Program Structure for Fourth Year Information Technology

Semester VII & VIII

UNIVERSITY OFMUMBAI
(With Effect from2022-2023)
Semester VII
Teaching Scheme
Credits Assigned
Course (Contact Hours)
Course Name
Code Pract.
Theory Theory Pract. Total
Tut.
ITC701 AI and DS –II 3 -- 3 -- 3
ITC702 Internet of Everything 3 -- 3 3
Department Optional
ITDO701 3 -- 3 -- 3
Course – 3
X
Department
ITDO702 3 -- 3 -- 3
Optional Course –4
X
Institute Optional
ITIO701X 3 -- 3 -- 3
Course – 1
ITL701 Data Science Lab -- 2 -- 1 1
ITL702 IOE Lab -- 2 -- 1 1
ITL703 Secure Application -- 2 -- 1 1
Development
ITL704 Recent Open Source -- 2 -- 1 1
Project Lab
ITP701 Major Project I -- 6# -- 3 3
Total 15 14 15 7 22
Examination Scheme
Term Prac/o
Theory Total
Work ral
Course End Exam.
Course Name Internal Assessment Sem Duration
Code
Exam (in Hrs)

Test1 Test2 Avg

ITC701 AI and DS –II 20 20 20 80 3 -- -- 100

ITC702 Internet of Everything 20 20 20 80 3 -- -- 100
Department
ITDO701 20 20 20 80 3 -- -- 100
Optional Course –3
X
Department
ITDO702 20 20 20 80 3 -- -- 100
Optional Course –4
X
Institute Optional
ILO701X 20 20 20 80 3 -- -- 100
Course – 1
ITL701 Data Science Lab -- -- -- -- -- 25 25 50
ITL702 IOE Lab -- -- -- -- -- 25 25 50
ITL703 Secure Application -- -- -- -- -- 25 25 50
Development
ITL704 Recent Open Source -- -- -- -- -- 25 25 50
Project Lab
ITP701 Major Project I -- -- -- -- -- 25 25 50
Total -- -- 100 400 -- 125 125 750
 # indicates work load of Learner (Not Faculty), for Major Project

ITDO701X Department Optional Course –3

ITDO7011 Storage Area Network
ITDO7012 High Performance computing
ITDO7013 Infrastructure Security
ITDO7014 Software Testing and QA

ITDO702X Department Optional Course –4

ITDO7021 MANET
ITDO7022 AR – VR
ITDO7023 Quantum Computing
ITDO7024 Information Retrieval System

# Institute Level Optional Course (ILO)

Every student is required to take one Institute Elective Course for Semester VII, which is
not closely allied to their disciplines. Different sets of courses will run in the both
the semesters.

ILO701X Institute Optional Course – 1 ( Common for all branches will be notified )
ILO7011 Product Lifecycle Management
ILO7012 Reliability Engineering
ILO7013 Management Information System
ILO7014 Design of Experiments
ILO7015 Operation Research
ILO7016 Cyber Security and Laws
ILO7017 Disaster Management and Mitigation

Measures
ILO7018 Energy Audit and Management
ILO7019 Development Engineering
 Course Code Course Name Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
03 -- -- 03 -- -- 03
ITC701 AI and DS –II

Course Course
Examination Scheme
Code Name
Theory Marks
Internal assessment End Term
Practical Oral Total
Avg. of 2 Sem. Work
Test1 Test 2
Tests Exam
AI and DS –
ITC701 20 20 20 80 -- -- -- 100
II

Course Objectives:

Sr. No. Course Objectives

The course aims:
1 To model a decision making for a new problem in an uncertain domain.
2 To demonstrate Cognitive skills of Artificial Intelligence.
3 To become familiar with the basics of Fuzzy Logic and Fuzzy Systems.
4 To become familiar with Deep Learning Concepts and Architectures.
5 To define and apply metrics to measure the performance of various learning algorithms.
6 To enable students to analyze data science methods for real world problems.

Course Outcomes:

Sr. No. Course Outcomes Cognitive levels of

attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Design models for reasoning with uncertainty as well as the use of unreliable L1,L2,L3
information.
2 Analyze the process of building a Cognitive application. L1,L2,L3,L4
3 Design fuzzy controller system. L1,L2,L3
4 Apply learning concepts to develop real life applications. L1,L2,L3
5 Evaluate performance of learning algorithms. L1,L2,L3,L4,L5
6 Analyze current trends in Data Science. L1,L2,L3,L4

Prerequisite: AI and DS - 1 (ITC604) , Data Mining & Business Intelligence (ITC601)

DETAILED SYLLABUS:

Sr. Module Detailed Content Hours CO

No. Mapping
 02 --
0 Prerequisite Intelligent Agents, Search Techniques, Knowledge and
Reasoning, Data Science, Machine Learning.
04 CO1
Uncertainty in AI, Inference using full joint distributions,
Bayes Theorem, the semantics of Bayesian Networks,
I Uncertainty Inference in Bayesian networks, Decision Theory, Markov
Decision Processes.
Self-learning Topics: Hidden Markov Model (HMM),
Gaussian Mixture Model (GMM).
06 CO2
Foundation of Cognitive Computing, Design Principles for
Cognitive Systems, Natural Language Processing in Support of
a Cognitive System, Representing Knowledge in Taxonomies
Cognitive and Ontologies, Applying Advanced Analytics to Cognitive
II
Computing Computing, The Process of Building a Cognitive Application.

Self-learning Topics: Cognitive Systems such as IBM’s

Watson.

07 CO3
Introduction to Fuzzy Sets, Properties of Fuzzy Sets,
Operations on Fuzzy Sets, Fuzzy Membership Functions,
Fuzzy Relations with Operations and its Properties, Fuzzy
Composition: Max-Min Composition, Max-Product
Fuzzy Logic
Composition, Defuzzification Methods, Architecture of
III & Its
Mamdani Type Fuzzy Control System, Design of Fuzzy
Applications
Controllers like Domestic Shower Controller, Washing
Machine Controller, Water Purifier Controller, etc.

Self-learning Topics: Other Fuzzy Composition

Operations, Fuzzy Inference System (FIS) & ANFIS.
08 CO4
Introduction to Deep Learning, ANN, Machine Learning Vs
Deep Learning, Working of Deep Learning; Convolutional
Neural Network: Introduction, Components of CNN
Architecture, Properties of CNN, Architectures of CNN,
Introduction to Applications of CNN, Recurrent Neural Network: Introduction,
IV
Deep Learning Simple RNN, LSTM Implementation, Deep RNN,
Autoencoder: Introduction, Features, Types, Applications of
Deep Learning.

Self-learning Topics: Restricted Boltzmann Machine

(RBM).
06 CO4
Ensemble Classifiers: Introduction to Ensemble Methods, CO5
Bagging, Boosting, Random forests, Improving classification
accuracy of Class-Imbalanced Data.
Advanced ML
V Classification Metrics for Evaluating Classifier Performance, Holdout
Techniques Method and Random Subsampling, Cross-Validation,
Bootstrap, Model Selection Using Statistical Tests of
Significance, Comparing Classifiers Based on Cost–Benefit
and ROC Curves.
Self-learning Topics: Introduction to ML (Revision),
 Introduction to Reinforcement Learning.

06 CO6
Data Science: applications and case studies, Data science for
Trends and text, image, video, audio. Data science for Multimodal
VI applications in applications.
Data Science
Self-learning Topics: ImageNet Large Scale Visual
Recognition Challenge (ILSVRC).

Text Books:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition, Pearson Education.
2. Judith S. Hurwitz, Marcia Kaufman, Adrian Bowles, “Cognitive Computing and Big Data Analytics”, Wiley India, 2015.
3. S.N. Sivanandam, S.N. Deepa, “Principles of Soft Computing”, Wiley Publication.
4. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, “Deep Learning Using Python”, Wiley India, 2020.
5. B. Uma Maheshwari, R. Sujatha, “Introduction to Data Science Practical Approach with R and Python”, Wiley India,
2021.
6. François Chollet, “Deep Learning with Python”, Manning Publications, 2018.
7. Han J, Kamber M, Pei J, “Data Mining Concepts and Techniques”, Third Edition, Morgan Kaufmann.

References:
1. Deepak Khemani, “A First Course in Artificial Intelligence”, McGraw Hill Publication.
2. Ethem Alpaydin , “Introduction to Machine Learning”, PHI Learning Pvt. Ltd.
3. Jon Krohn, Grant Beyleveld, Aglae Bassens, “Deep Learning Illustrated: A Visual, Interactive Guide to Artificial
Intelligence”, Pearson Education.
4. Prateek Joshi, “Artificial Intelligence with Python”, Packt Publishing.

Online References:
Sr. No. Website Links

1 https://d2l.ai/index.html

2 https://onlinecourses.nptel.ac.in/noc20_cs62/preview

3 https://onlinecourses.nptel.ac.in/noc22_cs35/preview

4 https://www.coursera.org/specializations/deep-learning

5 https://onlinecourses.nptel.ac.in/noc22_cs56/preview

Assessment:
Internal Assessment for 20 marks:
Consisting of Two Compulsory Class Tests
Approximately 40% to 50% of syllabus content must be covered in First test and remaining 40% to 50% of syllabus contents
must be covered in second test.

End Semester Examination: Some guidelines for setting the question papers are as:
 Weightage of each module in end semester examination is expected to be/will be proportional to number of respective
lecture hours mentioned in the syllabus.
 Question paper will comprise of total six questions, each carrying 20 marks.
 Q.1 will be compulsory and should cover maximum contents of the syllabus.
 Remaining question will be mixed in nature (for example if Q.2 has part (a) from module 3 then part (b) will be from
any other module. (Randomly selected from all the modules.)
 Total four questions need to be solved.

Course Code Course Name Theory Practical Tutorial Theory Practical/ Tutorial Total
Oral
Internet of 03 -- -- 03 -- -- 03
ITC702 Everything

Course Course
Examination Scheme
Code Name
Theory Marks
Internal assessment End Term
Practical Oral Total
Avg. of 2 Sem. Work
Test1 Test 2
Tests Exam
Internet of
ITC702 Everything 20 20 20 80 -- -- -- 100

Course Objectives:

Sr. No. Course Objectives

The course aims:
1 To comprehend Characteristics and Conceptual Framework of IoT.
2 To understand levels of the IoT architectures.
3 To correlate the connection of smart objects and IoT access technologies.
4 To Interpret edge to cloud protocols.
5 To explore data analytics and data visualization on IoT Data.
6 To explore IoT applications.

Course Outcomes:

Sr. No. Course Outcomes Cognitive levels of

attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Describe the Characteristics and Conceptual Framework of IoT. L1,L2
2 Differentiate between the levels of the IoT architectures. L1,L2,L3,L4
3 Analyze the IoT access technologies. L1,L2,L3,L4
4 Illustrate various edge to cloud protocol for IoT. L1,L2,L3
5 Apply IoT analytics and data visualization. L1,L2,L3
6 Analyze and evaluate IoT applications. L1,L2,L3,L4

Prerequisite:
1. Python programming
2. C programing language
3. Computer Networks
DETAILED SYLLABUS:
Sr. Module Detailed Content Hours CO Mapping
No.

