infrared
sensors, X-ray and Nuclear Radiation Sensors, Fibre
Optic Sensors, Capacitive and Inductive
Displacement Sensor, Electromagnetism and
Inductance, Magnetic Field Sensors

Self-learning Topics: Optical sources and

detectors, Sensors based on polymer optical
fibers, Micro-structured and solid fibers
III Presence, Presence, force, Pressure, Flow Sensors 9 CO1, CO2, CO3,
force, CO4
Potentiometric Sensors, Piezoresistive Sensors,
Pressure,
Capacitive Sensors for presence, Inductive and
Flow Sensors
Magnetic Sensors, Strain gages, Pressure sensitive
films, piezoelectric force sensor, Piezoelectric
Cables, Concept of Pressure, Mercury Pressure
Sensor, Bellows, Membranes, and Thin Plates, Piezo
resistive Sensors, Capacitive Sensors, VRP Sensors,
Optoelectronic Pressure Sensors, Indirect Pressure
Sensor, Vacuum Sensors, Basics of Flow Dynamics,
Pressure Gradient Technique, Thermal Transport
Sensors, Ultrasonic Sensors, Level Sensors
Self-learning Topics: Vibration energy harvesting
with Piezoelectric, MEMS systems. Develop a
sensor system for force measurement using
piezoelectric transducer. Develop Resistance
Temperature Detector
IV Humidity, Humidity, Moisture Chemical and Biological 8 CO1, CO2, CO3,
Moisture Sensors CO4, CO5
Chemical
Microphones: Characteristics, Resistive, condenser,
and
Electret, Optical, Pizoelectric, Dynamic,
Biological
Sensors Concept of humidity, Capacitive Humidity Sensors,
Resistive Humidity Sensors, Thermal Conductivity
Sensors, Optical Hygrometers, Oscillating
Hygrometer, Soil Moisture
Chemical Sensor Characteristics, Electrical and
Electrochemical Sensors, Photoionization Detectors,
Physical Transducers, Spectrometers, Thermal
Sensors, Optical Transducers, Multi-sensor Arrays
Artificial Microsystems for Sensing Airflow,
Temperature, and Humidity by Combining MEMS
and CMOS Technologies
Self-learning Topics: Biosensors for biomedical
applications
V Interface Interface Electronic Circuits 8 CO1, CO2, CO5
Electronic
Introduction, Signal Conditioners, Sensor
Circuits
Connections, Excitation Circuits, Analog to Digital
Converters, Integrated Interfaces, Data Transmission,
Noise in Sensors and Circuits, Batteries for Low-
Power Sensors, Types of Single board computers,
various sensor interfacing with Arduino, Embedded
C Programming. data communication protocol
interfacing, study the properties of LDR, Build a
simple LED light intensity controller, Linux on
Raspberry Pi, Interfaces, and Programming.
Self-learning Topics: Python Programming to
interface sensors
VI Current Current Trends in sensors and Technology 9 CO1, CO2, CO3,
Trends in CO4, CO5, CO6
Smart Sensors: Introduction, Primary sensors,
sensors and
Excitation, Amplification, Filters, Converters,
Technology
Compensation, Information Coding/Processing, Data
Communication, Standards for Smart Sensor
Interface, The Automation
Sensor Technologies: Introduction, Film Sensors,
Thick Film Sensors, Thin Film Sensors,
Semiconductor IC Technology—Standard Methods,
Microelectromechanical Systems (MEMS), Nano-
sensors
Sensor Applications: Onboard Automobile sensors,
Home appliances sensors, Aerospace Sensors,
Sensors for Environmental Monitoring
Self-learning Topics: Energy Harvesting, Self-
powered Wireless Sensing in ground, Ground
penetrating sensors

Text Books:
1. Jacob Fraden, ―Hand Book of Modern Sensors: physics, Designs and Applications‖, 2015, 3rd
edition, Springer, New York.
2. Jon. S. Wilson, ―Sensor Technology Hand Book‖, 2011, 1st edition, Elsevier, Netherland
3. D. Patranabis – Sensor and Transducers (2e) Prentice Hall, New Delhi, 2003
4. Vijay Madisetti and Arshdeep Bahga, ―Internet of Things (A Hands-on-Approach)‖,1st Edition,
VPT, 2014

References:
1. Edited by Qusay F Hasan, Atta ur rehman Khan, Sajid A madani, ―Internet of Things Challenges,
Advances, and Application‖, CRC Press
2. Triethy HL - Transducers in Electronic and Mechanical Designs, Mercel Dekker, 2003
3. Gerd Keiser,‖Optical Fiber Communications‖, 2017, 5th edition, McGraw-Hill Science, Delhi.
4. John G Webster, Halit Eren, ―Measurement, Instrumentation and sensor Handbook‖, 2014, 2nd
edition, CRC Press, Taylor and Fransis Group, New York.
5. Adrian McEwen, ―Designing the Internet of Things‖, Wiley Publishers, 2013, ISBN: 978-1-118-
43062-0
6. Nathan Ida, ―Sensors, Actuators and their Interfaces: A Multidisciplinary Introduction‖, Second
Edition, IET Control, Robotics and Sensors Series 127, 2020

Online References:
Sr. No. Website Name
7. https://nptel.ac.in/courses/108/108/108108123/
8. https://nptel.ac.in/courses/108/108/108108098/
3. https://nptel.ac.in/noc/courses/noc19/SEM2/noc19-ee41/
4. https://nptel.ac.in/courses/108/106/108106165/

Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Code Course Theory Practical Tutorial Theory Practical Tutorial Total
Title /Oral
HIoTC601 IoT System 04 -- -- 04 -- -- 04
Design

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HIoTC60 IoT System
1 Design 30 30 30 10 -- -- -- 100
60

Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To learn basic principles, concepts, and technologies for internet of things.
2 To understand various architectures of IOT.
3 To train the students to build IoT systems using sensors, single board computers and open
source IoT platform for given application.
4 To learn and implement various networking and communication protocols.
5 To design and analyze IoT for given applications.
6 To Evaluate performance of given IoT system.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Able to explain principles, concepts, and technologies for internet of things. L1, L2
2 Able to identify various building blocks of IoT system L1,L2

3 Able to analyze and evaluate various networking and communication L3,L4

protocols used in IoT system
4 Able to select appropriate interface for given application L3
5 Able to design and analyze IoT system for given application L4,L5
6 Able to evaluate performance of given IOT System L5
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO
No. Mapping

0 Prerequisite Comment (Prerequisite syllabus should not be 2 --

considered for paper setting) Basics of
Embedded System,IoT Sensors, Digital design

