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Artificial Intelligence and Machine
Learning for EDGE Computing
This page intentionally left blank
Artificial Intelligence
and Machine Learning
for EDGE Computing

Edited by
Rajiv Pandey
Amity University, Lucknow, India

Sunil Kumar Khatri

Amity University, Tashkent, Uzbekistan

Neeraj Kumar Singh

Department of Computing and Applied Mathematics, INPT-ENSEEIHT / IRIT, Toulouse, France

Parul Verma
Amity University, Lucknow, India
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Typeset by STRAIVE, India

Contents

Contributors xv 2 What are regression and classification

Preface xix problems? 23
3 Learning algorithms 25
3.1 Linear regression 25
3.2 Logistic regression 25
Part I 3.3 Decision tree 26
4 Evaluation metrics 26
AI and machine learning 4.1 Mean square error 27
4.2 Root mean square error 27
1. Supervised learning
4.3 Confusion matrix 27
Kanishka Tyagi, Chinmay Rane, and 4.4 Accuracy 28
Michael Manry 4.5 Recall 28
1 Introduction 3 4.6 Precision 28
2 Perceptron 3 4.7 F1 score 28
3 Linear regression 4 5 Supervised learning to detect fraudulent
3.1 Training a linear regression 5 credit card transactions 29
3.2 Steepest descent 6 5.1 Data exploration 29
3.3 Conjugate gradient 6 5.2 Data preprocessing 30
4 Logistic regression 8 5.3 Fitting and evaluation 30
4.1 Softmax classifier 9 6 Supervised learning for hand writing
5 Multilayer perceptron 9 recognition 30
5.1 Structure and notation 10 7 Conclusion 32
5.2 Initialization 11 References 32
5.3 First-order learning algorithms 11
5.4 Second-order learning 3. Unsupervised learning
algorithms 14
6 KL divergence 15 Kanishka Tyagi, Chinmay Rane,
Raghavendra Sriram, and Michael Manry
7 Generalized linear models 16
8 Kernel method 17 1 Introduction 33
9 Nonlinear SVM classifier 17 2 k-means clustering 33
10 Tree ensembles 17 3 k-means++ clustering 35
10.1 Decision trees 17 4 Sequential leader clustering 36
10.2 Random forest 18 5 EM algorithm 37
10.3 Boosting 20 6 Gaussian mixture model 38
References 21 7 Autoencoders 39
7.1 AEs: Structure, notations, and
2. Supervised learning: From theory training 39
to applications 7.2 Variants of AEs 41
8 Principal component analysis 41
Ashish Tiwari
8.1 Generic PCA derivation 42
1 Introduction 23 8.2 Advantages of PCA 44
1.1 Supervised learning 23 8.3 Assumptions behind PCA 45
1.2 Unsupervised learning 23 8.4 Comments 45

v
vi Contents

9 Linear discriminant analysis 47 5.4 PC3 dataset 72

9.1 Algorithm 47 5.5 KC1 dataset 72
9.2 Derivation of LDA algorithm 47 6 Threats to validity 72
9.3 PCA vs. LDA 48 6.1 Threats to internal validity 72
9.4 Comments and programmers 6.2 Threats to external validity 74
perspective 48 7 Conclusions 74
10 Independent component analysis 49 References 74
10.1 Limitations of ICA 49
10.2 Assumptions 51
References 51 6. Learning in sequential
decision-making under
4. Regression analysis uncertainty
Kanishka Tyagi, Chinmay Rane, Manu K. Gupta, Nandyala Hemachandra,
Harshvardhan, and Michael Manry and Shobhit Bhatnagar
1 Introduction 53 1 Introduction 75
2 Linear regression 55 2 Multiarmed bandit problem 76
3 Cost functions 56 2.1 Applications 76
3.1 MSE 56 2.2 Algorithms for multiarmed bandit
3.2 MSLE 56 problem 76
3.3 RMSE 56 2.3 Nonstationary environment 78
3.4 MAE 56 3 Markov decision process planning
4 Gradient descent 57 problem 79
5 Polynomial regression 58 3.1 Multiarmed bandits and MDP
6 Regularization 60 planning problem 79
6.1 Ridge regression 60 4 Reinforcement learning 80
6.2 Lasso regression 60 4.1 RL and MDP planning problem 80
6.3 Dropout 60 4.2 Model-free RL algorithms 82
6.4 Early stopping 60 4.3 Model-based RL algorithms 82
7 Evaluating a machine learning model 61 4.4 RL in nonstationary environment 83
7.1 Bias-variance trade-off 62 5 Summary 84
7.2 R-squared 63 Acknowledgments 84
7.3 Adjusted R-squared 63 References 85
References 63

5. The integrity of machine learning 7. Geospatial crime analysis and

algorithms against software defect forecasting with machine
prediction learning techniques
Param Khakhar and Rahul Kumar Dubey Boppuru Rudra Prathap

1 Introduction 65 1 Introduction 87
2 Related works 66 2 Related work 87
3 Proposed method 66 2.1 Motivation and objective of the
3.1 Overview 66 research 89
3.2 KMFOS 67 2.2 Literature-based problem
3.3 Dataset 68 identification 90
4 Experiment 69 2.3 List of crime keywords considered 90
4.1 Design 69 3 Methodology 90
4.2 Evaluation metrics 69 3.1 Implementation of the process 91
5 Results 69 3.2 Proposed analytic approach 91
5.1 Hyperparameters 69 4 Results and discussion 92
5.2 Individual algorithms 70 4.1 India: Crime visualization using nave
5.3 PC4 dataset 71 Bayes and K-means algorithms 92
 Contents vii