0 Prerequisite Ports, Timers ,Programming of controller , How to 02

use IDE to write code of microcontroller, TCP-IP
protocol stack
I Introduction to IoT Introduction to IoT- Defining IoT, Characteristics of 04 CO1
IoT, Conceptual Framework of IoT, Physical design
of IoT, Logical design of IoT, Functional blocks of
IoT, Brief review of applications of IoT. Smart
Object – Definition, Characteristics and Trends
Self-learning Topics: Hardware and software
development tools for - Arduino, NodeMCU, ESP32,
Raspberry Pi, for implementing internet of things,
Simulators-Circuit.io,Eagle,Tinkercad
II IoT Architecture Drivers Behind New Network Architectures : 06 CO2
Scale,Security,Constrained Devices and Networks
,Data,Legacy Device Support
Architecture : The IoT World Forum (IoTWF)
Standardized Architecture :Layer 1-7, IT and OT
Responsibilities in the IoT Reference
Model,Additional IoT Reference Models
A Simplified IoT Architecture
The Core IoT Functional Stack ::Layer 1-3 ,
Analytics Versus Control Applications , Data Versus
Network Analytics Data Analytics Versus Business
Benefits , Smart Services,
IoT Data Management and Compute Stack :Fog
Computing , Edge Computing ,The Hierarchy of
Edge, Fog, and Cloud
Self-learning Topics: Brief review of applications
of IoT: Connected Roadways , Connected Factory,
Smart Connected Buildings , Smart Creatures etc,
III Principles of 08 CO3
Connected Devices RFID and NFC (Near-Field Communication),
and Protocols in IoT Bluetooth Low Energy (BLE) roles, LiFi , WPAN std
: 802.15 standards: Bluetooth, IEEE 802.15.4,
Zigbee, Z-wave, Narrow Band IoT, Internet Protocol
and Transmission Control Protocol, 6LoWPAN,
WLAN and WAN , IEEE 802.11, Long-range
Communication Systems and Protocols: Cellular
Connectivity-LTE, LTE-A, LoRa and LoRaWAN.

IV Edge to Cloud 08 CO4

Protocol HTTP, WebSocket, Platforms. HTTP - MQTT -
.Complex Flows: IoT Patterns: Real-time Clients,
MQTT, MQTT-SN, Constrained Application
Protocol (CoAP), Streaming Text Oriented Message
Protocol (STOMP), Advanced Message Queuing
Protocol (AMQP), Comparison of Protocols.

V IoT and Data Defining IoT Analytics, IoT Analytics challenges, 06 CO5
Analytics IoT analytics for the cloud, Strategies to organize
Data for IoT Analytics, Linked Analytics Data Sets,
 Managing Data lakes, The data retention strategy,
visualization and Dashboarding-Designing visual
analysis for IoT data, creating a dashboard ,creating
and visualizing alerts.
Self-learning Topics: AWS and Hadoop
Technology
VI IoT Application 04 CO6
Design Prototyping for IoT and M2M, Case study related to
: Home Automation (Smart lighting, Home intrusion
detection), Cities (Smart Parking), Environment
(Weather monitoring, weather reporting Bot, Air
pollution monitoring, Forest fire detection,
Agriculture (Smart irrigation), Smart Library.
Introduction to I-IoT, Use cases of the I-IoT,IoT and
I-IoT – similarities and differences, Introduction to
Internet of Behavior (IoB).

Self-learning Topics: Internet of Behaviors (IoB)

and its role in customer services

Text Book

1.Arsheep Bahga (Author), Vijay Madisetti, Internet Of Things: A Hands-On Approach Paperback, Universities Press, Reprint
2020
2.David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry, IoT Fundamentals Networking
Technologies, Protocols, and Use Cases for the Internet of Things CISCO.
3.Analytics for the Internet of Things (IoT) Intelligent Analytics for Your Intelligent Devices.Andrew Minteer,Packet
4.Giacomo Veneri , Antonio Capasso,” Hands-On Industrial Internet of Things: Create a powerful Industrial IoT infrastructure
using Industry 4.0”, Packt

References:
1. Pethuru Raj, Anupama C. Raman, The Internet of Things: Enabling Technologies, Platforms, and Use Cases by , CRC
press,
2. Raj Kamal, Internet of Things, Architecture and Design Principles, McGraw Hill Education, Reprint 2018.
3. Perry Lea, Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication
infrastructure, edge computing, analytics, and security, Packt Publications, Reprint 2018.
4. Amita Kapoor, “Hands on Artificial intelligence for IoT”, 1st Edition, Packt Publishing, 2019.
5. Sheng-Lung Peng, Souvik Pal, Lianfen Huang Editors: Principles of Internet of Things (IoT)Ecosystem:Insight Paradigm,
Springer

Online Resources:
Sr. No. Website Name
1. https://owasp.org/www-project-internet-of-things/
2. NPTEL: Sudip Misra, IIT Khargpur, Introduction to IoT: Part-1,
https://nptel.ac.in/courses/106/105/106105166/
3. NPTEL: Prof. Prabhakar, IISc Bangalore, Design for Internet of Things,
https://onlinecourses.nptel.ac.in/noc21_ee85/preview
4. Mohd Javaid, Abid Haleem, Ravi Pratap Singh, Shanay Rab, Rajiv Suman,Internet of
Behaviors (IoB) and its role in customer services,Sensors International,Volume
2,2021,100122,ISSN 2666-3511,https://doi.org/10.1016/j.sintl.2021.100122

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of syllabus
content must be covered in First IA Test and remaining 40% to 50% of syllabus content must be covered in
Second IA Test

 Question paper format

 Question Paper will comprise of a total of six questions each carrying 20 marks Q.1 will be compulsory
and should cover maximum contents of the syllabus

 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be from
different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be from any other
Module randomly selected from all the modules)

 A total of four questions need to be answered.

 Teaching Scheme (Contact
Hours) Credits Assigned
Course Code Course Name Theory Practical Tutorial Theory Practical Tutorial Total
& Oral
Data Science -- 2 -- -- 1 -- 01
ITL701
Lab

Examination Scheme
Course Theory Marks
Course Name
Code Internal assessment End Term Practical/
Total
Avg. of 2 Sem. Work Oral
Test1 Test 2
Tests Exam
ITL701 Data Science Lab -- -- -- -- 25 25 50

Lab Objectives:

Sr. No Lab Objectives

1 To apply reasoning for a problem in an uncertain domain.
2 To discuss the solution after building a Cognitive application.
3 To familiarize the students with the basics of Fuzzy Logic and Fuzzy Systems.
4 To familiarize the students with Learning Architectures and Frameworks.
5 To define and apply metrics to measure the performance of various learning algorithms.
6 To enable students to analyze data science methods for real world problems.

Lab Outcomes:
Sr. Lab Outcomes Cognitive levels
No of attainment as
per Bloom’s
Taxonomy
1 Implement reasoning with uncertainty. L1, L2, L3
2 Explore use cases of Cognitive Computing L1, L2
3 Implement a fuzzy controller system. L1, L2, L3
4 Develop real life applications using learning concepts. L1, L2, L3
5 Evaluate performance of applications. L1, L2, L3, L4
6 Implement and analyze applications based on current trends in Data Science. L1, L2, L3, L4, L5

Hardware & Software requirements:

Hardware Specifications Software Specifications

PC with following Configuration Python, MySQL or Database Software
1. Intel Core i3/i5/i7
2. 4 GB RAM
3. 500 GB Hard disk

Prerequisite: Artificial Intelligence and Data Science-I, Python Programming, Data Mining & Business Intelligence.
DETAILED SYLLABUS:

Sr. Module Detailed Content Hours LO

No. Mapping

1.Implement Inferencing with 02 LO1

I Uncertainty in AI
Bayesian Network in Python
2.Building a Cognitive Healthcare 04 LO2
application
3.Smarter cities: Cognitive Computing
Cognitive in Government
II
Computing
4.Cognitive computing in Insurance
5.Cognitive computing in Customer
Service
6.Implementation of Fuzzy 04 LO3
Membership Functions.
Fuzzy Logic & Its 7. Implementation of fuzzy set
III
Applications Properties.
8.Design of a Fuzzy control system
using Fuzzy tool.
9.Implementing Deep Learning 06 LO4
Applications like
a. Image Classification System
Introduction to Deep b. Handwritten Digit Recognition
IV
Learning System (like MNIST Dataset)
c. Traffic Signs Recognition
System.
d. Image Caption Generator
10.Implementation of supervised 05 LO4,LO5
learning algorithm like
Advanced ML a. Ada-Boosting
V Classification b. Random forests
Techniques 11.Evaluation of Classification
Algorithms.

12.Build text/ image/ video/ audio based 05 LO6

DS Applications such as
Mini-project on a. Chatbot
trends and b. Document Classification
VI
applications in Data c. Sentiment Analysis
Science d. Bounding Box Detection
e. Music/Video Genre
Classification

Text Books:

1. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition, Pearson Education.
2. Judith S. Hurwitz, Marcia Kaufman, Adrian Bowles, “Cognitive Computing and Big Data Analytics”, Wiley India,
2015.
 3. S.N. Sivanandam, S.N. Deepa, “Principles of Soft Computing”, Wiley Publication.
4. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, “Deep Learning Using Python”, Wiley India, 2020.
5. B. Uma Maheshwari, R. Sujatha, “Introduction to Data Science Practical Approach with R and Python”, Wiley
India, 2021.
6. François Chollet, “Deep Learning with Python”, Manning Publications, 2018.
7. Han J, Kamber M, Pei J, “Data Mining Concepts and Techniques”, Third Edition, Morgan Kaufmann.

References:
1. Deepak Khemani, “A First Course in Artificial Intelligence”, McGraw Hill Publication.
2. Ethem Alpaydin , “Introduction to Machine Learning”, PHI Learning Pvt. Ltd.
3. Jon Krohn, Grant Beyleveld, Aglae Bassens, “Deep Learning Illustrated: A Visual, Interactive Guide to Artificial
Intelligence”, Pearson Education.
4. Prateek Joshi, “Artificial Intelligence with Python”, Packt Publishing.

Online References:
Sr. No. Website Links

1 https://wisdomplexus.com/blogs/cognitive-computing-examples/

2 http://vlabs.iitb.ac.in/vlabs-dev/labs/machine_learning_old/labs/explist.php

3 https://infyspringboard.onwingspan.com/en/app/toc/lex_auth_01329517021676339249401_
shared/overview

4 https://infyspringboard.onwingspan.com/en/app/toc/lex_auth_01329500219268300841860_
shared/overview

5 https://www.udemy.com/course/ibm-watson-for-artificial-intelligence-cognitive-computing/
Term Work:

Term Work shall consist of at least 10 practical based on the above list. Also Term Work Journal must include Mini-Project
as mentioned in above syllabus.