I Overview of IoT What is IoT System? IoT Impact, Current Trends 6 CO1, CO2
System in IoT , IoT Challenges, Comparing IoT
Architectures, A Simplified IoT Architecture,
The Core IoT Functional Stack How are IoT
Systems different from traditional system Values
and Uses of IoT Functional View and
Infrastructure view of IoT Systems
Self-learning Topics: Understanding the Issues
and Challenges of a More Connected World
II Networking OSI Model for the IoT/M2M System 8 CO3
Protocols Lightweight M2M Communication Protocols,
Internet based Communications, IP addressing in
IoT, Network Model, TCP & UDP, Client-
Server architecture
Self-learning Topics: How to choose correct
protocol for our network.
III Communication IoT Edge to Cloud protocols: HTTP, REST 10 CO3,CO4
Protocols APIs, WebSocket, MQTT, COAP, Comparison
of Protocols.M2M Communication Protocols ,
Bluetooth BR/EDR and Bluetooth low energy
.RFID IoT System , RFID IoT Network
Architecture, ZigBee IP/ZigBee SE2.0,
Wifi(WLAN), Message Communication
protocols for connected devices Data exchange
formats: JSON & XML, Node-Red, Flow control
using Node-Red, learning the different nodes of
Node-RED for implementing the
Communication Protocols
Self-learning Topics: Types of Communication
IV Sensor Interfaces Digital Interfaces : UART, Serial Peripheral 10 CO4
Interface (SPI), I2C (Inter-Integrated Circuit),
Controller Area Network (CAN), Middleware
Technologies, Communication Protocols and
Models. Practical Components Programming
with interface in Arduino, MBed and Raspberry
Pi
Self-learning Topics: SMART SENSOR
INTERFACES
V Design principles Design solution for ubiquitionos and utility, 8 CO5
for prototyping Interface design for user experience, Desiging
for data privacy, Interfacing – Apps & Webs,
Designing for Affordability, Cost v/s Ease of
Prototyping, Prototypes and Production,
Selection of embedded platform, Prototype and
Mass personalization, Open Source v/s Closed
Source ,Amplification and Signal Conditioning-
Integrated Signal Conditioning- Digital
conversion- MCU Control MCUs for Sensor
Interface- Techniques and System
Considerations- Sensor Integration
Self-learning Topics: Principles for Prototyping
and moving towards Product Development

VI IoT, case studies Arduino Programming for Ethernet and Wifi 8 CO6
connectivity , Networking and Datalogging with
Raspberry Pi Applications-Agriculture,
Medical,Fire detection, Air pollution prediction,
Earthquake early detection; for smart
environmental care, smart traveling, Home
Automation
Self-learning Topics: IoT enabled Business
solution in Supply Chain
Text Books:
1. S. Misra, A. Mukherjee, and A. Roy, 2020. Introduction to IoT. Cambridge University Press.
2. Adrian McEwen and Hakim Cassimally, ―Designing the Internet of Things‖, John Wiley and Sons Ltd,
UK, 2014.
3. Milan Milenkovic, Internet of Things: Concepts and System Design, Springer International
Publishing,May 2020cation
4. Dr.Raj Kamal,Internet of Things(IoT) , Architecture and Design Principles.McGraw Hill Education.

References:
1. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry,"IoT
Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things
2. N. Ida, Sensors, Actuators and Their Interfaces, Scitech Publishers, 2014.
3. Editors OvidiuVermesan Peter Friess,'Internet of Things – From Research and Innovation to Market
4. Dr. Guillaume Girardin , Antoine Bonnabel, Dr. Eric Mounier, 'Technologies Sensors for the Internet
of Things Businesses & Market Trends 2014 -2024',Yole Development Copyrights ,2014
Assessment:
Continuous Assessment (CA):
The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Course Course Title Theory Practical Tutorial Theory Practical/ Tutorial Total
Code Oral
HIoTC701 Dynamic 03 -- -- 03 -- -- 03
Paradigm in IoT

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HIoTC70 Dynamic
1 Paradigm in 30 30 30 10 -- -- -- 100
60
IoT
Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To explore the role of the cloud in Internet of Things deployment.
2 To introduce the usage of different machine learning algorithms on IoT Data.
3 To explore data analytics and data visualization on IoT Data.
4 To explore the role of Fog computing in Internet of Things.
5 To explore design issues and working principles of various security measures and various
standards for secure communication in IoT.
6 To develop the ability to integrate IoT with Dev-ops.

Course Outcomes:
Sr. Course Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Identify the need for the cloud in IoT deployment and describe different L1,L2
Cloud provider‘s architecture.
2 Use and correlate machine learning techniques on IoT Data. L3,L4

3 Apply IoT analytics and data visualization. L3

4 Recognize the use of Fog Computing in the Internet of things. L1,L2
5 Explain the need of security measures in the Internet of Things. L4
6 Apply the knowledge of Dev-ops in IoT applications. L3
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO Mapping
No
.
0 Prerequisite Basics of Cloud Computing, Basics of Machine 2 --
learning and primitives of cryptography

I IoT and Cloud Computing Concept, Grid/SOA and 10 CO1

CLOUD Cloud Computing, Cloud Middleware
NIST’s SPI Architecture and Cloud Standards,
The Cloud of Things--The Internet of Things and
Cloud Computing
The Cloud of Things Architecture-- Four
Deployment Models, Vertical
Applications, Fifteen Essential Features, Four
Technological Pillars, Three Layers of IoT
Systems, Foundational Technological Enabler
Cloud Providers and Systems -- Microsoft Azure
IoT, Amazon Web Services, Google‘s cloud IoTs.

Self-learning Module: IBM Watson Cloud

II IoT and Advantages of IoT and Machine Learning 6 CO2
Machine Integration, Implementation of Supervised
Learning Algorithm- Regression (Linear and Logistic),
SVM for IoT-Neural Network on case study:
Agriculture and IoT, Smart Home etc.

Self-Learning Module: Regression, SVM

III IoT and Defining IoT Analytics, IoT Analytics 8 CO3
Data challenges, IoT analytics for the cloud-Microsoft
Analytics Azure overview– Strategies to organize Data for
IoT Analytics, Linked Analytics Data Sets,
Managing Data lakes, The data retention strategy.
Communicating with Others- Visualization and
Dash boarding- Designing visual analysis for IoT
data, creating a dashboard –creating and visualizing
alerts.
Self-learning Topics: Study real time case study
on IoT Analytics.
IV IoT and Fog Fog computing Basics, The Hadoop philosophy 8 CO4
Computing for Fog computing, Fog Computing versus Edge
Computing versus cloud computing, Open Fog
Reference Architecture Application services--
Application support, Node management and
software backplane, Hardware virtualization, Open
Fog node security, Network Accelerators Compute,
Storage Hardware platform infrastructure, Protocol
abstraction, Sensors, actuators, and control systems,
Fog Topology.
Self-learning Module: Amazon Green grass and
Lambda (implementation)
V IoT and it’s Cyber security vernacular Attack and threat terms, 8 CO5
Security Defense terms, Anatomy of IoT cyber attacks –
Mirai, Stuxnet, Chain Reaction, Physical and
hardware security, Root of Trust, Key management
and trusted platform modules, Processor and
memory space, Storage security, Network stack –
Transport Layer Security, Software defined
perimeter, Software-Defined Perimeter
architecture,