4.2 Geo-space-crime visualization 10 Summary 117

(hotspot detection)—Bangalore using 11 Conclusions 117
nave Bayes and K-means 12 Future enhancements 118
algorithms 94 Acknowledgments 118
4.3 Analysis of geospatial crime density References 118
using the KDE algorithm—India and
Bangalore 95
4.4 Time series analysis using ARIMA 9. Reliable diabetes mellitus
model 97 forecasting using artificial
5 Conclusions 101 neural network multilayer
References 101 perceptron
8. Trust discovery and information Vijayalakshmi Saravanan, Megha Nivurruti,
Ketaki Barde, Anju S. Pillai, and
retrieval using artificial intelligence Isaac Woungang
tools from multiple conflicting
sources of web cloud computing 1 Introduction 121
2 Related works 121
and e-commerce users
3 Methodology 122
P. Solainayagi, G.O. Jijina, K. Sujatha, 3.1 Challenges in applying machine
N. Kanimozhi, N. Kanya, and S. Sendilvelan learning algorithms 122
1 Introduction 103 4 Building the diabetic diagnostic
1.1 Trustworthiness of online or web criteria 122
information 103 5 Evaluating the diabetes outcomes using
2 Trusted computing 104 classification algorithms 127
2.1 Computational trust 104 5.1 Improving the accuracy of SVM and RF
2.2 Trust process 104 algorithms 128
3 Problem identification 105 5.2 ANN: Multilayer perceptron 130
4 Truth content discovery algorithm 105 6 Conclusions 130
5 Trustworthy and scalable service References 131
providers algorithm 106
5.1 TSSP system architecture 106
5.2 Graphical representation 106 10. A study of deep learning approach
5.3 Flow diagram of TSSP 107 for the classification of
6 Efficient feature extraction and electroencephalogram (EEG) brain
classification (EFEC) algorithm 107 signals
6.1 Graphical representation of the EFEC
Dharmendra Pathak, Ramgopal Kashyap, and
algorithm 109
Surendra Rahamatkar
6.2 Data flow diagram of EFEC
algorithm 109 1 Introduction 133
7 QUERY retrieval time (QRT) 110 2 Methods 134
7.1 Programming environment 111 3 Results 135
7.2 Comparison with state-of-the-art 3.1 EEG dataset 135
methods 111 3.2 Implementation domain 135
8 Trust content discovery and trustworthy 3.3 EEG recordings and data
and scalable service providers augmentations 136
algorithm 112 3.4 Data preprocessing techniques 136
8.1 Simulation result 113 3.5 Deep learning architectures 137
8.2 System execution time (SET) 113 3.6 Performance evaluations 139
8.3 Communication cost (CC) 113 3.7 Comparative analysis 140
8.4 Trust score (TS) 114 4 Discussion 141
9 Efficient feature xtraction and 4.1 Rationale 141
classification (EFEC) algorithm and 4.2 Proposed architecture to overcome the
customer review datasets 115 challenges 142
9.1 Performance evaluation matrix 115 5 Conclusions 143
9.2 Accuracy and F-measure 117 References 143
viii Contents

11. Integrating AI in e-procurement of 1.3 Blockchain trust builder attributes 171

hospitality industry in the UAE 2 Applications of artificial intelligence,
machine learning, and blockchain
Elezabeth Mathew and Sherief Abdulla technology 171
1 Introduction 145 2.1 Flag bearers of blockchain
2 Problem statement 146 technology 171
3 Authors’ contributions 147 2.2 Flag bearers of artificial intelligence
4 Significance of the study 147 and machine learning 173
5 Theoretical framework 148 2.3 Flag bearers of blockchain and
6 Research aims and objectives 149 artificial intelligence 173
7 Literature review 150 2.4 Blockchain initiatives by the
7.1 Big data business analytics in the government of India 175
hospitality industry 151 2.5 Current application areas of
7.2 BDBA itself has two dimensions: Big blockchain with artificial
data (BD) and business analytics intelligence 175
(BA) 152 3 It takes two to tango: Future of artificial
7.3 Deep learning and machine learning intelligence and machine learning in
techniques in the hospitality blockchain technology 176
industry 152 3.1 Sustainability 176
7.4 Ecosystem in hospitality 153 3.2 Scalability 176
7.5 Predictive analysis in the hospitality 3.3 Security 176
industry 154 3.4 Privacy 176
7.6 Agent-based technology (ABT) in the 3.5 Adaptability 176
hospitality industry 155 3.6 Efficiency 177
8 Major findings 156 3.7 Transaction speed 177
8.1 Statistics of the trend in 3.8 Performance 177
publishing 156 3.9 Validation of various elements 177
8.2 Major areas of research 157 3.10 Maintainability 177
8.3 Content analysis in the selected 3.11 Scope estimation 177
publications 158 3.12 Cost estimation 177
8.4 New proposed conceptual 3.13 Effort estimation 177
framework for the hospitality 3.14 Project-specific modus operandi 177
industry 158 4 Edge computing: A potential use case of
8.5 Conceptual model for e-procurement blockchain 178
in the hospitality industry 160 4.1 Edge computing architectures 178
8.6 Comparing various studies 161 4.2 Flag bearers of edge computing 181
8.7 Case study 161 4.3 Applications of blockchain technology
8.8 Interview and survey with subject in edge computing 183
matter expert(s) 162 5 Conclusions 183
8.9 Interview and survey validation 162 Acknowledgment 184
9 Discussions 163 References 184
10 Major gaps in the study 164
11 Conclusions 164 Part II
References 165
Data science and predictive
analysis
12. Application of artificial intelligence
and machine learning in 13. Implementing convolutional
blockchain technology neural network model for
Zeeshan Ali Siddiqui and Mohd Haroon prediction in medical imaging
1 Introduction 169 Rajiv Pandey, Archana Sahai,
and Harsh Kashyap
1.1 Blockchain characteristics 169
1.2 Advantages of blockchain 170 1 Introduction 189
 Contents ix

1.1 Deep learning against machine 5 Result and discussion 217

learning 189 5.1 Graphical representation of both
1.2 Deep learning algorithms 190 features and the normalized
2 Convolutional neural networks 190 datasets 217
2.1 Computer image recognition 190 5.2 Principal component analysis
2.2 Image classification 191 result 218
2.3 Why convolutional neural 5.3 The decision boundaries of the SVM,
networks? 191 K-NN, RF, LDA, and CART training
2.4 Functional description of a CNN 192 results 223
3 Implementing CNN for biomedical 5.4 Performance evaluation of training
imaging and analysis 197 results of the five models using ROC,
3.1 Importing the essential python libraries specificity, and sensitivity 223
and Keras library 197 5.5 K-fold cross-validation results 226
3.2 Printing the folder name by using the 5.6 Testing results of the SVM, K-NN, RF,
library’s list directory function 197 LDA, and CART models for fire
3.3 Image generation for evaluation 198 outbreak prediction 229
3.4 Implementing CNN through high-level 6 Conclusions 232
library Keras 199 References 232
3.5 Making CNN model 199
3.6 Analysis of accuracy and result 201 15. Vehicle telematics: An Internet of
3.7 Plotting accuracy and loss graph for Things and Big Data approach
each epoch process 202
4 Architecture models for different image Mukul Singh, Rahul Kumar Dubey, and
Swarup Kumar
type 203
4.1 VGG 16 203 1 Introduction 235
4.2 VGG on chest X-ray dataset 203 2 Big Data 235
5 Conclusion 206 2.1 Definition and characterization 235
6 Future scope 206 2.2 Challenges with Big Data
References 206 analytics 237
2.3 Big Data architecture 237
14. Fuzzy-machine learning models 3 Big Data with cloud computing 238
for the prediction of fire 3.1 Cloud computing 238
outbreaks: A comparative analysis 3.2 Cloud computing with Big Data 240
4 Internet of Things (IoT) 240
Uduak A. Umoh, Imo J. Eyoh, 5 Vehicle telematics 241
Vadivel S. Murugesan, and 5.1 Definition and overview 241
Emmanuel E. Nyoho
5.2 How a telematics system works 241
1 Introduction 207 5.3 Architecture of a telematics
2 Related literature 208 system 241
3 Research methodology 210 5.4 Issues with the telematics system 242
3.1 Data acquisition 211 5.5 Vehicle telematics and Big Data use
3.2 Data label estimation using interval cases 242
type-2 fuzzy logic 211 5.6 Vehicle telematics data
3.3 Data normalization 213 description 243
3.4 Feature selection and dimensionality 6 Case study—Vehicle reaction time
reductions 214 prediction 243
4 Machine learning algorithms for fire 6.1 Dataset 243
outbreak prediction 214 6.2 Data preprocessing 244
4.1 Support vector machine 214 6.3 Sequence formation 244
4.2 K-nearest neighbor 215 6.4 Feature selection 244
4.3 Random forest 215 6.5 Prediction 248
4.4 Linear discriminant analysis 215 6.6 Training the model 250
4.5 Classification and regression tree 216 6.7 Evaluating the model 251
4.6 K-fold cross-validation 217 6.8 End notes 252
x Contents