Term Work Marks: 25 Marks (Total marks) = 10 Marks (Experiments) + 10 Marks (Mini-project) + 5 Marks (Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.
 Teaching Scheme
(Contact Hours) Credits Assigned
Course Course Name Theory Practical Tutorial Theory Practical Tutorial Total
Code & Oral
ITL702 Internet of -- 2 -- -- 1 -- 01
Everything Lab

Examination Scheme
Course Theory Marks
Course Name Internal assessment
Code End Term Practical/
Total
Avg. of Sem. Work Oral
Test1 Test 2
2 Tests Exam
ITL702 Internet of
Everything Lab -- -- -- -- 25 25 50

Lab Objectives:

Sr. Lab Objectives

No.
The Lab experiments aims:
1 To learn different types of sensors.
2 To design the problem solution as per the requirement analysis done using sensors.
3 To study the basic concepts of programming/sensors/ emulators.
4 To design and implement the mini project intended solution for project based learning.
5 To build and test the mini project successfully.
6 To improve the team building, communication and management skills of the students.

Lab Outcomes:

Sr. Lab Outcomes Cognitive Levels

No. of Attainment as
per Bloom’s
Taxanomy
On successful completion, of course, learner/student will be able to:
1 Identify the requirements for the real world problems. L1,L2
2 Conduct a survey of several available literatures in the preferred field of study. L1,L2
3 Study and enhance software/ hardware skills. L1,L2
4 Demonstrate and build the project successfully by hardware/sensor requirements, L1,L2,L3
coding, emulating and testing.
5 To report and present the findings of the study conducted in the preferred domain. L1,L2,L3,L4
6 Demonstrate an ability to work in teams and manage the conduct of the research study. L1,L2,L3,L4

Hardware & Software requirements:

Hardware Specifications Software Specifications

PC with following Configuration Python/C, Conitiki, Cooja or any open source
 1. Intel Core i3/i5/i7 Simulator
2. 4 GB RAM
3. 500 GB Hard disk
4. Arduino/ Raspberry Pi kit

Prerequisite: Basics of Java and Python Programming, Devops

DETAILED SYLLABUS:

Sr. No. Module Detailed Content Hours LO

Mapping

0 Prerequisite Experimentation with Microprocessor and Microcontroller , 02

Experimentation with python and c

I Arduino Introduction to Arduino, Hardware requirements, Software 05 LO1, LO2

requirements, Arduino Programming Language, Arduino Uno
Wired & Wireless connectivity, LCD commands, Serial
Communication commands. Program for blinking LED using
Arduino. Traffic Light pattern using Arduino. ESP8266 WiFi
Module

II Raspberry Pi Introduction to Raspberry Pi, Installation of NOOBS and 05 LO1,LO2

Raspbian on SD card, Libraries on Raspberry Pi, getting static IP
address of Raspberry Pi, Interfacing of Relay, DHT11, DC Motor
and LCD with Raspberry Pi.

05
III Contiki OS Contiki OS : History of Contiki OS, Applications, Features, LO3
,Communication Components in Contiki OS, Cooja simulator
,Running Cooja Simulator,

IV Cooja Using the Contiki OS with the Cooja simulator to program the 03 LO5,LO6
Simulator IoT for broadcasting data from sensors

V Protocols and Understanding of 6LowPAN , COAP and protocol 03 LO5,LO6

Security with implementation in Cooja . Encryption Decryption techniques
Cooja for IoT
 VI IoT data to Installing the Remote desktop server. Installation of Pi camera, 03 LO4,L06
Cloud Face recognition, serial peripheral interface using Raspberry Pi.
. DHT11 data logger with ThingSpeak/ thingsboard/ AWS/
Azure server .

Text Books:
1. Interconnecting Smart Objects with IP: The Next Internet, Jean-Philippe Vasseur, Adam Dunkels, Morgan
Kuffmann
2. Designing the Internet of Things , Adrian McEwen (Author), Hakim Cassimally
3. Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, Dr. Ovidiu
Vermesan, Dr. Peter Friess, River Publishers
4. Internet of Things (A Hands-on-Approach) , Vijay Madisetti , Arshdeep Bahga

References:
1. 6LoWPAN: The Wireless Embedded Internet, Zach Shelby, Carsten Bormann, Wiley
2. Building the internet of things with ipv6 and mipv6, The Evolving World of M2M Communications, Daniel
Minoli John Wiley & Sons
3. Contiki Cooja User Guide.
4. Fundamentals of Sensor Network Programming: Applications and Technology, By S. Sitharama Iyengar, Nandan
Parameshwaran, Vir V. Phoha, N. Balakrishnan, Chuka D. Okoye, Wiley publication.
5. Recent research/white papers

Digital Reference :
1. IoT Analytics -Thingshttps://thingspeak.com
2. https://www.contiki-ng.org/
3. http://www.ideationinstru.com/training.htm

List of Experiments.
Guidelines for Mini Project
1. The mini project work is to be conducted by a group of three students
2. Each group will be associated with a subject Incharge/ mini project mentor. The group should meet with the concerned
faculty during Laboratory hours and the progress of work discussed must be documented.
3. The students must understand the
a. Concept
b. Importance
c. Interdisciplinary
d. Challenges
e. Various applications/smart objects
f. Major Players/Industry Standards.

4. The students must understand the IoT Architecture:

a. Node Structure: Sensing, Processing, Communication, Powering
b. Networking: Topologies, Layer/Stack architecture
c. Communication Technologies: Introduction to ZigBee, BLE, WiFi, LTE, IEEE 802.11ah, Discuss data rate,
range, power, computations/bandwidth, QoS
d. Smartness - Signal Processing/Analytics: Impact on Power/Energy savings, dynamic networks, simple case
studies
e. IoT Fabricator: Introduction to Embedded electronics, fabricating electronics, Communication Network
 requirements, Data processing challenges – recreation, IP/security, Challenges
f. Hands-on in IoT: Projects based on some Hardware (Raspberry pi, Arduino, Intel, IITH Mote, Smartphones),
Software (Contiki, TinyOS, Android), IoT Fabricator etc. can be used.

5. The students may visit different websites to identify their IOT topic for the mini project.
6. The students may do surveys for different applications using different types of sensors for their mini project.
7. Each group will identify the Hardware (Motes from different Motes families) & sensor configuration and software
requirements for their mini project problem statement.
8. Design your own circuit board using multiple sensors etc.
9. Installation, configure and manage your sensors in such a way so that they can communicate with each other.
10. Work with operating system, emulator like contiki cooja and do coding to for input devices on sensors
11. Create an interface using Mobile/Web to publish or remotely access the data on the Internet.
12. Each group along with the concerned faculty shall identify a potential problem statement, on which the study and
implementation is to be conducted.
13. Analyze data collected from different sensors on platform like thinkspeak/AWS/Azure etc
14. Devops and Advance Devops concepts students have learnt in earlier semesters can be used while working with IoT
projects.
15. Each group may present their work in various project competitions or paper presentations.
16. A detailed report is to be prepared as per guidelines given by the concerned faculty.

Term Work:

Term Work shall consist of Mini-Project based on the above syllabus and guidelines. Also Term Work Journal must
include at least 2 assignments.

Term Work Marks:

25 Marks (Total marks) = 15 Marks (Mini-Project) + 5 Marks (Assignments) + 5 Marks (Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.
 Teaching Scheme (Contact
Hours) Credits Assigned
Course Code Course Name Theory Practical Tutorial Theory Practical Tutorial Total
& Oral
Secure -- 2 -- -- 1 -- 01
ITL703 Application
Development

Examination Scheme
Course Theory Marks
Course Name
Code Internal assessment End Term Practical/
Total
Avg. of 2 Sem. Work Oral
Test1 Test 2
Tests Exam
Secure
ITL703 Application -- -- -- -- 25 25 50
Development

Lab Objectives:

Sr. No Lab Objectives

 The Lab experiments aims:
1  To understand the secure programming of application code.
2  To understand the Owasp methodologies and standards.
3  Understand and Identify main vulnerabilities inherent in applications.
4  Understand how Data Validation and Authentication can be applied for application .
5  Understand how to apply Security at Session Layer Management.
6  Understand how to apply to secure coding for cryptography.

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  Apply secure programming of application code. L1,L2,L3
2  Understand the Owasp methodologies and standards. L1,L2,L3
3  Identify main vulnerabilities inherent in applications. L1,L2,L3
4 Apply Data Validation and Authentication for application L1,L2,L3,L4,L5
5 Apply Security at Session Layer Management L1,L2,L3,L4,L5
6 Apply secure coding for cryptography. L1,L2,L3,L4,L5

Hardware & Software requirements:

 Hardware Specifications Software Specifications
PC with following Configuration Web Application, HTML5, CSS3, Java, C, Python,
1. Intel Core i3/i5/i7 MySQL or Database Software.
2. 4 GB RAM Internet Connection, Browser, Security tools. SAST
3. 500 GB Hard disk tools etc.

 Prerequisite: Knowledge of programming languages like java/python/C is required.

DETAILED SYLLABUS:

Sr. Module Detailed Content Hours LO

No. Mapping

Programming Language and Web application basic 02

0 Prerequisite
concepts.
Introduction to laws, standards and guidelines of cyber 04 LO1
security. What do you mean by attacks, types of attacks
Introduction to and statistics of main vulnerabilities?
I Secure
Programming
Lab1: Study of different laws and standards of cyber
security.
Software Development Lifecycle. Risk Analysis. Threat 06 LO2
Modeling. Study different SAST (Static Application
Security Testing) tools. Study different top 10
methodologies and guidelines of OWASP (Open Web
Application Security Project) for the secure application
development. Any top 5 OAT. Best eight guidelines for
Secure Coding. Understand the flow of Verification
testing for secure coding.
Methodologies for
II developing secure
code
Lab2: Case study for SDLC.
Lab3: Exercise on Threat Modeling.
Lab4: Study of SAST Tools (open Source like GitHub,
GitLab and so on) and use at least one for practical
Lab5: Study and implement at least any 5
methodologies of OWASP.
Lab6: Study and implement at least any 5 OAT Denial
of Inventory for E-commerce Website..
Introduction to the HTTP protocol. Owasp Web 04 LO3
VAPT of Security Testing Guidelines. Tools for VAPT testing.
III
Applications
Lab7: Use Burp proxy to test web applications.
Guidelines for input data validation (Data type, Data 05 LO4
Data Validation & size, Data range, Data Content etc.) and
IV
Authentication authentication for login page. Types of
Authentication attacks. Study different type of
 vulnerabilities like SQL Injection vulnerability,
LDAP and XPath Injection vulnerabilities, Cross-
Site Scripting (XSS) vulnerability, OS Command,
LFI/RFI, Unvalidated file upload and buffer
overflow etc.

Lab8: Registration Page Data Validation.