Self-learning Module: OWASP-Existing Security

attacks and its prevention methods.
VI IoT and Introduction to DevOps, DevOps application - 10 CO6
Devops business scenarios, DevOps process -- Source
Code Management (SCM), Code review,
Configuration Management, Build management,
Artifacts repository management, Release
management, Test automation, Continuous
integration, Continuous delivery, Continuous
deployment, Infrastructure as Code, Routine
automation, Key application performance
monitoring/indicators. DevOps frameworks--
DevOps maturity life cycle, DevOps maturity map,
DevOps progression framework/readiness model,
DevOps maturity checklists, Agile framework for
DevOps process projects, Agile ways of
development
Tool for IoT—Chef and Puppet, Setting up Chef
and Puppet, Multi-tier Application Deployment,
NETCONF-YANG Case Studies- Steps for IoT
device management with NETCONF-YANG,
Managing Smart irrigation IoT system with
NETCONF-YANG, Managing Home Intrusion
Detection IoT system with NETCONF-YANG
Self-learning Topics: Compare different tool of
IoT.
Text Books:
1. The Internet of Things in the Cloud A Middleware Perspective, Honbo Zhou – CRC Publication.
2. Analytics for the Internet of Things (IoT), Andrew Minteer, Packt Publication 2017
3. Internet of Things- Hands on Approach, Arshdeep Bagha, Vijay Medisetti, Published by Arshdeep
Bagha and Vijay Medisetti,2014.
4. Hands-on DevOps, Sricharan Vadapalli, Packt Publication, 2017
5. Internet of things For Architects, Perry Lea Packt Publication,2018

References:
1. Enterprise Cloud Computing, Gautam Shroff, Cambridge,2010
2. Mastering Cloud Computing -Foundations and Applications Programming, Raj Kumar Buyya, Christian
Vecchiola, S. Thamarai Selvi, MK Publication, 2013.
3. Machine Learning in Action‖, Peter Harrington, DreamTech Press
4. Introduction to Machine Learning‖, Ethem Alpaydın, MIT Press
5. Learning AWS IoT- Effectively Manage Connected Devices on the AWS Cloud Using Services Such as
AWS Greengrass, AWS Button, Predictive Analytics and Machine Learning, Agus Kurniawan, Packt
Publication,2018
6. Practical Dev-Ops, Joakim Verona, Packt Publication, 2016

Online References:
Sr. No. Website Name
1. https://hub.packtpub.com/25-datasets-deep-learning-iot/
2. https://data.world/datasets/iot
3. https://dashboard.healthit.gov/datadashboard/data.php
4. https://www.data.gov/
5. https://dev.socrata.com/data/
6. https://www.kaggle.com/
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
Teaching Scheme
(Contact Hours) Credits Assigned
Course Code Course Title Theory Practical Tutorial Theory Practical Tutorial Total
& Oral
HIoTSBL601 Interfacing & -- 4 -- -- 2 -- 02
Programming
with IoT Lab

Examination Scheme

Theory Marks
Course Code Course Title Practical/
Internal assessment End Term
Total
Sem. Work Oral
Avg. of 2
Test1 Test 2 Exam
Tests
HIoTSBL601 Interfacing &
Programming
-- -- -- -- 50 50 100
with IoT Lab
(SBL)

Lab Objectives:
Sr. No. Lab Objectives
The Lab aims:
1 To Understand the definition and significance of the Internet of Things.
2 To Discuss the architecture, operation, and business benefits of an IoT solution.
3 To Examine the potential business opportunities that IoT can uncover.
4 To Explore the relationship between IoT, cloud computing, and DevOps.
5 To Identify how IoT differs from traditional data collection systems.
6 To Explore the interconnection and integration of the physical world and able to design &
develop IOT Devices.
Lab Outcomes:
Sr. Lab Outcomes Cognitive levels
No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of lab, learner/student will be able to:
1 Adapt different techniques for data acquisition using various IoT sensors for L6
different applications.
2 Demonstrate the working of actuators based on the collected data. L2

3 Use different IoT simulators and correlate working of IoT protocols. L3

4 Adapt different techniques for Integrating IoT services to other third-party L6
Clouds.
5 Execute DevOps methodologies for continuous integration and continuous L3
deployment of IoT application.
6 Implement IoT protocols like MQTT for communication to realize the L3
revolution of internet in mobile devices, cloud and sensor networks.
Prerequisite:
IoT introduction course: Basics of IoT, Introduction to Embedded systems
Hardware & Software Requirements:

Hardware Requirements Software Requirements Other Requirements

PC With Following 1. Windows or Linux Desktop OS 1. Internet Connection for installing

Configuration additional packages if required
2. DeVops
1. Intel PIV Processor
3.Python
2. 4 GB RAM
4. IoT Simulator/Emulator (open
3. 500 GB Harddisk source)

4. Network interface card

5. Sensors

6. IoT Kit
(Arduino/ARM/Raspberry Pi)

This lab will describe the market around the Internet of Things (IoT), the technology used to build these
kinds of devices, how they communicate, how they store data, and the kinds of distributed systems needed to
support them. Divided into four main modules, we will learn by doing. We will start with simple examples
and integrate the techniques we learn into a class project in which we design and build an actual IoT system.
The client will run in an emulated ARM environment, communicating using common IoT protocols with a
cloud enabled backend system with DevOps integration.
Suggested List of Experiments
Sr. No. Detailed Content Hours LO
Mapping
1 To study and implement interfacing of different IoT 4 LO1
sensors with Raspberry Pi/Arduino/ModeMCU…
2 To study and implement interfacing of actuators based on 4 LO2
the data collected using IoT sensors. (like led switch
ON/OFF, stepper word)
3 To study and demonstrate Contiki OS for RPL (like Create 4 LO3
2 border router and 10 REST clients, Access border router
from other network (Simulator))
4 To study and demonstrate use of IoT simulators (like 4 LO3
Beviswise) on any real time device (LED/stepper motor)
5 Select any one case study (in a group of 2-3) and perform 8 LO4
the experiments 5 to 10. The sample case studies can be as
follows:

1. Smart home automation system

2. Healthcare management system
3. Smart traffic management system & so on…

Write a program on Raspberry Pi to push and retrieve the

data from cloud like thingspeak, thingsboard, AWS, Azure
etc.

6 To install MySQL database on Raspberry Pi and perform 6 LO4

basic SQL queries for analysis data collected.
7 4 LO4
To study and implement IoT Data processing using Pandas.

8 To study and implement Continuous Integration using 6 LO6

Jenkins on IoT data and also perform interfacing of
Raspberry Pi into Jenkins.
9 6 LO6
To study and implement Continuous Deployment
(Infrastructure as a code) for IoT using Ansible.

10 To study MQTT Mosquitto server and write a program on 6 LO5

Arduino/Raspberry Pi to publish sensor data to MQTT
broker.