7 Conclusions 253 2 Literature review 279

References 253 3 Theoretical background 280
3.1 Edit distance 280
16. Evaluate learner level assessment 3.2 Embeddings 280
in intelligent e-learning systems 4 Modeling 281
using probabilistic network model 4.1 Modified Levenstien distance (edit
score) 281
Rohit B. Kaliwal and Santosh L. Deshpande 4.2 Embedding cosine score 282
1 Introduction 255 4.3 Ensemble model 282
2 Related work 256 5 Experimental settings 282
3 Contribution of intelligent e-learning 5.1 Datasets 282
system using BN model 256 5.2 Evaluation tasks 283
3.1 Outline of intelligent tutoring 5.3 Evaluation metrics 283
systems 256 6 Results and discussion 283
3.2 Methods of handling uncertainty 258 6.1 Evaluation of baseline models 283
3.3 Bayesian network (BN) 258 6.2 Evaluation of ensemble models 284
4 Learner assessment model 260 7 Conclusions 286
4.1 Design model 261 References 286
5 Results and discussions 263
6 Conclusions and future work 264 19. Neural hybrid recommendation
References 265 based on GMF and hybrid MLP
Lamia Berkani, Sofiane Zeghoud, and
17. Ensemble method for Imene Lydia Kerboua
multiclassification of COVID-19
1 Introduction 287
virus using spatial and frequency
2 Theoretical background and related
domain features over X-ray images works 288
Anju Yadav, Rahul Saxena, Vipin Pal, 2.1 Recommender systems 288
Ashray Gupta, Parth Arora, Josh Agarwal, and 2.2 Machine learning- and deep learning-
Anuj Diwedi based recommendation 288
1 Introduction 267 3 Neural hybrid recommendation
1.1 Contribution and organization of (NHybF) 289
paper 267 3.1 Description of the model layers 290
2 Literature review 268 3.2 Training 292
3 Proposed methodology 269 4 Experiments 294
3.1 Dataset description 269 4.1 Implementation of the recommender
3.2 Preprocessing 269 system 294
3.3 Feature extraction 269 4.2 Evaluation metrics 294
3.4 Supervised classifiers 271 4.3 Datasets 294
4 Result analysis 272 4.4 Evaluation results 295
4.1 Feature extraction methods analysis for 4.5 Discussion 302
the multiclassification 273 5 Conclusions 302
5 Discussion and conclusions 276 References 302
5.1 Discussion 276
5.2 Conclusions 276 20. A real-time performance
References 276 monitoring model for processing
of IoT and big data using machine
18. Chronological text similarity with learning
pretrained embedding and edit Eesha Mishra and Santosh Kumar
distance
1 Introduction 305
R. Shree Charran, Rahul Kumar Dubey, and 1.1 Monitoring system using IoT-based
Shashi Jain
sensors 305
1 Introduction 279 1.2 Big data processing 306
 Contents xi

1.3 Involvement of machine learning in 4 Results and discussion 334

manufacturing industries 306 4.1 Development of the deep learning
2 Experimental study 306 neural network model 334
2.1 System modeling 306 4.2 Development of fuzzy controller for
2.2 System implementation 306 AHS 334
2.3 Fault detection prediction model 306 4.3 Development of the hybrid deep
3 Major findings 309 learning neuro-fuzzy model 336
3.1 Monitoring system 309 5 Validation of model 339
3.2 IoT-based sensor performance 309 6 Discussions on performance
3.3 Big data processing performance 309 evaluation 339
3.4 Fault detection prediction model 309 7 Conclusions 339
4 Conclusions 313 8 Future scope 340
References 313 References 340

21. COVID-19 prediction from chest 23. An intelligent framework to assess

X-ray images using deep core competency using the level
convolutional neural network prediction model (LPM)
Shambhavi Sharma S. Nithya, M. Sangeetha, and
K.N. Apinaya Prethi
1 Introduction 315
1.1 Contributions of this study 316 1 Introduction 343
1.2 Literature review 316 2 Related work 343
2 Methodology 317 2.1 Summary of limitations 344
2.1 Dataset development 317 2.2 Limitations that are considered for
2.2 Data augmentation 317 design 344
2.3 Proposed architecture 318 3 Existing applications 344
2.4 Model development 319 3.1 JAGRAN JOSH computer GK quiz 345
3 Results and discussions 320 3.2 EDU ZIP the knowledge hub 345
4 Conclusions 322 3.3 TREE KNOX computer quiz 345
References 323 4 Proposed system 345
Further reading 323 4.1 Architecture of the system 345
4.2 Experimental setup 346
22. Hybrid deep learning neuro-fuzzy 5 Experimental 350
networks for industrial parameters 5.1 Classical methods of conducting
tests 350
estimation
5.2 Exam conducted through the level
K. Sujatha, G. Nalinashini, R.S. Ponmagal, prediction model 350
A. Ganesan, A. Kalaivani, and Rajeswary Hari 5.3 Comparison of the classic exam
1 Introduction 325 method and the level prediction
1.1 Literature survey 326 model 351
1.2 Research gaps 327 6 Conclusions 352
1.3 Objectives of this work 327 References 352
2 Preliminaries 327
2.1 Deep learning neural network (DNN)
controller 328 Part III
2.2 Fuzzy logic controller 329 Edge computing
2.3 Hybrid deep learning neuro-fuzzy
logic controller (HDNFLC) 330 24. Edge computing: A soul to Internet
3 Methodology 331 of things (IoT) data
3.1 Development of the deep learning
neural network (DNN) model 331 Vaishali Singh, Ajay Kumar Bharti, and
3.2 Development of the fuzzy logic (FLC) Nilesh Chandra
model 332 1 Introduction 355
3.3 Hybrid deep learning neuro-fuzzy 2 Edge computing characteristics 355
system 333 2.1 Dense geographical distributions 355
xii Contents