Lab9: SQL injection vulnerability allows login page
to bypass.
Lab10: LDAP and XPath Injection vulnerabilities for
login /registration page.
Lab11: Cross-Site Scripting (XSS) vulnerability Lab
Lab12: OS Command vulnerability Lab
Lab13: LFI/RFI or Unvalidated file upload or Buffer
Overflow vulnerability Lab.
Lab14: Online Password attack.
Introduction to Session Layer in Web Applications 03 LO5
and management. Session Management Best
practices according to OWASP.
Security in Session
V
Layer
Lab15: Session Management for Web Application.

Overview of cryptography and guidelines for using 02 LO6

encryption. Types of cryptography ie symmetric and
asymmetric. Hashing Algorithms etc.
Secure Coding for
VI
cryptography.
Lab16: Symmetric and Asymmetric
Lab17: Symmetric Encryption and Hashing.

Text & References Books:

1. Fundamental Practice for Secure Software Development.

2. The OWASP Automated Threat Handbook - Web Applications.
3. OWASP Alpha Release Code Review Guide 2.0
4. Secure Programming HOWTO
5. OWASP Quick reference guide 2.

Online References:
Sr. No. Website Links

1 https://www.udemy.com/course/secure-coding-secure-application-development/

2 https://kirkpatrickprice.com/blog/secure-coding-best-practices/
 3 https://owasp.org/www-project-automated-threats-to-web-applications/assets/oats/EN/OAT-
021_Denial_of_Inventory

Term Work:

Term Work shall consist of at least 10 to 12 practical based on the above list. Also Term Work Journal must include at least
2 assignments as mentioned in above syllabus.

Term Work Marks: 25 Marks (Total marks) = 15 Marks (Experiments) + 5 Marks (Assignment) + 5 Marks (Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.
 Teaching Scheme (Contact
Hours) Credits Assigned
Course Course Name Theory Practical Tutorial Theory Practical Tutorial Total
Code & Oral
Recent Open -- 2 -- -- 1 -- 01
ITL704 Source Project
Lab

Examination Scheme
Course Theory Marks
Course Name
Code Internal assessment End Term Practical/
Total
Avg. of 2 Sem. Work Oral
Test1 Test 2
Tests Exam
Recent Open
ITL704 Source Project -- -- -- -- 25 25 50
Lab

Lab Objectives:

Sr. No Lab Objectives

The Lab experiments aims:
1 To understand the basic concepts of Open Source Software.
2 To understand the GPL(General Public Licence) and Contribute of Open Source.
3 To Understand Contribute to Open Source in different Operating System.
4 To Understand Contribute to Open Source in different Technologies.
5 To Understand Contribute to Open Source in different Network Management..
6 To Understand Contribute to Open Source in different Applications and Services.

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 Understand and apply the basic concepts of Open Source Software. L1,L2,L3
Identify the difference between the GPL(General Public Licence) and L1,L2,L3
2
Contribute to Open Source.
Apply and evaluate your knowledge for the Contribute to Open Source in L1,L2,L3,L4,L5
3
different Operating System.
Apply and evaluate your knowledge for the Contribute to Open Source in L1,L2,L3,L4,L5
4
different Technologies.
Apply and evaluate your knowledge for the Contribute to Open Source in L1,L2,L3,L4,L5
5
different Network Management..
Apply and evaluate your knowledge for the Contribute to Open Source in L1,L2,L3,L4,L5
6
different Applications and Services.
Hardware & Software requirements:

Hardware Specifications Software Specifications

PC with following Configuration Internet Connection.
1. Intel Core i3/i5/i7 Any Operating System.
2. 4 GB RAM Any technology open source tools/simulator/emulator.
3. 500 GB Hard disk Any open source Testing Tools
Any open source Network Monitoring Tools.
Any open Source Database tools.
Any open source Latex report writing tools.

Prerequisite: OS, Programming Language, DBMS, IP, Network.

DETAILED SYLLABUS:

Sr. Module Detailed Content Hours LO

No. Mapping

Basic Concepts of OS, Programming Language, Network 02 --

0 Prerequisite
and DMBS.
Overview of OSS. Basic Concepts of OSS. Advantages 04 LO1
of OSS. Difference between free and open source
I Introduction to OSS
software. What is GPL and Contribute to Open Source
Project. Different ways to contribute.
Overview of Contribute Open Source Project. Steps or 04 LO2
Guidelines of Contribute to Open Source Projects : 1.
Why to Contribute to open source Project. 2. What do
Contribute to Open you mean by Contribute Open Source Projects. 3.
II
Source Project.
Identifying the new/existing open source projects to
contribute. 4. Submit your contribute to open source.5.
Results after submitting your contribute to Open Source.
As per Contribute to Operating System to introduce 04 LO3
Contribute to Open new OS version, Improve OS by removing bugs,
III Source in Operating Improve existing Skill sets for growth in career.
System. Interact with Stakeholders for feedback and provide
training and mentoring. Start own Startup.
As per Contribute to various emerging technologies 04 LO4
like AI/ML/DL/Blockchain/IoT/Data Analytics/Cyber
Security/Andriod/iOS/Flutter/DeVoPs/Virtualization
Contribute to Open and Cloud Computing etc. To improve technologies.
IV Source in
Introduce new version of technologies, Improve
Technologies.
technologies by removing bugs, Improve existing Skill
sets for growth in career. Interact with Stakeholders
and provide training and mentoring. Start own Startup.
As per Contribute to different types of Network and 04 LO5
Contribute to Open Management Systems like LAN/WAN/MAN/Adhoc
V Source in Network Network/Data Centre/Wireless Network/Enterprise
Management. Network etc. To improve Networks as a Network
administrator. Design own Network as per customer
 requirements, Improve existing Skill sets for growth in
career. Interact with Stakeholders and provide training
and mentoring. Start own Startup.

As per Contribute to various Applications or Case 04 LO6

Contribute to Open studies using Cloud Services etc. To improve
Source in applications, remove bugs. Improve existing Skill sets
VI
Application & Cloud for growth in career. Interact with Stakeholders and
Services. provide training and mentoring. Start own Startup.

Guidelines for Recent Open Source Mini Project as per above syllabus.
 Students shall form a group of 3 to 4 students, while forming a group shall not be allowed less than three
or more than four students, as it is a group activity.
 Students should do survey and identify needs, which shall be converted into problem statement how to
contribute to open source mini project in consultation with faculty supervisor/head of department/internal
committee of faculties.
 Students shall submit implementation plan in the form of Gantt/PERT/CPM chart, which will cover weekly
activity of recent contribute to open source mini project.
 A log book to be prepared by each group, wherein group can record weekly work progress,
guide/supervisor can verify and record notes/comments.
 Faculty supervisor may give inputs to students during mini project activity; however, focus shall be on self-
learning.
 Students in a group shall understand contribute to open source problem effectively, propose multiple
solution and select best possible solution in consultation with guide/ supervisor.
 Students shall convert the best solution into working model using various components of their domain areas
and demonstrate.
 The solution to be validated with proper justification and report using open source tools to be compiled in
standard format of University of Mumbai.
 With the focus on the self-learning, innovation, addressing societal problems and entrepreneurship quality
development within the students through the open source Mini Projects.
Guidelines for Assessment of Recent Open Source Mini Project:
Term Work
 The review/ progress monitoring committee shall be constituted by head of departments of each
institute. The progress of mini project to be evaluated on continuous basis, minimum two reviews in
each semester.
 In continuous assessment focus shall also be on each individual student, assessment based on
individual’s contribution in group activity, their understanding and response to questions.
 Distribution of Term work marks for both semesters shall be as below;
o Marks awarded by guide/supervisor based on log book : 10
o Marks awarded by review committee : 10
o Quality of Project Report :05
Text & Reference Books:

1. Forge Your Future with Open Source: Build Your Skills. Build Your Network. Build the Future
of Technology. 1st Edition

Online References:

Sr. No. Website Links

1 https://github.com/freeCodeCamp/how-to-contribute-to-open-source

2 https://opensource.guide/how-to-contribute/#why-contribute-to-open-source

Term Work:

Term Work shall consist of at least Open Source Project based on the above syllabus. Also Term Work Journal must include
at least 2 assignments to explain contribute to open source as mentioned in above syllabus.

Term Work Marks: 25 Marks (Total marks) = 15 Marks (Mini-Project) + 5 Marks (Assignment) + 5 Marks (Attendance)

Oral Exam: An Oral exam will be held based on the above syllabus.
 Teaching Scheme Credits Assigned
Course Code Course Name (Contact Hours)
Theory Practical Tutorial Theory Practical Tutorial Total

ITM701 Major Project – I -- 06 -- -- 03 -- 03

Examination Scheme
Course Course Name
Code Theory Marks
Internal assessment End
Term Work Pract. /Oral Total
Sem.
Test1 Test 2 Avg.
Exam
ITM701 Major Project – I
-- -- -- -- 25 25 50

Course Objectives
1. To acquaint with the process of identifying the needs and converting it into the problem.
2. To familiarize the process of solving the problem in a group.
3. To acquaint with the process of applying basic engineering fundamentals to attempt solutions to the problems.
4. To inculcate the process of self-learning and research.
Course Outcome: Learner will be able to…
1. Identify problems based on societal /research needs.
2. Apply Knowledge and skill to solve societal problems in a group.
3. Develop interpersonal skills to work as member of a group or leader.
4. Draw the proper inferences from available results through theoretical/ experimental/simulations.
5. Analyse the impact of solutions in societal and environmental context for sustainable development.
6. Use standard norms of engineering practices
7. Excel in written and oral communication.
8. Demonstrate capabilities of self-learning in a group, which leads to life long learning.
9. Demonstrate project management principles during project work.

Guidelines for Major Project

 Students shall form a group of 3 to 4 students, while forming a group shall not be allowed less than three or
more than four students, as it is a group activity.
 Students should do survey and identify needs, which shall be converted into problem statement for mini
project in consultation with faculty supervisor/head of department/internal committee of faculties.
 Students shall submit implementation plan in the form of Gantt/PERT/CPM chart, which will cover weekly
activity of major project-I and major project-II.
 A log book to be prepared by each group, wherein group can record weekly work progress, guide/supervisor
can verify and record notes/comments.
 Faculty supervisor may give inputs to students during major project-I & II activity; however, focus shall be
on self-learning.
 Students in a group shall understand problem effectively, propose multiple solution and select best possible
solution in consultation with guide/ supervisor.
  Students shall convert the best solution into working model using various components of their domain areas
and demonstrate.
 The solution to be validated with proper justification and report to be compiled in standard format of
University of Mumbai.
 With the focus on the self-learning, innovation, addressing societal problems and entrepreneurship quality
development within the students through the Major Project, it is preferable that a single project of
appropriate level and quality to be carried out in two semesters by all the groups of the students. i.e. Major
Project-I in semester VII and Major Project-II in semesters VIII.
 However, based on the individual students or group capability, with the mentor’s recommendations, if the
proposed Major Project adhering to the qualitative aspects mentioned above gets completed in odd semester,
then that group can be allowed to Scopus paper publications in Journal/Conference or motivate for
Copyright or Indian Patent as an extension of the Major Project-1 with suitable improvements/modifications
after testing and analysis in even semester. This policy can be adopted on case by case basis.
Guidelines for Assessment of Major Project:
Term Work
1. The review/ progress monitoring committee shall be constituted by head of departments of each
institute. The progress of major project to be evaluated on continuous basis, minimum two reviews
in each semester VII and VIII.
2. In continuous assessment focus shall also be on each individual student, assessment based on
individual’s contribution in group activity, their understanding and response to questions.
3. Distribution of Term work marks for both semesters shall be as below;
a. Marks awarded by guide/supervisor based on log book : 10
b. Marks awarded by review committee : 10
c. Quality of Project report : 05

Review/progress monitoring committee may consider following points for assessment based on either one
year major project as mentioned in general guidelines.
One-year project:
 In semester VII entire theoretical solution shall be ready, including components/system selection and
cost analysis, building of working prototype. Two reviews will be conducted based on presentation
given by students group.
 First shall be for finalization of problem and proposed solution of the problem
 Second shall be on readiness of working and testing of prototype to be conducted.
 In semester VIII expected work shall be procurement of testing and validation of results based on
work completed in an odd semester.
 First review is based on improvements in testing and validation results cum demonstration
for publication to be conducted.
 Second review shall be based on paper presentation in conference/journal or copyright or
Indian patent in last month of the said semester.