Books / References:
1. Jake VanderPlas,― Python Data Science Handbook‖, O‘Reilly publication,2016
2. Joakim Verona,‖ Practical DevOps‖, PACKT publishing, 2016
3.Honbo Zhou,‖ The internet of things in the cloud‖, CRC press, Taylor and Francis group, 2012
4. Perry Lea,‖ Internet of things for architects‖, PACKT publishing, 2018

Online Resources:
Sr. No. Website Name
1. https://spoken-tutorial.org/watch/Arduino/Introduction+to+Arduino/English/
2. https://pythonprogramming.net/introduction-raspberry-pi-tutorials/
3. https://iotbytes.wordpress.com/basic-iot-actuators/
4. http://www.contiki-os.org/
5. https://www.bevywise.com/iot-simulator/
6. https://mqtt.org/

Term Work:

The Term work shall consist of at least 10 practical based on the above list. The term work Journal must
include at least 2 assignments. The assignments should be based on real world applications which cover
concepts from all above list.
Term Work Marks: 50 Marks (Total marks) = 40 Marks (Experiment) + 5 Marks (Assignments/tutorial/write
up) + 5 Marks (Attendance)

Practical & Oral Exam: An Oral & Practical exam will be held based on the above syllabus.
Course Course Theory Practical Tutorial Theory Practical/ Tutorial Total
Code Title Oral
HIoTC801 Industrial 04 -- -- 04 -- -- 04
IoT

Examination Scheme
Theory Marks
Course Course
Code Title Internal assessment Inter End Term
nal Sem. Practical Oral Total
Work
Test Avg. of Asses Exam
Test 2
1 2 Tests sment
HIoTC80 Industrial
1 IoT 30 30 30 10 -- -- -- 100
60
Course Objectives:
Sr. No. Course Objectives
The course aims:
1 To learn the concepts of Industry 4.0 and IIOT.
2 To learn reference Architecture of IIOT.
3 To learn Industrial Data Transmission and Industrial Data Acquisition.
4 To learn middleware and WAN technologies.
5 To learn IIOT Block chain and Security.
6 To learn different applications and securities in IIOT.

Course Outcomes:

Sr. Course Outcomes Cognitive levels

No. of attainment as
per Bloom’s
Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the concepts of Industry 4.0 and IIOT. L1,L2
2 Understand reference Architecture of IIOT. L1,L2
3 Understand Industrial Data Transmission and Industrial Data Acquisition. L1,L2
4 Understand middleware and WAN technologies in IIOT. L1,L2
5 Understand the concepts of Blockchain and Security in IIOT. L1,L2
6 Apply security in IIOT applications. L3
DETAILED SYLLABUS:
Sr. Module Detailed Content Ho CO Mapping
No. urs

0 Prerequisite IOT Concepts, Sensor Technology, IOT 02 --

Stack and Protocols, Design IoT systems,
WSN etc.

I Introduction 06 CO1
Overview of Industry 4.0 and Industrial
Internet of Things, Industry 4.0: Industrial
Revolution: Phases of Development,
Evolution of Industry 4.0, Environment
impacts of industrial revolution, Industrial
Internet, Basics of CPS, CPS and IIOT,
Design requirements of Industry 4.0, Drivers
of Industry 4.0, Sustainability Assessment of
Industries, Smart Business Perspective,
Cyber security, Impacts of Industry 4.0,
Industrial Internet of Things: Basics, IIOT
and Industry 4.0, Industrial Internet Systems,
Industrial Sensing, Industrial Processes, IIOT
Challenges – Identifying Things within the
internet, Discovering Things and the Data
they possess, Managing massive amount of
data, Navigating Connectivity Outages, IIOT
Edge - Leveraging the Power of Cloud
Computing, Communicating with Devices on
the Edge, Determining a Request/Response
Model

Self-learning Topics: Study real time IIoT

challenges in industry.

II IIOT Reference The IIC Industrial Internet Reference 08 CO2

Architecture Architecture - Industrial Internet Architecture
Framework (IIAF),Industrial Internet
Viewpoints -Functional, Operational,
Information Application and Business
Domain of IIAF.
The Three-Tier Topology, Key Functional
Characteristics of Connectivity.
Software Architectural Style for the Industrial
Internet of Things - Software Architecture
Practice, Advanced Architectural Styles,
Systems of Systems, Challenges of Software
Engineering in IIoT, Principles for Software
Architecture design in IIoT, The Principled
Decomposition, The Architectural Style
Self-learning Topics: Study IIoT
Architecture.

III Industrial Data Introduction, (Features and Components of - 10 CO3

Transmission and Foundation Fieldbus, Profibus,
Industrial Data HART,Interbus, Bitbus, CC-Link, Modbus,
Acquisition Batibus, DigitalSTROM, Controller Area
Network, DeviceNet, LonWorks, ISA
100.11a, Wireless HART, LoRa and
LoRaWAN) NB-IoT, IEEE 802.11AH,
Distributed Control System, PLC, SCADA

Self-learning Topics: Study SCADA, PLC

in detail.

IV 10 CO4
IIOT Middleware (From Industrial Application Perspective)
and WAN
Technologies Examining Middleware Transport Protocols
(TCP/IP, UDP, RTP, CoAP), Middleware
Software Patterns (Publish Subscribe Pattern,
Delay Tolerant Networks),

Software Design Concepts – Application

Programming Interface – A Technical
Perspective, Why Are APIs Important for
Business? Web Services,

IIOT Middleware Platforms – Middleware

Architecture

IIOT WAN Technologies and Protocols -

IIoT Device Low-Power WAN Optimized
Technologies for M2M,
SigFox,LoRaWAN,nWave, Dash7
Protocol, Ingénue RPMA, Low Power
Wi-Fi, LTE Category-M, Weightless,
Millimeter Radio

Self-learning Topics: Study different IIoT

Middleware and WAN Technologies.

V 08 CO5
IIOT Blockchain Blockchains and cryptocurrencies in IoT,
and Security Bitcoin (blockchain-based), IOTA-
distributed ledger (directed a cyclical graph-
based), Government regulations and
intervention, US Congressional Bill –Internet
of Things (IoT) Cyber security Improvement
Act of 2017, Other governmental bodies, IoT
security best practices, Holistic security.
Self-learning Topics: Case study on IIoT
Block chain and Security.

VI IIOT 08 CO6
Applications and The IoT Security Lifecycle-
Securities
The secure IoT system implementation
lifecycle, Implementation and integration,
IoT security CONOPS document, Network
and security integration, System security
verification and validation (V&V), Security
training, Secure configurations, Operations
and maintenance, Managing identities, roles,
and attributes, Security monitoring,
Penetration testing, Compliance monitoring,
Asset and configuration management,
Incident management, Forensics, Dispose,
Secure device disposal and zeroization, Data
purging, Inventory control, Data archiving
and records management

Securing the Industrial Internet - Security in

Manufacturing, PLCs and DCS, Securing the
OT (Operation Technology), Network,
System Level: Potential Security Issues,
Identity Access Management

Develop New Business Models –

Adopt Smart Architectures and Technologies,

Sensor-Driven Computing, Industrial
Analytics, Intelligent Machine Applications,
Transform the Workforce

Case Studies –

Healthcare Applications in Industries –

Challenges associated with Healthcare,
Introduction, Smart Devices, Advanced
technologies used in Healthcare.