2.2 Mobility support 356 8 Pertinent open issues which require

2.3 Location awareness 356 additional investigations for edge
2.4 Proximity 357 computing 369
2.5 Low latency 357 8.1 Privacy and security 369
2.6 Heterogeneity 357 8.2 Convergence and consistency 370
3 New challenges in Internet of technology 8.3 Managing edge resources 370
(IoT): Edge computing 357 8.4 Software and hardware updates 370
3.1 Data aggregation amount and rate of 8.5 Service delivery and mobility 370
IoT devices 357 8.6 Cost 370
3.2 Latency 357 8.7 Collaborations between
3.3 Network bandwidth constraints 358 heterogeneous edge computing
3.4 Resource constrained devices 358 systems 370
3.5 Uninterrupted services with 9 Conclusions 370
intermittent connectivity to the References 371
cloud 358
3.6 Security challenges 358 25. 5G: The next-generation
3.7 Scalability 359 technology for edge
3.8 Privacy 359 communication
3.9 Domination of few stakeholders
(monopoly vs. open IoT Nilesh Chandra, Vaishali Singh, and
Ajay Kumar Bharti
competition) 359
4 Edge computing support to IoT 1 Introduction 373
functionality 359 2 History 374
4.1 Device management 360 2.1 1G: That is where it all started 375
4.2 Security 360 2.2 2G: Cultural revolution 375
4.3 Priority messaging 360 2.3 3G: “Pack-switching” version 376
4.4 Data aggregation 360 2.4 4G: Broadcast time 376
4.5 Data replication 361 2.5 5G: Internet of things age 376
4.6 Cloud enablement 361 3 5G technology 377
4.7 IoT image and audio processing 361 3.1 1G: Radio access network 378
5 IoT applications: Cloud or edge 3.2 Core network 378
computing? 361 4 5G cellular network 378
6 Benefits and potential of edge computing 5 Components used in 5G technology/
for IoT 363 network 379
6.1 Low latency 364 5.1 3GPP on 5G 379
6.2 Less power consumption by IoT 5.2 Spectrum for 5G and frequency 379
devices 364 5.3 MEC (multiaccess edge
6.3 Simpler, cheaper devices 364 computing) 380
6.4 Bandwidth availability and efficient 5.4 NFV (network function virtualization)
data management 364 and 5G 380
6.5 Network connectivity 364 5.5 5G RAN architecture 380
6.6 Network security 365 5.6 eCPRI 381
6.7 Autonomy 365 5.7 Network slicing 381
6.8 Data privacy 365 5.8 Beamforming 381
6.9 Data filtering/prioritization 365 6 Differences from 4G architecture 381
6.10 Support to 5G technology 365 6.1 Worldwide adoption of 5G 382
7 Use case: Edge computing in IoT 365 7 Security of 5G architecture 382
7.1 Autonomous vehicles 365 8 5G time period 382
7.2 Smart cities 366 9 Case study on 5G technology 382
7.3 Smart grid 366 9.1 5G use cases and services 383
7.4 Industrial manufacturing 367 9.2 The 5G project use cases 383
7.5 Health care 367 9.3 Smart mobility 388
7.6 Cloud gaming 368 10 5G advancement 389
7.7 Augmented reality devices 368 10.1 Superspeed 389
 Contents xiii

10.2 Increased bandwidth 390 27. State of the art for edge security in
10.3 Global wide coverage 390 software-defined networks
10.4 Our own world will be a Wi-Fi
zone 390 Shailesh Pramod Bendale,
Jayashree Rajesh Prasad, and
10.5 Improved battery life 390 Rajesh Shardanand Prasad
11 Advantage and disadvantage of 5G
technology 391 1 Introduction 411
11.1 Important benefits 391 2 Hybrid software-defined networks 412
11.2 Other benefits of common 3 Security challenges in hybrid software-
people 391 defined networks 413
11.3 Disadvantages 391 4 Solutions for hybrid software-defined
12 Challenges 391 networks 415
12.1 Technological challenges 392 4.1 QoS (quality of service) 415
12.2 Common challenges 392 4.2 DDoS (distributed denial-of-service)
13 Future scope 393 attack 415
14 Conclusions 393 4.3 MITM (man In the middle) attack 415
References 393 4.4 Programmable network solution 415
4.5 ARP poisoning 415
26. Challenges and opportunities in 4.6 DoS (denial-of-service) attack 415
edge computing architecture using 4.7 Botnet attacks 416
4.8 Platforms for hybrid software-defined
machine learning approaches
networks 416
Naman Bhoj and Robin Singh Bhadoria 5 Learning techniques for hybrid software-
defined networks 417
1 Introduction 395
5.1 Machine-learning techniques 417
2 Overview of edge computing 396
5.2 Supervised learning 417
2.1 Architecture of edge computing 396
5.3 Unsupervised learning 419
2.2 Use cases of edge computing 397
5.4 Deep learning 420
2.3 Advantages of edge computing 398
6 Discussion and implementation 420
3 Security and privacy in edge
7 Conclusions 422
computing 399
References 422
4 Intersection of machine learning and
Further reading 424
edge using enabling technologies 399
4.1 Defining AI, ML, DL 399
4.2 Enabling technologies for machine
learning and edge computing 400 28. Moving to the cloud, fog, and edge
5 Machine learning and edge bringing AI to computing paradigms:
IoT 402 Convergences and future research
6 OpenVINO toolkit 403 direction
6.1 Example of edge computing
K. Rajkumar and U. Hariharan
architecture for malaria detection 405
6.2 Edge computing architecture 1 Introduction 425
developed by industry pioneers 405 2 Features and differences between cloud,
7 Challenges in machine learning and edge fog, and edge computing 426
computing integration 406 2.1 Cloud computing 426
7.1 Different data distribution 406 2.2 Edge computing (EC) 427
7.2 Discovering edge node 406 2.3 Fog computing 428
7.3 Secure usage of edge nodes 407 3 Framework and programming
7.4 Heterogeneity in data 407 models: Architecture of fog
7.5 Energy consumption of edge computing 428
devices 407 3.1 Framework as well as programming
8 Conclusions 407 models: Data modeling within fog
References 407 computing 429
xiv Contents

4 Moving cloud to edge computing 431 2.3 Proposed blockchain-enabled smart

4.1 The necessity for edge computing 431 grid framework 457
4.2 Challenges in industries that are 2.4 Result discussion 460
different 431 2.5 Proposed work benefit 464
5 Case study: Edge computing for 2.6 Comparative analysis 464
intelligent aquaculture 435 3 Conclusions 465
5.1 Technology considerations 436 References 465
5.2 Guide architectures 437
5.3 Logically centralized control 30. AI cardiologist at the edge
plane 437
5.4 Architectural considerations Marjan Gusev
that are shaping future edge 1 Introduction 469
computing 439 2 Related work 470
6 Conclusions 441 2.1 Internet of medical things 470
References 441 2.2 Health-care edge computing IoT
solutions 470
2.3 ML and DL with edge computing 470
29. A comparative study on IoT-aided 3 Architectural approach 471
smart grids using blockchain 3.1 Postcloud architectures 471
platform 3.2 Dew computing solution 471
3.3 Autonomous AI-based solution 471
Ananya Banerjee 4 ECGalert use case 472
1 Introduction to smart grid, IoT role, and 4.1 Functional description 472
challenges of smart grid 4.2 AI solution at the edge 473
implementations 443 5 Discussion 474
1.1 Introduction 443 5.1 Challenges 475
2 Secure smart grid using blockchain 5.2 Benefits and disadvantages 475
technology 456 6 Conclusions 475
2.1 Blockchain’s opportunities and References 476
emerging solutions in energy
sector 456
2.2 Blockchain-based smart grid 457 Index 479
Contributors