Assessment criteria of Major Project.

Major Project shall be assessed based on following criteria;

1. Quality of survey/ need identification
2. Clarity of Problem definition based on need.
3. Innovativeness in solutions
4. Feasibility of proposed problem solutions and selection of best solution
 5. Cost effectiveness
6. Societal impact
7. Innovativeness
8. Cost effectiveness and Societal impact
9. Full functioning of working model as per stated requirements
10. Effective use of skill sets
11. Effective use of standard engineering norms
12. Contribution of an individual’s as member or leader
13. Clarity in written and oral communication

 In one year, project, first semester evaluation may be based on first six criteria’s and remaining
may be used for second semester evaluation of performance of students in mini project.
Guidelines for Assessment of Major Project Practical/Oral Examination:
 Report should be prepared as per the guidelines issued by the University of Mumbai.
 Major Project shall be assessed through a presentation and demonstration of working model by the student
project group to a panel of Internal and External Examiners preferably from industry or research
organizations having experience of more than five years approved by head of Institution.
 Students shall be motivated to publish a paper based on the work in Scopus Conferences/Journals or copy
right or Indian Patent.

Major Project shall be assessed based on following points;

1. Quality of problem and Clarity
2. Innovativeness in solutions
3. Cost effectiveness and Societal impact
4. Full functioning of working model as per stated requirements
5. Effective use of skill sets
6. Effective use of standard engineering norms
7. Contribution of an individual’s as member or leader
8. Clarity in written and oral communication
9. Publications in Sem VIII.
Course Code Course Name Theory Practical Tutorial Theory Practical Tutorial Total
/Oral
ITDO7013 Infrastructure 03 -- - 03 -- - 03
Security

Examination Scheme
Course Theory Marks
Course Name
Code Internal assessment End Term
Practical Oral Total
Avg. of 2 Sem. Work
Test1 Test 2
Tests Exam
ITDO7013 Infrastructure 20 20 20 80 - - - 100
Security

Course Objectives:

Sr. No. Course Objectives

The course aims:
1 To understand underlying principles of infrastructure security.
2 To explore software vulnerabilities, attacks and protection mechanisms to learn security aspects of wireless
network infrastructure and protocols.
3 To investigate web server vulnerabilities and their countermeasures.
4 To investigate cloud infrastructure vulnerabilities and their countermeasures.
5 To learn the different attacks on Open Web Applications and Web services.
6 To learn the different security policies.

Course Outcomes:

Sr. No. Course Outcomes Cognitive levels of

attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Understand the concept of vulnerabilities, attacks and protection mechanisms. L1,L2
2 Analyze and evaluate software vulnerabilities and attacks on databases and L1,L2,L3
operating systems.
3 Explain the need for security protocols in the context of wireless communication. L1,L2,L3
4 Understand and explain various security solutions for Cloud infrastructure. L1,L2
5 Understand, and evaluate different attacks on Open Web Applications and Web L1,L2
services.
6 Design appropriate security policies to protect infrastructure components. L1,L2,L3

Prerequisite: Computer Networks, Cryptography and Network Security

DETAILED SYLLABUS:

Sr. Module Detailed Content Hours CO

No. Mapping
0 Prerequisite Basic of OSI Model, Topology’s and Computer Networks, 02 --
 Cryptography and Network Security.

Cyber-attacks, Vulnerabilities, Defense Strategies and

Techniques, Authentication Methods- Password, Token
and Biometric, Access Control Policies and Models
(DAC,MAC, RBAC, ABAC, BIBA, Bell La
I Introduction 04 CO1
Padula),
Self-Learning Topics: Authentication and Access Control
Services- RADIUS, TACACS, and TACACS+

Software Vulnerabilities:

Buffer overflow, Format String, Cross-Site Scripting, SQL

Injection, Malware: Viruses, Worms, Trojans, Logic
Bomb, Bots, Rootkits

Operating System Security:

Software Memory and Address Protection, File

II Security Protection Mechanism, User Authentication. 08 CO2

Database Security:

Database Security Requirements, Reliability and Integrity,

Sensitive Data, Inference Attacks, Multilevel Database
Security
Self-Learning Topics: Format String, File System
Security (Windows and Linux OS)
Mobile Device Security- Security Threats,
Device Security, IEEE 802.11xWireless LAN Security,
III Wireless 06 CO3
Security VPN Security, Wireless Intrusion Detection System
(WIDS)
Self-Learning Topics: Wireshark, Cain and Abel,
Aircrack.
Cloud Security Risks and Countermeasures,
Data Protection in Cloud, Cloud Application Security,
IV Cloud 06 CO4
Cloud Identity and Access Management, Cloud Security as
Security
a Service.
Self-Learning Topics: Metasploit, Ettercap.
 Web Security Considerations, User
Authentication and Session Management, Cookies, SSL,
HTTPS, SSH, Privacy on Web, Web Browser Attacks,
Account Harvesting, Web Bugs, Clickjacking, Cross- Site
Request Forgery, Session Hijacking and Management,
V Web Security Phishing and Pharming Techniques, DNS Attacks, Web 08 CO5
Service Security, Secure Electronic Transaction, Email
Attacks, Web Server Security as per OWASP, Firewalls.
Self-Learning Topics: Penetration Testing tools: SQL
Map, Wapiti.

Information Security Policies, Business Continuity Plan,

Security and Risk Analysis, Incident Management, Legal
VI 05 CO6
Risk System and Cybercrime, Ethical Issues in
Management Security Management.
Self-Learning Topics: The Indian IT Act, Indian Cyber
Law

Text Books:

1. Computer Security Principles and Practice, William Stallings, Sixth Edition, Pearson Education
2. Security in Computing, Charles P. Pfleeger, Fifth Edition, Pearson Education
3. Network Security and Cryptography, Bernard Menezes, Cengage Learning
4. Network Security Bible, Eric Cole, Second Edition, Wiley

References Books:

1. Web Application Hackers Handbook by Wiley.

2. Computer Security, Dieter Gollman, Third Edition, Wiley
3. CCNA Security Study Guide, Tim Boyle, Wiley
4. Introduction to Computer Security, Matt Bishop, Pearson.
5. Cloud Security and Privacy, Tim Mather, Subra Kumaraswamy, Shahed Latif , O’Riely
6. Nina Godbole, Sunit Belapure, Cyber Security, Wiley India, New Delhi

Online References:
1. https://www.cousera.org
2. https://nptel.ac.in

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of syllabus
content must be covered in First IA Test and remaining 40% to 50% of syllabus content must be covered in
Second IA Test

 Question paper format

 Question Paper will comprise of a total of six questions each carrying 20 marks Q.1 will be compulsory
and should cover maximum contents of the syllabus

 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be from
different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be from any other
Module randomly selected from all the modules)

 A total of four questions need to be answered.

 Course Code Course Theory Practical Tutorial Theory Practical/ Tutorial Total
Name Oral
ITDO7024 Information 03 -- -- 03 -- -- 03
Retrieval
System

Examination Scheme
Course Theory Marks
Course Name
Code Internal assessment End Term
Practical Oral Total
Avg. of 2 Sem. Work
Test1 Test 2
Tests Exam
ITDO7024 Information
Retrieval 20 20 20 80 -- -- -- 100
System

Course Objectives:

Sr. No. Course Objectives

The course aims:
1 To learn the fundamentals of the information retrieval system.
2 To classify various Information retrieval models.
3 To demonstrate the query processing techniques and operations.
4 To compare the relevance of query languages for text and multimedia data.
5 To evaluate the significance of various indexing and searching techniques for information retrieval.
6 To develop an effective user interface for information retrieval.

Course Outcomes:

Sr. No. Course Outcomes Cognitive levels of

attainment as per
Bloom’s Taxonomy
On successful completion, of course, learner/student will be able to:
1 Define and describe the objectives of the basic concepts of the Information L1,L2
retrieval system.
2 Evaluate the taxonomy of different information retrieval models. L1,L2,L3,L4
3 Try to solve and process text and multimedia retrieval queries and their L1,L2
operations.
4 Evaluate text processing techniques and operations in the information retrieval L1,L2,L3,L4
system.
5 Demonstrate and evaluate various indexing and searching techniques. L1,L2,L3,L4
6 Design the user interface for an information retrieval system. L1,L2,L3,L4

Prerequisite: Data Structures

DETAILED SYLLABUS:

Sr. Module Detailed Content Hours CO

No. Mapping
 0 Prerequisite Indexing and searching Algorithms 02

I Introduction 06 CO1
Motivation, Basic Concepts, The Retrieval Process,
Information System: Components, parts and types on
information system; Definition and objectives on
information retrieval system, Information versus Data
Retrieval. Search Engines and browsers
Self-learning Topics: Search Engines , Search API
II IR Models Modeling: Taxonomy of Information Retrieval Models, 06 CO2
Retrieval: Formal Characteristics of IR models, Classic
Information Retrieval, Alternative Set Theoretic
models, Probabilistic Models, Structured text retrieval
Models, models for Browsing;

Self-learning Topics: Terrier

III Query Processing Query Languages: Keyword based Querying, Pattern 06 CO3
and Operations Matching, Structural Queries, Query Protocols; Query
Operations: User relevance feedback, Multimedia IR
models: Data Modeling

Self-learning Topics: Proximity Queries and

Wildcard Queries

IV Text Processing Text and Multimedia languages and properties: 06 CO4

Metadata, Markup Languages, Multimedia; Text
Operations: Document Preprocessing, Document
Clustering.

Self-learning Topics: Digital Library : Greenstone

V Indexing and Inverted files, Other indices for text, Boolean Queries, 07 CO5
Searching Sequential Searching, Pattern Matching, Structural
Queries, Compression; Multimedia IR: Indexing and
Searching:- A Generic Multimedia indexing approach, ,
Automatic Feature extraction; Searching Web:
Challenges, Characterizing the web, Search Engines.
Browsing, Meta searches, Searching using Hyperlinks.
Self-learning Topics: Koha
VI User interface and Human Computer interaction, the information access 06 CO6
visualization process, starting points, query specifications, context,
using relevance judgments, interface support for the
search process.