Inventory Management and Quality Control –

Introduction, Inventory Management and
IIOT, Quality Control

Manufacturing Industry, Automotive Industry

and Mining Industry

Self-learning Topics: Study real time IIoT

application.
Text Books:

1. ―Industry 4.0: The Industrial Internet of Things‖, by Alasdair Gilchrist (Apress)

2. ―Introduction to Industrial Internet of Things and Industry 4.0‖,by Sudip Misra, Chandana
Roy And Anandarup Mukherjee, CRC Press (Taylor & Francis Group)
3. ―Internet of Things Principles and Paradigms‖, by Rajkumar Buyya, Amir Vahid Dastjerdi,
ELSEVIER Inc.
4 . Internet of things For Architects, Perry Lea Packt Publication,2018

References:

1. ―Practical Internet of Things Security‖, by Brian Russell, Drew Van Duren (Packt Publishing)
2. ―Industrial Internet of Things and Communications at the Edge‖, by Tony Paine, CEO, Kepware
Technologies
3. ―Architectural Design Principles For Industrial Internet of Things‖, Hasan Derhamy, Luleå University of
Technology, Graphic Production

Online References:

Sr. No. Website Name

1. https://onlinecourses.nptel.ac.in/noc20_cs69/preview
2. https://www.coursera.org/specializations/developing-industrial-iot
3. https://www.coursera.org/lecture/advanced-manufacturing-enterprise/the-industrial-
internet-of-things-iiot-59EvI
4. https://www.coursera.org/lecture/industrial-iot-markets-security/segment-12-
blockchains-l4aG9
Assessment:

Continuous Assessment (CA):

The distribution of Continuous Assessment marks will be as follows –
1. Class Test 1 30 marks
2. Class Test 2 30 marks
3. Internal Assessment 10 marks

Continuous Assessment (30-Marks): Test-1 and Test-2 consists of two class tests of 30 marks each.
Test-1 is to be conducted on approximately 40% of the syllabus completed and Test-2 will be based
on remaining contents (approximately 40% syllabus but excluding contents covered in Test-1).
Duration of each test shall be one hour.
Internal Assessment(IA):
Marks will be allotted as per designed rubrics.

End Semester Theory Examination will be of 60-Marks with Three hour duration.
SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Autonomy Scheme-II

Internship Manual
(Prepared based on the Guidelines of AICTE
and University of Mumbai)

(with effect from AY 2022-2023)

SOMAIYA
VIDYAVIHAR

K J Somaiya Institute of Engineering and Information Technology

An Autonomous Institute affiliated to University of Mumbai

Academic Year 2022-23

INTERNSHIP MANUAL

AICTE-INTERNSHIP POLICY STATES THAT:

● The rise in global competition has prompted organizations to devise strategies to have a talented and
innovative workforce to gain a competitive edge.
● Developing an internship policy is an impactful strategy for creating a future talent pool for the industry.
● The Internship program not only helps fresh pass-outs in gaining professional know-how but also
benefits, corporate on fresh perspectives on business issues and even discovering future business leaders.
● Competition in the job sector is rising exponentially and securing entry-level jobs is getting very difficult,
as the students passing out from technical institutions lack the experience and skills required by industry.
● The main aim of this initiatives is enhancement of the employability skills of the students passing out
from Technical Institutions.
OBJECTIVES & EXPECTED OUTCOMES:
Following are the intended objectives of internship training:
1. Will expose Technical students to the industrial environment, which cannot be simulated in the classroom
and hence creating competent professionals for the industry.
2. Provide possible opportunities to learn, understand and sharpen the real time technical / managerial skills
required at the job.
3. Exposure to the current technological developments relevant to the subject area of training.
4. Experience gained from the ‘Industrial Internship’ in the classroom will be used in classroom discussions.
5. Create conditions conducive to quest for knowledge and its applicability on the job.
6. Learn to apply the Technical knowledge in real industrial situations.
7. Gain experience in writing Technical reports/projects.
8. Expose students to the engineer’s responsibilities and ethics.
9. Familiarize yourself with various materials, processes, products and their applications along with relevant
aspects of quality control.
10. Promote academic, professional and/or personal development.
11. Expose the students to future employers.
12. Understand the social, economic and administrative considerations that influence the working
environment of industrial organizations.
13. Understand the psychology of the workers and their habits, attitudes and approach to problem solving

BENEFITS OF INTERNSHIP:
Benefits to Students:
1. An opportunity to get hired by the Industry/ organization.
2. Practical experience in an organizational setting.
3. Excellent opportunity to see how the theoretical aspects learned in classes are integrated into the practical
world. On-floor experience provides much more professional experience which is often worth more than
classroom teaching.
4. Helps them decide if the industry and the profession is the best career option to pursue.
5. Opportunity to learn new skills and supplement knowledge.
6. Opportunity to practice communication and teamwork skills.
7. Opportunity to learn strategies like time management, multi-tasking etc in an industrial setup.
8. Opportunity to meet new people and learn networking skills.
9. Makes a valuable addition to their resume.
10. Enhances their candidacy for higher education.
11. Creating networks and social circles and developing relationships with industry people.
12. Provides opportunity to evaluate the organization before committing to a full time position.

Benefits to the Institute:

● Build industrial relations.
● Makes the placement process easier.
● Improve institutional credibility & branding.
● Helps in retention of the students.
● Curriculum revision can be made based on feedback from Industry/ students.
● Improvement in teaching learning process.

Benefits to the Industry:

● Availability of ready to contribute candidates for employment.
● Year round source of highly motivated pre-professionals.
● Students bring new perspectives to problem solving.
● Visibility of the organization is increased on campus.
● Quality candidate’s availability for temporary or seasonal positions and projects.
● Freedom for industrial staff to pursue more creative projects.
● Availability of flexible, cost-effective work force not requiring a long-term employer commitment.
● Proven, cost-effective way to recruit and evaluate potential employees.
● Enhancement of employer’s image in the community by contributing to the educational enterprise.

STANDARD OPERATING PROCEDURE (SOP) FOR INTERNSHIP:

The general procedure for arranging internship is given below:
Step 1: Request Letter/ Email from the Dean, IIIC/ HOD and/or IIIC members of resp. depts. of the college shall
be send to industry to allot various slots of 4-6 weeks during summer vacation as internship periods for
the students. Students request letter/profile/ interest areas may be submitted to industries for their
willingness for providing the training. (Sample attached)
Step 2: Industry will confirm the training slots and the number of seats allocated for internships via Confirmation
Letter/ Email. In case the students arrange the training themselves the confirmation letter will be
submitted by the students to Dean, IIIC/ HOD and/or IIIC members of resp. depts. Based on the number
of slots agreed to by the Industry, Dean, IIIC/ HOD and/or IIIC members will allocate the students to the
Industry. In addition, the internship slots may be conveyed through Telephonic or Written
Communication (by Fax, Email, etc.) by the Dean or other members of the IIIC who are particularly
looking after the Internship of the students.
Step 3: Students on joining Training at the concerned Industry / Organization, submit the Joining Report/ Letters
/ Email.
Step 4: Students undergo industrial training at the concerned Industry / Organization. In-between Faculty
Member(s) evaluate(s) the performance of students once/twice by visiting the Industry/Organization and
Evaluation Report of the students is submitted to Department IIIC Member with the consent of Industry
persons/ Trainers.
Step 5: Students will submit a training report after completion of internship.
Step 6: Training Certificate to be obtained from industry.
Step 7: List of students who have completed their internship successfully certificate will be issued by Departments,
Sections, Professional bodies, Cells, Committees in collaboration with IIIC cell.
Step 8: In addition to Step 1 to Step 7, Departments, Sections, Professional bodies, Cells, Committees of KJSIEIT
may organize in house / Industry collaborated internship of 1/2/3/4 weeks duration for students with the
same procedure as stated above, with in Principal approval from Principal.