Numbers in parenthesis indicate the pages on which the authors’ Santosh L. Deshpande (255), Department of Computer
contributions begin. Science & Engineering, Visvesvaraya Technological
Sherief Abdulla (145), Faculty of Engineering and University, Belagavi, Karnataka, India
Informatics, British University in Dubai, Dubai, United Anuj Diwedi (267), Manipal University Jaipur, Jaipur,
Arab Emirates Rajasthan, India
Josh Agarwal (267), Manipal University Jaipur, Jaipur, Rahul Kumar Dubey (65, 235, 279), Robert Bosch
Rajasthan, India Engineering and Business Solutions Private Limited,
K.N. Apinaya Prethi (343), Department of CSE, Bengaluru, Karnataka, India
Coimbatore Institute of Technology, Coimbatore, Imo J. Eyoh (207), Department of Computer Science,
Tamilnadu, India University of Uyo, Uyo, Akwa Ibom, Nigeria
Parth Arora (267), Manipal University Jaipur, Jaipur, A. Ganesan (325), Department of EEE, RRASE College of
Rajasthan, India Engineering, Chennai, Tamil Nadu, India
Ananya Banerjee (443), Department of Computer Science, Ashray Gupta (267), Manipal University Jaipur, Jaipur,
Kalyani Government Engineering College, Kalyani, Rajasthan, India
West Bengal, India
Manu K. Gupta (75), Department of Management Studies,
Ketaki Barde (121), M.S (Data Science), Rochester IIT Roorkee, Roorkee, India
Institute of Technology, Rochester, NY, United
States Marjan Gusev (469), Ss Cyril and Methodius University in
Skopje, Faculty of Computer Science and Engineering,
Shailesh Pramod Bendale (411), SKNCOE, Research Skopje, North Macedonia
Centre, Savitribai Phule Pune University, Pune,
India Rajeswary Hari (325), Department of Biotechnology, Dr.
MGR Educational & Research Institute, Chennai,
Lamia Berkani (287), Laboratory for Research in Arti- Tamil Nadu, India
ficial Intelligence, Department of Artificial Intelligence
and Data Sciences, Faculty of Informatics, USTHB Uni- U. Hariharan (425), Department of Computer Science and
versity, Algiers, Algeria Engineering, Apex Institute of Technology, Chandigarh
University, Mohali, Punjab, India
Robin Singh Bhadoria (395), Dept. of Computer Science
& Engineering, Birla Institute of Applied Sciences Mohd Haroon (169), CSE Department, Integral Uni-
(BIAS), Bhimtal, Uttarakhand, India versity, Lucknow, India
Ajay Kumar Bharti (355, 373), Department of Computer Harshvardhan (53), Department of Civil Engineering,
Science, Babu Banarasi Das University, Lucknow, Indian Institute of Technology, Delhi, India
India Nandyala Hemachandra (75), Industrial Engineering and
Shobhit Bhatnagar (75), Industrial Engineering and Oper- Operations Research, IIT Bombay, Mumbai, India
ations Research, IIT Bombay, Mumbai, India Shashi Jain (279), Department of Management Studies,
Naman Bhoj (395), Dept. of Computer Science & Engi- Indian Institute of Science, Bengaluru, Karnataka, India
neering, Birla Institute of Applied Sciences (BIAS), G.O. Jijina (103), Department of Electronics and Commu-
Bhimtal, Uttarakhand, India nication Engineering, Aarupadai Veedu Institute of
Nilesh Chandra (355, 373), Department of Computer Technology, Chennai, India
Science, Maharishi University of Information Tech- A. Kalaivani (325), Department of CSE, Saveetha School
nology, Lucknow, India of Engineering, SIMATS, Chennai, Tamil Nadu, India