Self-learning Topics: SeeSoft

Text Books:

1. Modern Information Retrieval, Ricardo Baeza-Yates,berthier Ribeiro- Neto, ACM Press- Addison Wesley
2. Information Retrieval Systems: Theory and Implementation, Gerald Kowaski, Kluwer Academic Publisher
3. Storage Network Management and Retrieval by Dr. Vaishali Khairnar, Nilima Dongre, Wiley India.

References Books:
 1. Introduction to Information Retrieval By Christopher D. Manning and Prabhakar Raghavan, Cambridge University
Press.
2. Information Storage & Retrieval By Robert Korfhage – John Wiley & Sons
3. Introduction to Modern Information Retrieval. G.G. Chowdhury. NealSchuman.

Online References:

1. https://www.geeksforgeeks.org/what-is-information-retrieval/
2. https://nlp.stanford.edu/IR-book/
3. https://en.wikipedia.org/wiki/Information_retrieval
4. https://people.ischool.berkeley.edu/~hearst/irbook/10/node1.html

Assessment:
Internal Assessment (IA) for 20 marks:
 IA will consist of Two Compulsory Internal Assessment Tests. Approximately 40% to 50% of syllabus
content must be covered in First IA Test and remaining 40% to 50% of syllabus content must be covered in
Second IA Test

 Question paper format

 Question Paper will comprise of a total of six questions each carrying 20 marks Q.1 will be compulsory
and should cover maximum contents of the syllabus

 Remaining questions will be mixed in nature (part (a) and part (b) of each question must be from
different modules. For example, if Q.2 has part (a) from Module 3 then part (b) must be from any other
Module randomly selected from all the modules)

 A total of four questions need to be answered.

 Course Code Course Name Credits

ILO7011 Product Life Cycle Management 03

Course Objectives: Students will try :

1. To familiarize the students with the need, benefits and components of PLM
2. To acquaint students with Product Data Management & PLM strategies
3. To give insights into new product development program and guidelines for designing and
developing a product
4. To familiarize the students with Virtual Product Development

Course Outcomes: Students will be able to :

1. Gain knowledge about phases of PLM, PLM strategies and methodology for PLM feasibility
study and PDM implementation.
2. Illustrate various approaches and techniques for designing and developing products.
3. Apply product engineering guidelines / thumb rules in designing products for moulding,
machining, sheet metal working etc.
4. Acquire knowledge in applying virtual product development tools for components, machining
and manufacturing plant

Module Detailed Contents Hrs

Introduction to Product Lifecycle Management (PLM):Product Lifecycle 10

Management (PLM), Need for PLM, Product Lifecycle Phases, Opportunities of
Globalization, Pre-PLM Environment, PLM Paradigm, Importance & Benefits of PLM,
Widespread Impact of PLM, Focus and Application, A PLM Project, Starting the PLM
01
Initiative, PLM Applications
PLM Strategies:Industrial strategies, Strategy elements, its identification, selection and
implementation, Developing PLM Vision and PLM Strategy ,
Change management for PLM
ProductDesign:Product Design and Development Process, Engineering Design, 09
Organization and Decomposition in Product Design, Typologies of Design Process
Models, Reference Model, Product Design in the Context of the Product Development
Process, Relation with the Development Process Planning Phase, Relation with the Post
design Planning Phase, Methodological Evolution in Product Design, Concurrent
02 Engineering, Characteristic Features of Concurrent Engineering, Concurrent Engineering
and Life Cycle Approach, New Product Development (NPD) and Strategies, Product
Configuration and Variant Management, The Design for X System, Objective Properties
and Design for X
Tools, Choice of Design for X Tools and Their Use in the Design Process

Product Data Management (PDM):Product and Product Data, PDM systems and 05
03 importance, Components of PDM, Reason for implementing a PDM system,
financial justification of PDM, barriers to PDM implementation
04 Virtual Product Development Tools:For components, machines, and 05
manufacturing plants, 3D CAD systems and realistic rendering techniques,
 Digital mock-up, Model building, Model analysis, Modeling and simulations in Product
Design, Examples/Case studies
Integration of Environmental Aspects in Product Design:Sustainable 05
Development, Design for Environment,Need for Life Cycle Environmental Strategies,
05 Useful Life Extension Strategies, End-of-Life Strategies, Introduction of Environmental
Strategies into the Design Process, Life Cycle Environmental Strategies and
Considerations for Product Design
Life Cycle Assessment and Life Cycle Cost Analysis:Properties, and 05
Framework of Life Cycle Assessment, Phases of LCA in ISO Standards, Fields of
06 Application and Limitations of Life Cycle Assessment, Cost Analysis and the Life Cycle
Approach, General Framework for LCCA, Evolution of Models for Product Life Cycle
Cost Analysis

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper.Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. John Stark, “Product Lifecycle Management: Paradigm for 21st Century Product Realisation”, Springer-
Verlag, 2004. ISBN: 1852338105
2. Fabio Giudice, Guido La Rosa, AntoninoRisitano, “Product Design for the environment-A life cycle
approach”, Taylor & Francis 2006, ISBN: 0849327229
3. SaaksvuoriAntti, ImmonenAnselmie, “Product Life Cycle Management”, Springer, Dreamtech, ISBN:
3540257314
4. Michael Grieve, “Product Lifecycle Management: Driving the next generation of lean thinking”, Tata
McGraw Hill, 2006, ISBN: 0070636265
 Course Code Course Name Credits

ILO7012 Reliability Engineering 03

Objectives:
1. To familiarize the students with various aspects of probability theory
2. To acquaint the students with reliability and its concepts
3. To introduce the students to methods of estimating the system reliability of simple and complex systems
4. To understand the various aspects of Maintainability, Availability and FMEA procedure

Outcomes: Learner will be able to…

1. Understand and apply the concept of Probability to engineering problems
2. Apply various reliability concepts to calculate different reliability parameters
3. Estimate the system reliability of simple and complex systems
4. Carry out a Failure Mode Effect and Criticality Analysis

Module Detailed Contents Hrs

Probability theory: Probability: Standard definitions and concepts; Conditional

Probability, Baye’s Theorem.
01 Probability Distributions: Central tendency and Dispersion; Binomial, Normal, 08
Poisson, Weibull, Exponential, relations between them and their significance.
Measures of Dispersion: Mean, Median, Mode, Range, Mean Deviation,
Standard Deviation, Variance, Skewness and Kurtosis.
Reliability Concepts: Reliability definitions, Importance of Reliability, Quality
Assurance and Reliability, Bath Tub Curve.
Failure Data Analysis: Hazard rate, failure density, Failure Rate, Mean Time To 08
02 Failure (MTTF), MTBF, Reliability Functions.
Reliability Hazard Models: Constant Failure Rate, Linearly increasing, Time
Dependent Failure Rate, Weibull Model. Distribution functions and reliability analysis.

03 System Reliability: System Configurations: Series, parallel, mixed 05

configuration, k out of n structure, Complex systems.
Reliability Improvement: Redundancy Techniques: Element redundancy, Unit
04 redundancy, Standby redundancies. Markov analysis. 08
System Reliability Analysis – Enumeration method, Cut-set method, Success
Path method, Decomposition method.
Maintainability and Availability: System downtime, Design for Maintainability:
Maintenance requirements, Design methods: Fault Isolation and self-diagnostics, Parts 05
05 standardization and Interchangeability, Modularization and Accessibility, Repair Vs
Replacement.
Availability – qualitative aspects.
Failure Mode, Effects and Criticality Analysis: Failure mode effects analysis,
06 severity/criticality analysis, FMECA examples. Fault tree construction, basic symbols, 05
development of functional reliability block diagram, Fau1t tree
analysis and Event tree Analysis

Assessment:
Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. L.S. Srinath, “Reliability Engineering”, Affiliated East-Wast Press (P) Ltd., 1985.
2. Charles E. Ebeling, “Reliability and Maintainability Engineering”, Tata McGraw Hill.
3. B.S. Dhillion, C. Singh, “Engineering Reliability”, John Wiley & Sons, 1980.
4. P.D.T. Conor, “Practical Reliability Engg.”, John Wiley & Sons, 1985.
5. K.C. Kapur, L.R. Lamberson, “Reliability in Engineering Design”, John Wiley & Sons.
6. Murray R. Spiegel, “Probability and Statistics”, Tata McGraw-Hill Publishing Co. Ltd.
 Course Code Course Name Credits

ILO7013 Management Information System 03

Objectives:
1. The course is blend of Management and Technical field.
2. Discuss the roles played by information technology in today’s business and define various
technology architectures on which information systems are built
3. Define and analyze typical functional information systems and identify how they meet the needs
of the firm to deliver efficiency and competitive advantage
4. Identify the basic steps in systems development

Outcomes: Learner will be able to…

1. Explain how information systems Transform Business
2. Identify the impact information systems have on an organization
3. Describe IT infrastructure and its components and its current trends
4. Understand the principal tools and technologies for accessing information from databases to improve
business performance and decision making
5. Identify the types of systems used for enterprise-wide knowledge management and how they provide
value for businesses

Module Detailed Contents Hrs

Introduction To Information Systems (IS): Computer Based Information Systems,

01 Impact of IT on organizations, Imporance of IS to Society. 4
Organizational Strategy, Competitive Advantages and IS.
Data and Knowledge Management: Database Approach, Big Data, Data warehouse and
02 Data Marts, Knowledge Management. 7
Business intelligence (BI): Managers and Decision Making, BI for Data analysis
and Presenting Results
03 Ethical issues and Privacy: Information Security. Threat to IS, and Security 7
Controls
Social Computing (SC): Web 2.0 and 3.0, SC in business-shopping, Marketing,
04 Operational and Analytic CRM, E-business and E-commerce – B2B B2C. 7
Mobile commerce.
05 Computer Networks Wired and Wireless technology, Pervasive computing, 6
Cloud computing model.
Information System within Organization: Transaction Processing Systems, Functional
06 Area Information System, ERP and ERP support of Business Process. Acquiring 8
Information Systems and Applications: Various System development
life cycle models.