GUIDELINES FOR THE STUDENTS:

Internship/ Placement is a student centric activity. Therefore, the major role is to be played by the
students. Deans, IIIC/HOD may also include involvement of the student in the following activities:
● Design and Printing of Internship / Placement Brochure – Soft copy as well as Hard copy.
● Preparing list of potential recruiters / Internship providers and past recruiters.
● Internship/ Placement Presentation at various organizations, if required.
● For allotment of internship slots all the students will be required to submit “student internship program
application” before the prescribed date

SOP FOR INTERNSHIP REPORT :

STUDENT’S DIARY/ DAILY LOG:
The main purpose of writing a daily diary is to cultivate the habit of documenting and to encourage the
students to search for details. The students should record in the daily training diary the day to day account
of the observations, impressions, information gathered and suggestions given, if any. It should contain
the sketches & drawings related to the observations made by the students. The diary should also be shown
to the Faculty Mentor from time to time. Student’s Diary and Internship Report should be submitted by
the students along with attendance record and an evaluation sheet duly signed, if any. It will be evaluated
on the basis of the following criteria:
● Regularity in maintenance of the diary.
● Adequacy & quality of information recorded.
● Drawings, sketches and data recorded.
● Thought process and recording techniques used.
● Organization of the information.
After completion of Internship, the student should prepare a comprehensive report to indicate what
he/she has observed and learnt in the training period. The training report should be signed by the
Internship Supervisor (from Industry/Organisation, if applicable), Faculty Incharge and HOD. The
Internship report will be evaluated on the basis of following criteria:
• Originality.
• Adequacy and purposeful write-up.
• Organization, format, drawings, sketches, style, language etc.
• Variety and relevance of learning experience.
• Practical applications, relationships with basic theory and concepts taught in the course. The industrial
training of the students will be evaluated in three stages:
● Evaluation by Industry
● Evaluation by faculty supervisor on the basis of site visit(s).
● Evaluation through seminar presentation/viva-voce at the Institute.
EVALUATION BY INDUSTRY:
The industry will evaluate the students based on the Punctuality, eagerness to learn, Maintenance of Daily
Diary and skill test in addition to any remarks.

EVALUATION THROUGH SEMINAR PRESENTATION/VIVA-VOCE AT THE INSTITUTE:

The student will give a seminar based on his/her internship/ training report, as decided by the institute.
The evaluation will be based on the following criteria:
● Quality of content presented.
● Proper planning for presentation.
● Effectiveness of presentation.
● Depth of knowledge and skills.
● Attendance record, daily diary, departmental reports shall also be analyzed along with the Internship
Report. Seminar presentation will enable sharing knowledge & experience amongst students & teachers
and build communication skills and confidence in students.

EXAMINATION AND EVALUATION FOR AWARD OF INTERNSHIP COMPLETION

CERTIFICATE
Internship Completion certificate will be awarded to graduating students on completion of minimum 5
Internship modules from Semester 2 to Semester 8 as per the internship policy document.

COMPLIANCES FOR INTERNSHIP COMPLETION CERTIFICATION :

1. Completion of 1 internship module will reflect addition of 2 credits so total credits earned will be 2
credits x 7 internship modules = 14 credits across Semester 2 to Semester 8.
2. Mandatory to complete minimum 5 internship modules across Semester 2 to Semester 8 for award of
Internship Certificate.
3. On completion of 5 Internship modules credit earned = 10
4. On completion of 6 Internship modules credit earned = 12
5. On completion of 7 Internship modules credit earned =14
6. No credits will be awarded AND / OR No Internship Completion Certificate will be issued for less
than 10 credits earned throughout the degree.
7. Internship evaluation will be as per Internship module assessment process defined in Internship
Manual course contents, for every individual student across Semester 2 to Semester 8.
8. Departments will submit Internship completion report and credits assigned sheet of every student
signed by Department internship coordinator, Class teachers and Head of the Department to Exam
Cell during 8th Semester ESE time duration of respective batch to generate the internship completion
certificate along with the regular grade sheet.
9. No further queries will be entertained if not meeting above compliances and not following the
internship modules designed under the guidelines of AICTE Internship policy.

Internship Scheme & Structure under KJSIEIT Autonomy Scheme-II wef

2022-23
for Bachelor of FY/SY/TY/LY (CE/IT/AI/ET Technology)
Semester- II-VIII

Note:
As per guidelines and suggestions by AICTE-Internship policy
• 1 Credit = 40 - 45 hours of Internship
• Total 600-700 hour of spending under Internship module courses to be completed for award
of Internship Completion Certification along with regular passing gradesheet. (e.g. Total
15 weeks of 5 days/week of 8 hrs/day spent=600hrs for complete degree duration)
• Total weeks of Internship shall be considered based on Hrs spent/Day
• For Internship course, No load to be allotted for mentors in faculty load distribution sheet.
Internship Modules & Contents Across Semester 2 to Semester 8

FY: (Sem II)

Internship Code Course Name Hours/Duration Credits
INT21 Internship-I 80-120 hrs (2-3 Weeks ) 02
Winter Vacation After SEM-I
& during SEM-II of FY

Prerequisite: Fundamental knowledge of Engineering and Technology

Internship 1. To get acquainted with institute level technical activities and

Objectives: initiatives.
2. To participate in department/Institute level technical learning and
training initiatives through Professional
cells/clubs/committees/bodies.
Internship Upon completion of the course, students will be able to:
Outcomes: 1. Get practical experience of institutional setting.
2. Meet and interact with new people and learn networking, innovation
and entrepreneurial skills.
3. Promote academic, professional and/or personal development.

Supporting Activities to be completed under Internship

• Attending Industry Workshops organised by departments
Activity- • Working in consultancy or research project initiated by department
Inter/Intra • Technical festival (participation)
Institutional
• Working in IIC Cell, Entrepreneurship Cell, NISP, IPR cell and/or
Activities
any other technical professional body/cell/committee/club of the
institute
• Activities related to Incubation or Innovation
• Learning in departmental Labs, Tinkering Lab

Term Work Assessment:

Duration to be considered for assessment:
Week Ends/ Semester Break/End of Semester (After ESE & Before Next Term Start )
1. Batch wise Faculty Supervisor who is the proctor (mentor) of the batch
Guidelines: will be allotted as in-charge for the course, at start of the Academic
year.
2. Students will submit the participation certificate of the activities to the
faculty mentors.
3. For working in cells related activities, Cell coordinator will submit list
of actively involved & participated students of each department,
semester wise to all department HODs, verified and authenticated by
Dean Students Welfare.
4. HODs will circulate the student list to all faculty mentors for
consideration of Hours spends under mentioned department activities.
5.For department Lab learning, FY HOD will circulate Term End report
to all faculty mentors with list of student’s undergone innovative
learning, verified by department academic coordinator.
6. Students will submit evaluation sheet by attaching Xerox copies of
Internship & other participation certificates & faculty mentor will
verify the Xerox from original copy for assessment purpose.
TW Marks (25) Assessment & evaluation based on rubrics :
& Certificate : Hours Spent for Internship: max 20 marks
Achievement/Recognition: max 05 marks
&
Internship and Activity Completion/Participation Certificates and
Evaluating Report