xv
xvi Contributors

Rohit B. Kaliwal (255), Department of Computer Science Rajiv Pandey (189), Amity Institute of Information Tech-
& Engineering, Visvesvaraya Technological Uni- nology, Lucknow, Uttar Pradesh, India
versity, Belagavi, Karnataka, India Dharmendra Pathak (133), Amity School of Engineering
N. Kanimozhi (103), Department of Computer Applica- and Technology, Amity University Chhattisgarh,
tions, A.V.C. College of Engineering, Mayiladuthurai, Raipur, Campus, India
India Anju S. Pillai (121), Department of Electrical and Elec-
N. Kanya (103), Department of Information Technology, tronics Engineering, Amrita School of Engineering,
Dr. M.G.R. Educational and Research Institute, Amrita Vishwa Vidyapeetham, Coimbatore, India
Chennai, India R.S. Ponmagal (325), Department of CSE, School of Com-
Ramgopal Kashyap (133), Amity School of Engineering puting, SRM Institute of Science & Technology, Kat-
and Technology, Amity University Chhattisgarh, tankulathur, Chennai, Tamil Nadu, India
Raipur, Campus, India
Jayashree Rajesh Prasad (411), School of Engineering,
Harsh Kashyap (189), Amity University, Lucknow, Uttar MIT Art, Design & Technology University, Pune, India
Pradesh, India
Rajesh Shardanand Prasad (411), School of Engineering,
Imene Lydia Kerboua (287), Institute of Communication, MIT Art, Design & Technology University, Pune, India
University Lumière Lyon 2, Lyon, France
Boppuru Rudra Prathap (87), Computer Science and
Param Khakhar (65), Department of Computer Science Engineering, CHRIST (deemed to be University), Ben-
and Engineering, Indian Institute of Technology Delhi, galuru, Karnataka, India
New Delhi, Delhi, India
Surendra Rahamatkar (133), Amity School of Engi-
Santosh Kumar (305), Department of Computer Engi- neering and Technology, Amity University Chhat-
neering & Information Technology, Swarrnim Startup tisgarh, Raipur, Campus, India
& Innovation University, Gandhinagar, Gujarat, India
K. Rajkumar (425), Department of Computer Science and
Swarup Kumar (235), Robert Bosch Engineering and Engineering, Jain University, Faculty of Engineering
Business Solutions Private Limited, Bengaluru, Kar- Technology, Bangalore, Karnataka, India
nataka, India
Chinmay Rane (3, 33, 53), Quantiphi, Inc., Marlborough,
Michael Manry (3, 33, 53), Department of Electrical Engi- MA, United States
neering, The University of Texas at Arlington,
Arlington, TX, United States Archana Sahai (189), Amity Institute of Information Tech-
nology, Lucknow, Uttar Pradesh, India
Elezabeth Mathew (145), Faculty of Engineering and
Informatics, British University in Dubai, Dubai, United M. Sangeetha (343), Department of IT, Coimbatore
Arab Emirates Institute of Technology, Coimbatore, Tamilnadu, India
Eesha Mishra (305), Department of Computer Science & Vijayalakshmi Saravanan (121), Faculty, Rochester
Engineering, Maharishi University of Information Institute of Technology, Rochester, NY, United States
Technology, Lucknow, Uttar Pradesh, India Rahul Saxena (267), Manipal University Jaipur, Jaipur,
Vadivel S. Murugesan (207), Department of Industrial Rajasthan, India
Production Engineering, National Institute of Engi- S. Sendilvelan (103), Department of Mechanical Engi-
neering, Mysore, India neering, Dr. M.G.R. Educational and Research Institute,
G. Nalinashini (325), Department of EIE, RMD Engi- Chennai, India
neering College, Chennai, Tamil Nadu, India Shambhavi Sharma (315), Amity University, Noida, Uttar
S. Nithya (343), Department of CSE, Coimbatore Institute Pradesh, India
of Technology, Coimbatore, Tamilnadu, India R. Shree Charran (279), Department of Management
Megha Nivurruti (121), M.S (Data Science), Rochester Studies, Indian Institute of Science, Bengaluru, Kar-
Institute of Technology, Rochester, NY, United States nataka, India
Emmanuel E. Nyoho (207), Department of Computer Zeeshan Ali Siddiqui (169), CSE Department, Integral
Science, University of Uyo, Uyo, Akwa Ibom, Nigeria University, Lucknow, India
Vipin Pal (267), NIT Meghalaya, Shillong, Meghalaya, Mukul Singh (235), Indian Institute of Technology Delhi,
India Delhi, New Delhi, India
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ana- has been traced in not a few instances, (cp. Pischel, Gramm. d,
Prakrit-Spr. § 77). I should, however, be inclined to think that this
phenomenon is only a secondary development, having no true base
in the original language; thus sa. anas bhavakrta would mean ,made
not non-existing“ 9: brought into a state in which it can neither be
said to exist nor not to exist; in this case an-abhava would be adj.
,free from annihilation‘ (a-bhiva being taken in a kind of positive
value, as Fausbell suggests), and kata would be correct, cp.
anamatagga, *an-abhirati, f. not delighting in, discontent (w. loc.),
acc. with, 47,34 (agiira-majjhe), *an-abhirata, m/n. not taking plea 
 sure in (loc.), m. ~o (naceadisu) 64,32 (cp. abhiramati),
*an-amatagga, mfn. endless, loc. ~asmim samsire ,in the endless
revolution of being’ 89,15; °-katha, f. acc. ~am kathesi ,,he
instructed him about Samsara“ 89,15. This word has generally been
taken as = Sa, *an-amrta + agra ,which does not end in Nibbiina“
(ep, amata above), or *ana-mata (man) + agra, ,whose end is not
known“ (Alwis, Buddhist Nirv, p. 21., Tr. PM., p. 64, with the negative
prefix doubled, like ana-bbiva-kata). Weber, Ind. Str, III p. 150 refers
to Sa. an-amrta, ,without end or beginning (cp. an-dmatam
,immortal* Jat. 11 66, 9), but Jacobi and Pischel have shown that
anamatagga must be identical with Prakrit: anavadagga or
anavayagga and have taken it — So *a-namadagra (ynam) ,dessen
Anfang sich nicht veriindert, endlos“ (Jacobi. Erzih), 33,17, Pischel,
Gramm, § 251, cp. an-abhavakata above), an-ariya, mfn. ignoble,
low, m. ~o (anto) 66,27. an-alliyanta, », alliyati. an-avakasa, mfn,
that cannot take place, impossible, not occurring, m. ~0 yo... (w.
pot.) yit cannot occur that one should . ,“, 76,26, an-avatthita-citta,
mfn. unsteady-minded, gen. m. ~assa, Dh.38, *an-avassuta-citta,
mfn, whose mind is free from lust, gen. m. massa. Dh, 39. (cp.
avassuta, asava, SBE. X p, 183—14). an-aigata, mfn. future. acc. m.
wath (attharh) 112.4; loc. (adv.) atitanagate, in the past and in the