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.
End Semester Theory Examination:
Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Kelly Rainer, Brad Prince,Management Information Systems, Wiley

2. K.C. Laudon and J.P. Laudon, Management Information Systems: Managing the Digital Firm, 10th
Ed., Prentice Hall, 2007.
3. D. Boddy, A. Boonstra, Managing Information Systems: Strategy and Organization, Prentice Hall,
2008
 Course Code Course Name Credits

ILO7014 Design of Experiments 03

Objectives:
1. To understand the issues and principles of Design of Experiments (DOE)
2. To list the guidelines for designing experiments
3. To become familiar with methodologies that can be used in conjunction with experimental designs for
robustness and optimization

Outcomes: Learner will be able to…

1. Plan data collection, to turn data into information and to make decisions that lead to appropriate action
2. Apply the methods taught to real life situations
3. Plan, analyze, and interpret the results of experiments

Module Detailed Contents Hrs

Introduction
Strategy of Experimentation
01 Typical Applications of Experimental Design 06
Guidelines for Designing Experiments
Response Surface Methodology
Fitting Regression Models
Linear Regression Models
Estimation of the Parameters in Linear Regression Models
Hypothesis Testing in Multiple Regression 08
02
Confidence Intervals in Multiple Regression
Prediction of new response observation
Regression model diagnostics
Testing for lack of fit

Two-Level Factorial Designs

The 22 Design
The 23 Design
The General2k Design 07
03
A Single Replicate of the 2k Design
The Addition of Center Points to the 2k Design,
Blocking in the 2k Factorial Design
Split-Plot Designs

Two-Level Fractional Factorial Designs

The One-Half Fraction of the 2k Design
The One-Quarter Fraction of the 2k Design
04 The General 2k-p Fractional Factorial Design 07
Resolution III Designs
Resolution IV and V Designs
Fractional Factorial Split-Plot Designs
 Response Surface Methods and Designs
Introduction to Response Surface Methodology
05 The Method of Steepest Ascent 07
Analysis of a Second-Order Response Surface
Experimental Designs for Fitting Response Surfaces
Taguchi Approach
06 Crossed Array Designs and Signal-to-Noise Ratios 04
Analysis Methods
Robust design examples

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Raymond H. Mayers, Douglas C. Montgomery, Christine M. Anderson-Cook, Response Surface

Methodology: Process and Product Optimization using Designed Experiment, 3 rd edition,John
Wiley & Sons, New York, 2001
2. D.C. Montgomery, Design and Analysis of Experiments, 5th edition, John Wiley & Sons, New York,
2001
3. George E P Box, J Stuart Hunter, William G Hunter, Statics for Experimenters: Design,
Innovation and Discovery, 2nd Ed. Wiley
4. W J Dimond, Peactical Experiment Designs for Engineers and Scintists, John Wiley and Sons Inc.
ISBN: 0-471-39054-2
5. Design and Analysis of Experiments (Springer text in Statistics), Springer by A.M. Dean, and
D. T.Voss
 Course Code Course Name Credits

ILO7015 Operations Research 03

Objectives:
1. Formulate a real-world problem as a mathematical programming model.
2. Understand the mathematical tools that are needed to solve optimization problems.
3. Use mathematical software to solve the proposed models.

Outcomes: Learner will be able to…

1. Understand the theoretical workings of the simplex method, the relationship between a linear program
and its dual, including strong duality and complementary slackness.
2. Perform sensitivity analysis to determine the direction and magnitude of change of a model’s optimal
solution as the data change.
3. Solve specialized linear programming problems like the transportation and assignment problems, solve
network models like the shortest path, minimum spanning tree, and maximum flow problems.
4. Understand the applications of integer programming and a queuing model and compute important
performance measures

Module Detailed Contents Hrs

Introduction to Operations Research: Introduction, , Structure of the

Mathematical Model, Limitations of Operations Research
Linear Programming: Introduction, Linear Programming Problem,
Requirements of LPP, Mathematical Formulation of LPP, Graphical method,
Simplex Method Penalty Cost Method or Big M-method, Two Phase Method, Revised
simplex method, Duality, Primal – Dual construction, Symmetric and Asymmetric
Dual, Weak Duality Theorem, Complimentary Slackness Theorem, Main Duality
Theorem, Dual Simplex Method, Sensitivity Analysis Transportation Problem:
Formulation, solution, unbalanced Transportation problem. Finding basic feasible
solutions – Northwest corner rule, least cost method and Vogel’s approximation
01 14
method. Optimality test: the stepping stone method and MODI method.
Assignment Problem: Introduction, Mathematical Formulation of the Problem,
Hungarian Method Algorithm, Processing of n Jobs Through Two Machines and m
Machines, Graphical Method of Two Jobs m Machines Problem Routing Problem,
Travelling Salesman Problem
Integer Programming Problem: Introduction, Types of Integer Programming Problems,
Gomory’s cutting plane Algorithm, Branch and Bound Technique.
Introduction to Decomposition algorithms.

Queuing models: queuing systems and structures, single server and multi-server
02 models, Poisson input, exponential service, constant rate service, finite and infinite 05
population
03 Simulation: Introduction, Methodology of Simulation, Basic Concepts, 05

University of Mumbai, B. E. (Information Technology), Rev 2016 220

 Simulation Procedure, Application of Simulation Monte-Carlo Method:
Introduction, Monte-Carlo Simulation, Applications of Simulation, Advantages of
Simulation, Limitations of Simulation
Dynamic programming. Characteristics of dynamic programming. Dynamic
programming approach for Priority Management employment smoothening,
04 05
capital budgeting, Stage Coach/Shortest Path, cargo loading and Reliability problems.

Game Theory. Competitive games, rectangular game, saddle point, minimax

(maximin) method of optimal strategies, value of the game. Solution of games with saddle 05
05
points, dominance principle. Rectangular games without saddle point – mixed strategy
for 2 X 2 games.
Inventory Models: Classical EOQ Models, EOQ Model with Price Breaks, 05
06
EOQ with Shortage, Probabilistic EOQ Model,

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Taha, H.A. "Operations Research - An Introduction", Prentice Hall, (7th Edition), 2002.
2. Ravindran, A, Phillips, D. T and Solberg, J. J. "Operations Research: Principles and Practice", John
Willey and Sons, 2nd Edition, 2009.
3. Hiller, F. S. and Liebermann, G. J. "Introduction to Operations Research", Tata McGraw Hill, 2002.
4. Operations Research, S. D. Sharma, KedarNath Ram Nath-Meerut.
5. Operations Research, KantiSwarup, P. K. Gupta and Man Mohan, Sultan Chand & Sons.

University of Mumbai, B. E. (Information Technology), Rev 2016 221

 Course Code Course Name Credits

ILO7016 Cyber Security and Laws 03

Objectives:
1. To understand and identify different types cybercrime and cyber law
2. To recognized Indian IT Act 2008 and its latest amendments
3. To learn various types of security standards compliances

Outcomes: Learner will be able to…

1. Understand the concept of cybercrime and its effect on outside world
2. Interpret and apply IT law in various legal issues
3. Distinguish different aspects of cyber law
4. Apply Information Security Standards compliance during software design and development

Module Detailed Contents Hrs

Introduction to Cybercrime: Cybercrime definition and origins of the world,

01 Cybercrime and information security, Classifications of cybercrime, Cybercrime and the 4
Indian ITA 2000, A global Perspective on cybercrimes.

Cyber offenses & Cybercrime: How criminal plan the attacks, Social Engg, Cyber
stalking, Cyber café and Cybercrimes, Bot nets, Attack vector, Cloud computing,
Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit Card Frauds in
Mobile and Wireless Computing Era, Security Challenges Posed by Mobile Devices,
02 Registry Settings for Mobile Devices, Authentication Service Security, Attacks on 9
Mobile/Cell Phones, Mobile Devices: Security Implications for Organizations,
Organizational Measures for Handling Mobile, Devices-Related Security Issues,
Organizational Security Policies and Measures in Mobile
Computing Era, Laptops

Tools and Methods Used in Cyber line

03 6
Phishing, Password Cracking, Key loggers and Spywares, Virus and Worms,
Steganography, DoS and DDoS Attacks, SQL Injection, Buffer Over Flow, Attacks on
Wireless Networks, Phishing, Identity Theft (ID Theft)
The Concept of Cyberspace
E-Commerce , The Contract Aspects in Cyber Law ,The Security Aspect of Cyber
Law ,The Intellectual Property Aspect in Cyber Law
04 , The Evidence Aspect in Cyber Law , The Criminal Aspect in Cyber Law, Global 8
Trends in Cyber Law , Legal Framework for Electronic Data
Interchange Law Relating to Electronic Banking , The Need for an Indian Cyber
Law
Indian IT Act.
05 Cyber Crime and Criminal Justice : Penalties, Adjudication and Appeals Under the IT 6
Act, 2000, IT Act. 2008 and its Amendments
Information Security Standard compliances 6
06
SOX, GLBA, HIPAA, ISO, FISMA, NERC, PCI.

University of Mumbai, B. E. (Information Technology), Rev 2016 222

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination.

In question paper weightage of each module will be proportional to number of respective lecture hours as
mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Nina Godbole, Sunit Belapure, Cyber Security, Wiley India, New Delhi
2. The Indian Cyber Law by Suresh T. Vishwanathan; Bharat Law House New Delhi
3. The Information technology Act, 2000; Bare Act- Professional Book Publishers, New Delhi.
4. Cyber Law & Cyber Crimes By Advocate Prashant Mali; Snow White Publications, Mumbai
5. Nina Godbole, Information Systems Security, Wiley India, New Delhi
6. Kennetch J. Knapp, Cyber Security &Global Information Assurance Information Science Publishing.
7. William Stallings, Cryptography and Network Security, Pearson Publication
8. Websites for more information is available on : The Information Technology ACT, 2008-
TIFR : https://www.tifrh.res.in
9. Website for more information , A Compliance Primer for IT professional :
https://www.sans.org/reading-room/whitepapers/compliance/compliance-primer-professionals- 33538

University of Mumbai, B. E. (Information Technology), Rev 2016 223

 Course Code Course Name Credits

ILO7017 Disaster Management and Mitigation Measures 03

Objectives:

1. To understand physics and various types of disaster occurring around the world
2. To identify extent and damaging capacity of a disaster
3. To study and understand the means of losses and methods to overcome /minimize it.
4. To understand role of individual and various organization during and after disaster
5. To understand application of GIS in the field of disaster management
6. To understand the emergency government response structures before, during and after
disaster

Outcomes: Learner will be able to…

1. Get to know natural as well as manmade disaster and their extent and possible effects on the
economy.
2. Plan of national importance structures based upon the previous history.
3. Get acquainted with government policies, acts and various organizational structure
associated with an emergency.
4. Get to know the simple do’s and don’ts in such extreme events and act accordingly.

Module Detailed Contents Hrs

Introduction
1.1 Definition of Disaster, hazard, global and Indian scenario, general perspective,
01 importance of study in human life, Direct and indirect effects of disasters, long term 03
effects of disasters. Introduction to global warming and
climate change.
Natural Disaster and Manmade disasters:
Natural Disaster: Meaning and nature of natural disaster, Flood, Flash flood, drought,
cloud burst, Earthquake, Landslides, Avalanches, Volcanic eruptions, Mudflow,
Cyclone, Storm, Storm Surge, climate change, global warming, sea level rise, ozone
02 depletion 09
Manmade Disasters: Chemical, Industrial, Nuclear and Fire Hazards. Role of growing
population and subsequent industrialization, urbanization and changing lifestyle of
human beings in frequent occurrences of manmade
disasters.