SY (Sem III)
Internship Code Internship Hours/Duration Credits
Name
INT32 Internship-II 80-120 hrs (2 -3 Weeks) 02
Summer Vacation After SEM-II
& during SEM-III of SY

Prerequisite: Fundamental knowledge of program specific tools, instruments,

devices and programming languages etc.
Internship 1. To get the exposure to Innovation/IPR/ Entrepreneurship/ Startup
Objectives: initiatives
2. To participate & experience Incubation, Innovation & Business
development culture
Internship Upon completion of the course, students will be able to:
Outcomes: 1. Learn innovation and entrepreneurial skills to supplement engineering
knowledge.
2. Integrate theoretical aspects learned in classes with the practical world
3. Develop an innovative idea to be processed as a start-up

Supporting Activities to be completed under Internship

1. Participation in Innovation related competitions e.g. Hackathons etc.
Activity- 2. Awareness & knowledge sessions about Development of new
Innovation/ product/Business Plan/Registration of Start-up
3. Participation in all activities of IIC Cell, E-Cell, NISP, IPR Cell like
IPR/
● IPR workshop/
Entrepreneurship
● Leadership Talk
● Idea Design
● Innovation/Business Competition

Term Work Assessment:

Duration to be considered for assessment:

Week Ends/ Semester Break/End of Semester (After ESE & Before Next Term Start )

1. 1. Batch wise Faculty Supervisor who is the proctor (mentor) of the batch
Guidelines: will be allotted as in-charge for the course, at start of the Academic year.
2. 2. Students will submit the participation certificate of the activities to the
faculty mentors.
3. 3. For working in cells related activities, Cell coordinator will submit list
of actively involved & participated students of each department, semester
wise to all department HODs, verified and authenticated by Dean
Students Welfare.
4. 4. HODs will circulate the student list to all faculty mentors for
consideration of Hours spends under mentioned department activities.
5. Department IIIC Cell coordinator will collect, maintain each student
proofs/reports from all faculty mentors, department internship analysis
report will be prepared & submitted to Dean, IIIC for AICTE-CII survey
data
6. Students will submit evaluation sheet by attaching Xerox copies of all
participation/ IPR/ Copyright certificates & faculty mentor will verify it
with original copies, for assessment purpose.

TW Marks (25) Assessment & evaluation based on rubrics:

& Certificate : Hours Spent for Internship: max 20 marks
Achievement/Recognition: max 05 marks
&
Internship and Activity Completion /Participation Certificates and
Evaluating Report

SY (Sem IV)
Internship Code Internship Hours/Duration Credits
Name
INT43 Internship-III 80-120 hrs (2 - 3 Weeks) 02
Winter Vacation After SEM-III
& during SEM-IV of SY

Prerequisite: Skill sets of engineering and technology specific tools,

instruments, devices and programming languages etc.
Internship 1. To get the industrial environment expose for creating competent
Objectives: professionals for the industry.
2. To understand the psychology of the workers and their habits, attitudes
and approach to problem solving.
Internship Upon completion of the course, students will be able to:
Outcomes: 1. Get an expose to work with the future employers.
2. Familiarize with various materials, processes, products and their
applications along with relevant aspects of quality control in product
development lifecycle.

Supporting Activities to be completed under Internship

Internships in the field of:
● Industries
Activity- ● Government Sector
Internship ● Non-governmental Organization (NGO)
● MSMEs
● Rural Internship

Term Work Assessment:

Duration to be considered for assessment:

Week Ends/ Semester Break/End of Semester (After ESE & Before Next Term Start )
1. Batch wise Faculty Supervisor who is the proctor (mentor) of the batch
Guidelines: will be allotted as in-charge for the course, at start of the Academic
year.
2. Students will submit the participation certificate of the activities to the
faculty mentors.
3. For working in cells related activities, Cell coordinator will submit list
of actively involved & participated students of each department,
semester wise to all department HODs, verified and authenticated by
Dean Students Welfare.
4. HOD will circulate the student list to all faculty mentors for
consideration of Hours spends under mentioned department activities.
5. Department IIIC Cell coordinator will collect, maintain each student
proofs/reports from all faculty mentors, department internship analysis
report will be prepared & submitted to Dean, IIIC for AICTE-CII
survey data
6. Students will submit evaluation sheet by attaching Xerox copies of all
participation/ IPR/ Copyright certificates & faculty mentor will verify
it with original copies, for assessment purpose.

TW Marks (25) Assessment & evaluation based on rubrics:

& Certificate : Hours Spent for Internship: max 20 marks
Achievement/Recognition: max 05 marks
&
Internship and Activity Completion/Participation Certificates and
Evaluating Report

TY (Sem V)
Internship Internship Hours/Duration Credits
Code Name
INT54 Internship-IV 80-160 hrs (2 - 4 Weeks) Summer 02
Vacation After SEM-IV & during
SEM-V of TY

Prerequisite:
List of probable industries and organizations offering internships in
Engineering and Technology. Awareness about problem areas in
rural India
Internship 1. To get the awareness about engineer’s responsibilities and ethics.
Objectives: 2. Opportunities to learn understand and sharpen the real time technical /
managerial skills required at the job.
Internship Upon completion of the course, students will be able to:
Outcomes: 1. Get an opportunity to practice communication and teamwork skills.
2. Get an opportunity to learn strategies like time management, multi-
tasking etc in an industrial setup.

Supporting Activities to be completed under Internship

Activity- 1. Long Term Goal under Rural Development Internships or
Rural 2. Mandatory internship for developing project with:
Internships ● Industries
&/ ● Government Sector
Internships ● Non-governmental Organization (NGO)
● MSMEs

Term Work Assessment:

Duration to be considered for assessment:
Week Ends/ Semester Break/End of Semester (After ESE & Before Next Term Start )
1. Batch wise Faculty Supervisor who is the proctor (mentor) of the batch
Guidelines: will be allotted as in-charge for the course, at start of the Academic year.
2. Students will submit the participation certificate of the activities to the
faculty mentors.
3. For working in cells related activities, Cell coordinator will submit list
of actively involved & participated students of each department, semester
wise to all department HODs, verified and authenticated by Dean
Students Welfare.
4. HOD will circulate the student list to all faculty mentors for
consideration of Hours spends under mentioned department activities.
5. Department IIIC Cell coordinator will collect, maintain each student
proofs/reports from all faculty mentors, department internship analysis
report will be prepared & submitted to Dean, IIIC for AICTE-CII survey
data
6. Students will submit evaluation sheet by attaching Xerox copies of all
participation/ IPR/ Copyright certificates & faculty mentor will verify it
with original copies, for assessment purpose.
TW Marks (25) Assessment & evaluation based on rubrics:
& Certificate : Hours Spent for Internship: max 20 marks
Achievement/Recognition: max 05 marks
&
Internship and Activity Completion/Participation Certificates and
Evaluating Report
TY ( Sem VI)
Internship Internship Hours/Duration Credits
Code Name
INT65 Internship- 80-160 hrs (2-4 Weeks) 02
V Winter Vacation After SEM-V &
during SEM-VI of TY

Prerequisite: List of probable industries and organizations offering internships

on live projects. Awareness about probable solutions for identified
problem areas in rural India
Internship 1. To understand the social, economic and administrative considerations
Objectives: of working environment in industries, government, NGOs and private
organizations.
2. Learn to apply the Technical knowledge for solving real life problems.
Internship Upon completion of the course, students will be able to:
Outcomes: 1. Get an opportunity to get hired by the Industry/ organization.
2. Decide if working in the industry or set up a start-up would be best
career option to pursue.