future, 56,11; ~vatnsa, q. v. *an-agamana, n. not coming, not
returning; pacchato kassaci °-bhavam fatva , seeing no one
pursuing“, 40,11; asuranam °-atthaya, ,to prevent the A’s from
coming back“, 60,36. anan-agara, m. houseless, a mendicant, instr.
pl. wehi Dh. 404. an-Acara, m, misconduct, immorality, ace. wam
9,15, 52.30. an-acikkhitva, v. acikkhati. an-atura, mfn, free from
suffering, m, pl. ~a Dh, 198, *an-adana, mfn, free from affection or
desire, m. ~o Dh. 3652, ace, math Dh. 406 (opp. sadana). an-
aiyanta, v. ayati. *an-ilaya, m. not desiring, aversion, doing away
with, nom, ~o (tanhaya) 67,16. an-avila, mfn. clear, pure,
undisturbed, m. ~o (rahado) Dh, 82, ace. wath Dh, 413, an-asaka, f.
(sa. anagaka, n.) fast ing, Dh, 141 (cp. asa). *an-disava, mfn. free
from passions, m, aco. wath Dh. 386, gen. ~wassa, Dh, 94, pl, ~&
Dh. 126. an-ahara, mfn, having or taking no food, being without
nutriment, m, ~0 (aggi) 95,8. an-ukkanthamadna,v. ukkanthati. an-
utthahdna, v. utthahati. an-utthana, vn. the act of not rising, want of
energy or firmness; °-mala, mfn, whose taint (fault) is bad repair, pl.
~a ghara_ ,,houses are useless, if they are in bad repair’ Dh, 241
(cp. mala). an-uttara, mfn. best, highest, unsurpassed, m. ~o
(silagandho) Dh. 55, acc, wam (yogakkhemam) Dh, 23 an-uddhata,
mfn. ’not lifted up‘, calm (in speech), m. wo (bhikkhu) Dh. 363.
*“an-upakkameana, adv. not by attack (from external enemies)
76,97 (opp. partpakkamena, »v. upakka-. ma). an-upagata, an-
upagamma, ». upagacchati. an-upaghata, m. not striking, not
abusing, nom. ~o Dh. 185. *an-upaddava, m/n. uninjured, safe, loc.
#. ~e (mule) Dh, 338. |
 ans *an-upadduta, mfn, not annoyed, ‘not oppressed, m,
idam.. wath pbere is no distress“, 68,14, *an-upalitta, mfn, not
besmeared, free from taint, m. .~o Dh. 353 (ant). *an-upavada, m.
not blaming, not abusing, ~o Dh. 185. *an-upassattha, m/n. not
afflicted, not plagued, ». idara.. «am ,here is no danger“, 68,14.
*an-upahara, m. not presenting, afifassa ~a (abl.) ,because it can
get no other ial 95,8. an-upida = an-upadaya. ¢anupadiyana, vw.
upadiyati. *“an-upayena, adv. by misguided means, 34,17 (v.
wpaya). an-uppada, m, not coming into existence; °-dhamma, mfn,
not liable to come into existence again, nm. wala (ruparh) 95,11 (cp.
dhamma). an-usuyyaih, v. usuyyati. an-ussuka, mfn. not eager, free
from greed, m, vl. ~& Dh, 199. “in-ussute, mn, free from lust, acc,
m. ~um Th. 400 (= an-avassuta, g. v. (Fsb.); fr. sa. *an-udsruta
bastaly im-upalitta — an-upalitta, q¢. ». an-uhata, mfn, not
destroyed, loc, r-e Dh, 338 (v. dhaffati). an-eka, mfn. many;
%Akara, mfn, multiform ; °-vokira, m/fn, containing many
disadvantages, acc. m. wall (€dinavath) 85,6; °-fidinava, mfn. full of
dangers, m. xo (samuddo) 23.7, °-jati-eamcara, “m. a course of
many b'rths, ace, ~am Dh, 153; S-pariyayena, instr, adv. in many
ways, 59,18; °-3pa-vyaiijana, nifn. richly supplied with sauce &
condiments, », -vam (bahubhattam) 57.11. *an-eja, mfn, free from
lust (eja, f. q. v.) m. ~o0 (muni) 80.33, Dh, 4'4; acc, ~am Dh, 422.
ah enta, vw. etl. an-cka, m. a hovseless state, acc, 10 wat (adv,?)
Db, 87; °-sarin, min. wandering about homelese, acc. ™. wsarim Db.
404, ‘ an-okkanta, v. okkamati. “an-odaka, mfn. without water, dry, f.
~@% (nadi) 31,12. amsa, m. (= sd.) ‘) a shoulder, instr, wena
paticchitum nasakkhi ,could not get hold of him by his shoulder“ (9:
dropped him? or have we to take amsena — in part (adv.)? and
translate ,could not thoroughly get hold of him), *) (sa. am¢a) a
part. portion; v, ekathsa, sukkamsa. akka, m. (sa. arka) name of a
plant (Calotropis gigantea, ,swallowwort“ (Child.); gen. ~assa (jiya),
made from that plant, 92,16. akkamati, vd. (sa, a-\/kram), to tread
upon (acc.); ger. witva 3,21. akkamma (sa. a-kramya) 108.29.
akkosa, m. (sa, ikroga) abuse, res proach, acc. ~am Dh. 399.
akkosati, wb. (sa. a-Vkruc), to abuse (acc.) pr. 3. sg. wati (bhikkhu)
84.29; part. an-akkosam (m. = ~wanto, not abusing) 14,4. m. pl.
evanta, 73,34. akkha, m. (sa. aksha) an axle (of a chariot), nom. ~0
98,4. akkhara, » & m. (sa. akshara, n.) w letter, gen, pl. ~anatn Dh,
352. akkhatar, m. (sa. akhyatr) a preacher, » teacher, pl, waro (‘Ta+
thagata) ,(only) preachers“, Dh, 276. ukkhati, vb, (sa, a-Vkhya), to
tell, communicate; dmp. xabi (tarh me) 54,37; pp. akkhata, m. ~o
me maggo ,the way was preached by me", Dh, 275; sv-akkhato
dhammo , well taught is the doctrine“, 70,16 (cp. su-); samma-d-
akkhata Dh, 86, v. samma; an-akkhata q. v. akkhi, ». (sa. akshi) the
eye; pl. nom. wini 3.17; abl. mihi 5.4; gen. ~winam 59,5.
mandakkhi, adj. f. 20,27. v. manda. agara (d: dgira), m. (— sa) a
house; nom. sam 106,31 = Dh, 14;
 pl. ~anji Dh, 140, *) a household life, ace, wath 61,32, abl,
wa 61,33, ~asma (pabbajja) 68,4; °-majjhe yamid a householders
life“, 46,17, 47,21 (v. majjha). — bandhanigira, a prison, v.
bandhana, — suniagara, an empty house, v, suina. (cp. an-igiira, an-
agariya), *agarika, m. (fr. agara) a householder, a layman; °-bhito,
m. ,while he lived in his bouse“, 69,28 (cp, bhavati). agga, mfn. (sa.
agra) ') foremost, first; wam samgahath (acc.) ,,the first collection®
109,90; agga-nikkhittakii (thera) , original depositaries Buddha’s
doctrine)“ 109,11; agga-vado the first or original doctrine =
theravado, 109,30. ~ *) highest, topmost; agga-sakha (ace. f. pl.)
,,the topmost branches“ 62,11. — °) excellent, best, chief, principal;
m, ~o dhutavadanath »the chief propounder of the Dhutanga“
109,6; agga-dhamma, aggamahesi, qg. v.; agga-rasa-, v. nana;
agga-raja ,the chief King“ 98,13; agga-santike ,from the first (among
teachers)" 109,28, — *) subst. n. top, tip, point; ~am (acc,) ,the
best part“ 111,35; at the end of comp. : Aragge (loc.) on the point
of a needle (v. ari) Dh. 401; kusaggena (énstr.) »With the tip of a
blade of Kusa-grass“, Dh, 70; ktpagge (Joc.) on the top of the mast,
18,6; rukkhagge, 11,25; sakhagge, 18,22 and sikhaggesu (Joc. pl.)
12s (v. sakha); dumaggamha (abl.) down from the top of the tree,
13,4; -vettaggam 62,17 (v. vetta); labhagga-yasagga-ppatta, m/n.
having obtained the highest gain and glory, 18,16 (cp. patta);
rupagga-ppatta, mfn. of extraordinary beauty, 49,12 (~waya, gen. f.)