Disaster Management, Policy and Administration

Disaster management: meaning, concept, importance, objective of disaster
management policy, disaster risks in India, Paradigm shift in disaster management.
Policy and administration:
03 Importance and principles of disaster management policies, command and co- 06
ordination of in disaster management, rescue operations-how to start with
and how to proceed in due course of time, study of flowchart showing the entire
process.

Institutional Framework for Disaster Management in India:

4.1 Importance of public awareness, Preparation and execution of emergency
04 management programme.Scope and responsibilities of National Institute of Disaster 06
Management (NIDM) and National disaster management authority
(NDMA) in India.Methods and measures to avoid disasters, Management of

University of Mumbai, B. E. (Information Technology), Rev 2016 224

 casualties, set up of emergency facilities, importance of effective
communication amongst different agencies in such situations.
4.2 Use of Internet and softwares for effective disaster management.
Applications of GIS, Remote sensing and GPS in this regard.
Financing Relief Measures:
Ways to raise finance for relief expenditure, role of government agencies and NGO’s in
this process, Legal aspects related to finance raising as well as overall management of
05 disasters. Various NGO’s and the works they have carried out in the past on the occurrence 09
of various disasters, Ways to approach these teams.
International relief aid agencies and their role in extreme events.

Preventive and Mitigation Measures:

Pre-disaster, during disaster and post-disaster measures in some events in general
Structural mapping: Risk mapping, assessment and analysis, sea walls and embankments,
Bio shield, shelters, early warning and communication
06 Non Structural Mitigation: Community based disaster preparedness, risk transfer and 06
risk financing, capacity development and training, awareness and education, contingency
plans.
Do’s and don’ts in case of disasters and effective implementation of relief
aids.

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. ‘Disaster Management’ by Harsh K.Gupta, Universities Press Publications.

2. ‘Disaster Management: An Appraisal of Institutional Mechanisms in India’ by O.S.Dagur, published by
Centre for land warfare studies, New Delhi, 2011.
3. ‘Introduction to International Disaster Management’ by Damon Copolla, Butterworth Heinemann
Elseveir Publications.
4. ‘Disaster Management Handbook’ by Jack Pinkowski, CRC Press Taylor and Francis group.
5. ‘Disaster management & rehabilitation’ by Rajdeep Dasgupta, Mittal Publications, New Delhi.
6. ‘Natural Hazards and Disaster Management, Vulnerability and Mitigation – R B Singh, Rawat
Publications
7. Concepts and Techniques of GIS –C.P.Lo Albert, K.W. Yonng – Prentice Hall (India) Publications.
(Learners are expected to refer reports published at national and International level and updated
information available on authentic web sites)

University of Mumbai, B. E. (Information Technology), Rev 2016 225

 Course Code Course Name Credits

ILO7018 Energy Audit and Management 03

Objectives:

1. To understand the importance energy security for sustainable development and the
fundamentals of energy conservation.
2. To introduce performance evaluation criteria of various electrical and thermal installations to
facilitate the energy management
3. To relate the data collected during performance evaluation of systems for identification of energy
saving opportunities.

Outcomes: Learner will be able to…

1. To identify and describe present state of energy security and its importance.
2. To identify and describe the basic principles and methodologies adopted in energy audit of an utility.
3. To describe the energy performance evaluation of some common electrical installations and identify
the energy saving opportunities.
4. To describe the energy performance evaluation of some common thermal installations and identify
the energy saving opportunities
5. To analyze the data collected during performance evaluation and recommend energy saving
measures

Module Detailed Contents Hrs

Energy Scenario:
Present Energy Scenario, Energy Pricing, Energy Sector Reforms, Energy Security,
01 Energy Conservation and its Importance, Energy Conservation Act- 2001 and its Features. 04
Basics of Energy and its various forms, Material and
Energy balance
Energy Audit Principles:
Definition, Energy audit- need, Types of energy audit, Energy management (audit)
approach-understanding energy costs, Bench marking, Energy performance, Matching
energy use to requirement, Maximizing system efficiencies, Optimizing the input energy
02 requirements, Fuel and energy substitution. Elements of monitoring& targeting; Energy 08
audit Instruments; Data and information-analysis.
Financial analysis techniques: Simple payback period, NPV, Return on investment (ROI),
Internal rate of return (IRR)

Energy Management and Energy Conservation in Electrical System: Electricity

billing, Electrical load management and maximum demand Control; Power factor
improvement, Energy efficient equipments and appliances, star ratings.
Energy efficiency measures in lighting system, Lighting control: Occupancy 10
03 sensors, daylight integration, and use of intelligent controllers.
Energy conservation opportunities in: water pumps, industrial drives, induction motors,
motor retrofitting, soft starters, variable speed drives.

University of Mumbai, B. E. (Information Technology), Rev 2016 226

 Energy Management and Energy Conservation in Thermal Systems:
Review of different thermal loads; Energy conservation opportunities in: Steam
distribution system, Assessment of steam distribution losses, Steam leakages, Steam
04 trapping, Condensate and flash steam recovery system. 10
General fuel economy measures in Boilers and furnaces, Waste heat recovery, use of
insulation- types and application. HVAC system: Coefficient of performance, Capacity,
factors affecting Refrigeration and Air Conditioning system performance and savings
opportunities.
Energy Performance Assessment:
05 On site Performance evaluation techniques, Case studies based on: Motors and variable 04
speed drive, pumps, HVAC system calculations; Lighting System:
Installed Load Efficacy Ratio (ILER) method, Financial Analysis.
Energy conservation in Buildings:
06 03
Energy Conservation Building Codes (ECBC): Green Building, LEED rating,
Application of Non-Conventional and Renewable Energy Sources

Assessment:

Internal:
Assessment consists of two tests out of which; one should be compulsory class test and the other is either a
class test or assignment on live problems or course project.

End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question papers
of end semester examination. In question paper weightage of each module will be proportional to number of
respective lecture hours as mention in the syllabus.

1. Question paper will comprise of total six question

2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then part (b)
will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Handbook of Electrical Installation Practice, Geofry Stokes, Blackwell Science

2. Designing with light: Lighting Handbook, By Anil Valia, Lighting System
3. Energy Management Handbook, By W.C. Turner, John Wiley and Sons
4. Handbook on Energy Audits and Management, edited by A. K. Tyagi, Tata Energy
Research Institute (TERI).
5. Energy Management Principles, C.B.Smith, Pergamon Press
6. Energy Conservation Guidebook, Dale R. Patrick, S. Fardo, Ray E. Richardson, Fairmont Press
7. Handbook of Energy Audits, Albert Thumann, W. J. Younger, T. Niehus, CRC Press
8. www.energymanagertraining.com
9. www.bee-india.nic.in

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Course Code Course Name Credits

ILO7019 Development Engineering 03

Objectives:

1. To familiarise the characteristics of rural Society and the Scope, Nature and Constraints of rural
Development
2. To provide an exposure toimplications of 73rdCAA on Planning, Development and Governance of Rural
Areas
3. An exploration of human values, which go into making a ‘good’ human being, a ‘good’ professional, a
‘good’ society and a ‘good life’ in the context of work life and the personal life of modern Indian
professionals
4. To familiarise the Nature and Type of Human Values relevant to Planning Institutions

Outcomes: Learner will be able to…

1. Demonstrateunderstanding of knowledge for Rural Development.

2. Prepare solutions for Management Issues.
3. Take up Initiatives and design Strategies to complete the task
4. Develop acumen for higher education and research.
5. Demonstrate the art of working in group of different nature
6. Develop confidence to take up rural project activities independently

Module Contents Hrs

1 Introduction to Rural Development Meaning, nature and scope of development; Nature of 08

rural society in India; Hierarchy of settlements; Social, economic and ecological constraints
for rural development

Roots of Rural Development in India Rural reconstruction and Sarvodaya programme

before independence; Impact of voluntary effort and Sarvodaya Movement on rural
development; Constitutional direction, directive principles; Panchayati Raj - beginning of
planning and community development; National extension services.

2 Post-Independence rural Development Balwant Rai Mehta Committee - three tier system 06
of rural local Government; Need and scope for people’s participation and Panchayati Raj;
Ashok Mehta Committee - linkage between Panchayati Raj, participation and rural
development.

3 Rural Development Initiatives in Five Year Plans Five Year Plans and Rural Development; 07
Planning process at National, State, Regional and District levels; Planning, development,
implementing and monitoring organizations and agencies; Urban and rural interface -
integrated approach and local plans; Development initiatives and their convergence; Special
component plan and sub-plan for the weaker section; Micro-eco zones; Data base for local
planning; Need for decentralized planning; Sustainable rural development

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 4 Post 73rd Amendment Scenario 73rd Constitution Amendment Act, including - XI 04
schedule, devolution of powers, functions and finance; Panchayati Raj institutions
- organizational linkages; Recent changes in rural local planning; Gram Sabha - revitalized
Panchayati Raj; Institutionalization; resource mapping, resource mobilization including
social mobilization; Information Technology and rural planning; Need for further
amendments.

5 Values and Science and Technology Material development and its values; the 10
challenge of science and technology; Values in planning profession, research and education

Types of Values Psychological values — integrated personality; mental health; Societal

values — the modern search for a good society; justice, democracy, rule of law, values in
the Indian constitution; Aesthetic values — perception and enjoyment of beauty; Moral and
ethical values; nature of moral judgment; Spiritual values; different concepts; secular
spirituality; Relative and absolute values; Human values— humanism and human values;
human rights; human values as freedom, creativity, love and wisdom

6 Ethics Canons of ethics; ethics of virtue; ethics of duty; ethics of responsibility; 04

Work ethics; Professional ethics; Ethics in planning profession, research and education

Assessment:

Internal Assessment for 20 marks:

Consisting Two Compulsory Class Tests
First test based onapproximately 40% of contents and second test based on remainingcontents (approximately
40% but excluding contents covered in Test I)

End Semester Examination:

Weightage of each module in end semester examination will be proportional to number of respective lecture
hours mentioned in the curriculum.
1. Question paper will comprise of total six questions, each carrying 20 marks
2. Question 1 will be compulsory and should cover maximum contents of the curriculum
3. Remaining questions will be mixed in nature (for example if Q.2 has part (a) from module 3 then part
(b) will be from any module other than module 3)
4. Only Four questions need to be solved

Reference
1. ITPI, Village Planning and Rural Development, ITPI, New Delhi
2. Thooyavan, K.R. Human Settlements: A 2005 MA Publication, Chennai
3. GoI, Constitution (73rdGoI, New Delhi Amendment) Act, GoI, New Delhi
4. Planning Commission, Five Year Plans, Planning Commission
5. Planning Commission, Manual of Integrated District Planning, 2006, Planning Commission New Delhi
6. Planning Guide to Beginners
7. Weaver, R.C., The Urban Complex, Doubleday
8. Farmer, W.P. et al, Ethics in Planning, American Planning Association, Washington

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9. How, E., Normative Ethics in Planning, Journal of Planning Literature, Vol.5, No.2, pp. 123-150
10. Watson, V. Conflicting Rationalities: -- Implications for Planning Theory and Ethics, Planning Theory and
Practice, Vol. 4, No.4, pp.395 – 407

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