Supporting Activities to be completed under Internship

1. Long Term Goal under Rural Development Internships or
Activity- 2. Mandatory internship for developing project with:
Rural ● Industries
Internships ● Government Sector
● Non-governmental Organization (NGO)
& Internships
● MSMEs

Term Work Assessment:

Duration to be considered for assessment:
Week Ends/ Semester Break/End of Semester (After ESE & Before Next Term Start )
1. Batch wise Faculty Supervisor who is the proctor (mentor) of the batch
Guidelines: will be allotted as in-charge for the course, at start of the Academic
year.
2. Students will submit the participation certificate of the activities to the
faculty mentors.
3. For working in cells related activities, Cell coordinator will submit list
of actively involved & participated students of each department,
semester wise to all department HODs, verified and authenticated by
Dean Students Welfare.
4. HODs will circulate the student list to all faculty mentors for
consideration of Hours spends under mentioned department activities.
5. Department IIIC Cell coordinator will collect, maintain each student
proofs/reports from all faculty mentors, department internship analysis
report will be prepared & submitted to Dean, IIIC for AICTE-CII
survey data
6. Students will submit evaluation sheet by attaching Xerox copies of all
participation/ IPR/ Copyright certificates & faculty mentor will verify
it with original copies, for assessment purpose.
TW Marks (25) Assessment & evaluation based on rubrics:
& Certificate : Hours Spent for Internship: max 20 marks
Achievement/Recognition: max 05 marks
&
Internship and Activity Completion/Participation Certificates and
Evaluating Report

LY (Sem VII)
Internship Internship Hours/Duration Credits
Code Name
INT76 Internship- 80-160 hrs 02
VI (2-4 Weeks)
Summer Vacation of
TY and during SEM-VII
of LY

Prerequisite: In depth knowledge about societal/research/innovation/

entrepreneurial problems and appropriate applicable solutions
available through use of technology.
Internship 1. To gain the experience in preparing and writing Technical
Objectives: documentation/ reports for product/projects.
2. To Identify and analyse the societal/research/entrepreneurial
problem in detail to define its scope with problem specific data.
3. To develop clarity of presentation based on communication,
teamwork and leadership skills.
Internship Upon completion of the course, students will be able to:
Outcomes: 1. Apply the engineering and technical knowledge for problem
identification, analysis, design and developing solutions.
2. Present and demonstrate the real time problem solution across
national/international project competitions and conference.

Supporting Activities to be completed under Internship

For Sem VII PBL Course-Major Project-A, selected topic:
Activity- 1. Review literature through reference papers from reputed
PBL-Major conferences/ journals like IEEE, Elsevier, ACM etc. which are not
Project A- more than 3 years old.
Work/ 2. Participate in multiple Project Competitions presenting the Project A
Seminars solution
3. Participation in International Conferences presenting the literature
review and/or hypothesis for innovative solution.
4. Participation at institute annual International Conference on
Advances in Science and Technology-ICAST & other Conferences
/Journals.
Term Work Assessment:
Duration to be considered for assessment:
Week Ends/ Semester Break/End of Semester (After ESE & Before Next Term Start )
1. Batch wise Faculty Supervisor who is the proctor (mentor) of the
Guidelines: batch will be allotted as in-charge for the course, at start of the
Academic year.
2. Students will submit the participation certificate of the activities to
the faculty mentors.
3. Department IIIC Cell coordinator will collect, maintain each student
proofs/reports from all faculty mentors, department internship
analysis report will be prepared & submitted to Dean, IIIC for
AICTE-CII survey data
4. Students will submit evaluation sheet by attaching Xerox copies of
all participation/ IPR/ Copyright certificates & faculty mentor will
verify it with original copies, for assessment purpose.

TW Marks (25) Assessment & evaluation based on rubrics:

& Hours Spent for Internship: max 20 marks
Certificate : Achievement/Recognition: max 05 marks
&
Certificate Based on :
1.Project Competition certificate,
2. Participation in Conferences/Publications and/or proof of ICAST
participation & presentation.

LY (Sem VIII)
Internship Code Internship Hours/Duration Credits
Name
INT87 Internship-VII 80-160 hrs (2-4 Weeks) 02
Winter Vacation of Sem VII and
During SEM-VIII of LY

Prerequisite: In depth knowledge about filling IPR/ copywriting a

product/solution.
Internship 1. To gain the knowledge of filling patent and Copy write.
Objectives: 2. Presenting technology solutions across worldwide problems
through competitions and publications.
Internship Upon completion of the course, students will be able to:
Outcomes: 1. National and international recognition through IPR and/or copy
writes and paper publications.
2. Convert problem solution as a business plan for entrepreneurial
product.

Supporting Activities to be completed under Internship

For Sem VIII PBL Course-Major Project-B, selected topic:
1. File for Project solution Copyright and/or
Activity- File for Project topic IRP/Patent
PBL Major 2. Participate at Institute Annual Project Competition-INTECH
Project B 3. Publish the project solution at reputed International Journals,
Work/Conference preference should be given to UGC care list and/or SCI indexed
Presentation journals.

Term Work Assessment:

Duration to be considered for assessment:
Week Ends and during Semester
1. Batch wise Faculty Supervisor who is the proctor (mentor) of the
Guidelines: batch will be allotted as in-charge for the course, at start of the
Academic year.
2. Students will submit the participation certificate of the activities to
the faculty mentors.
3. Department IIIC Cell coordinator will collect, maintain each student
proofs/reports from all faculty mentors, department internship
analysis report will be prepared & submitted to Dean, IIIC for
AICTE-CII survey data
4. Students will submit evaluation sheet by attaching Xerox copies of
all participation/ IPR/ Copyright certificates & faculty mentor will
verify it with original copies, for assessment purpose.

TW Marks (25) & Assessment & evaluation based on rubrics:

Certificate : Hours Spent for Internship: max 20 marks
Achievement/Recognition: max 05 marks
&
Certificate Based on :
1.Project Copyright/ Project IRP
2. Project Competition certificate (INTECH)
3.International Journal Publication proof

Dr. S.K Ukarande

Principal

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