(cp. ajjatagge, anamatagga.) : *aggata, f. (fr. agga w. suff. -ta)
superiority; gunaggatamh (acc.) the summit of perfection* 109,s.
“agga-dhamma, mfn, most excellent in the knowledge of the true ll
aggha doctrine; wa tathigata (pl.) the T-s are the chiefs in the truth,
109,28. agga-mahesi, /. (sa. agra-mahishi) a queen, the chief-
queen, 19,7, 46,21; gen. wiya 38,9. *Aggalava, (m. or n.?) nom, pr.
a sanctuary at Alavi; Joc, ~e cetiye 86,13; °-viharam (acc.) 87,4. A.
seems to be a comp, agga + Alavi (q. v.), but might possibly be a
false etymology for *Aggalaya (sa, agnyalaya?), aggi, m. (sa. agni)
')fire; ~i 16,7. 95,3. Dh, 202. 251; aggiva 26,5. Dh. 31; acc, wim
kareyyadsi ymake a fire 85,8. wim jaletva ,to light a fire“ 100,24.
wim datvi ,to set light to%. Blu; instr, wind 16,2. 35,4; padipaggi,
the fire of a lamp, 101,7, —») a pyre, a funeral pile; wim pavisitva
51,10 (as an ordeal), ~ 5) the sacrificial fire; ~im paricare ,to
worship Agni* Dh, 107. — 4) metaph. »passion“ : dosaggi, mohaggi,
ragaggi (q. v.) ythe fire of anger, ignorance lust*, ; *aggikkhandha,
m. (aggi + khandha) @ great body of fire; ~o 26,3 (pajjalita-°),
aggidaddha. mfn, (aggi + daddha, pp. v, dahati) burnt by fire; ~o
Dh. 136. *Aggimala, m.(?) nom. pr. (aggi -+- mala — mala?) name
of an ocean; acc, ~am 26,3. — *Aggimali(m), m.(?) id, (= ,fire-
garlanded“) 26,8. *Aggi-Vacchagotta-suttanta, 2. the title of a
dialogue between Buddha and Vacchagotta, MN. 72, aggisikha, f.
(sa. agni-cikha) a flame; °-sikh’upama, mfn. ,like flaming fire“, ~o
(ayogulo) 107,1 — Dh, 308 (cp. upama). aggihutta, , (sa. agni-hotra)
oblation to Agni; acc. ~am juhato, e ‘sacrificing to Agni, 103,, — *)
the sacrificial fire, Db. 392. aggha, m. (sa. argha) value, price; in
comp, an-aggha, mfn. q. v. beyond
 agghati all price, invaluable; appaggha, mfn. of little value,
26,2; mubaggha (v. mahi) mfn, of great price, n. sam QF.5. :
*agghati, vb. (sa. -/argh), to be worth (w, ace.); pr. 8. eg. na wati
(mama saimikassa padarajam) 58,5; nigghuti (knlam sulasit) Dh, 70.
cans, neghiipoti, qv. "urghunika, mfn, (fr, agghuna, mn. (argh)
valuation, w. su/f. -ka) werth; satasahassaggharakam (muttaharam,
ace. m.) worth 100,000, 64,25. *agehapaniya, m. (fr. agghapana, .
(agghapeti)) a valuer; °%-kamma, ». the office of a valuer, loc. ne
24,18, *agghapeti, vb. caus. (fr. agghati), to appraiss; pr. 3, 6g. ~eti
24,20 (ace.). athka, m. sa.) a side, breast, hip; instr, ~ena uddhuri
(mam), lifted (me) up unto her hip, 20,25; darake athkenddaya, with
their childs on their hips, 21,2; loc. ~e nisinnam puttam ,a baby boy“
38,15. amkura, m.' (== 8a.) a sprout, o shoot; °-nibbattana-tthana,
n. the place where the sprout develops, 37,5. afikusa, m. (sa,
afikuga) a hook to guide an elephant with, a goad: instr, pl. ~ehi
77,13. — afikusa-ggaha, m. (sa. afikuga-graha) an elephantdriver,
Dh, 326, afiga, n. (= sa.) ') a limb, nember, a part of the body;
uttamafiga, the head, °ruha, m,n. growing on the head, pl, m. wa
(9: the hairs) 45,11; afigavijja, g.v. — *) a part or portion; afiga-
sambhara (abl.), bringing together the various parts, 98,30;
sabbaiiga-sampanna, mfn. complete in every part, 110,13, — %) a
point or a constituert part of a system of rules; uposathufigati (pl.),
the holy day wows, 61,7; bojjhafiga, sambodhianga, & Afiguttara (q.
v.). — *) 4 quality, attribute, ingr, pl. dasah(i) anehi, 82,14. — 5)
comp. vw. num. —_ 12 — -fold (ep. aiigika & afigin) navanga, eS,
pine-fold, ~am ee sanam 109,92. — ") comp. ™ afigi, ». sam-afigi-
bbita. afigana, n. (sa. afigana) on a space before a house; rajaigana,
the king’s courtyard, loc. ~e 8,1. naar #) metaph, (only in comp.
with the prefixon une, nites Kile) the mean or vulgar life o: lust, sim}
inenfigana, mfn. (q. v.) [ep. Bohtlingk, Ber. 4. siichs, Ges. 1898. p.
77; Rhys Davids, JRAS, 1898. p. 193 & 462.]. afiga-vijja, f. (sa.
afiga-vidya) the science of prognostication, chiromantia etc.: loc,
niiya 48,16. aiigira, m. (= sa.) charcoal, burning coals, fire; loc. we
15,32, °-gabbhe, amid the fire, 15,33 (v. gabbha) ; °-rasi, m, a heap
of burning coals, acc. ~1M 16,3. afigika, mfn. (sa. aiigaka) comp. w.
num, v. atthafigika, paiicafigika (cp. afiga 5) d& next), afigin, mfn.
(= sa.) comp. tw. num. v. caturaigin (cp. aiiga °) & prec.).
*Ajfiguttara-nikaya, m, nom. pr. (fr. aiiga + uttara o: one part more,
,the add-one collection*, cp. Morris, preliminary remarks, AN. vol. I.
p. 1X.), name of a canonical Paliwork, the fourth of the five Nikayas;
comm, Manoratha-pirani (q. v.); ~o 10214. afigula, m. (= 8a.) a
finger, the measure of a finger’s breadth, an inch; v. catur-afigula,
m/fn. afiguli, f. (= 8a.) a finger; »v. paicaigulika. *Aciravati, fi nom.
pr. a river in India (Rapti); °-tiram, n. the bank of A. 28,4. accagama
& accaga, t. atigacchati (cp. upaccaga). accanta, mfn. (fr. ati + anta,
sa, atyanta), excessive, perpetual; adv. ~am, in perpetuity,
absolutely: niccanta(th], not always, 6,21. — °-sukhumala, m. ,an
exceedingly delicate
 prince’ 97,34. — °-dussilya, ». ,,very great wickedness“ Dh.
162, accaya, m. (sa, atyaya, cp. atigacchati). !) passing away, lapse
(of time), end, death; instr. adv, ~ena pat the end of (wv, gen. or in
comp.): pitu wena ,when his father died“ 24,13; mam’ accayena
79,5; tass& rattiya a-° at the end of the night# 78,1; ekaha-dviha-°
,in one or two days“ 32,24; katipaha-° ,a few days later“ 49,92;
satt’-attha-divas’-a° ,seven or eight days later“ 36,1;
masaddhamasa-° ,,at the end of one and a half month 20,11, — *)
transgression, sin; ~O Mam accagama ytransgression has overcome
me“ 75,23; tassa me Bhagava accayamh accayato patiganhatu.,may
Bh, accept the cone fession I make of my sin“ 75,95; the words
accayath accayato (acc, ¢: abl.) may originally be due to phrases like
~am wato passati (Vin. J, 315) ,,to see the sin in its sinfullness“, or
~am ~ato deseti (SN.1, 239) ,,to confess, to apologize. — %)
overcoming, conquering; dur-accaya, mfn. difficult to be conquered,
acc. f. wath (tanham)108,1. acci, f. (sa. arci(s), m. 7.), a flame; nom.
ya acci 99,91. acchati, vb. (sa. Vas) to sit, stay, remain; pr. 3, pl.
~anti 76,29, The pr. acchati seems to be a later formation from aor.
acchi (sa, *atsit) cp. Tr. PM. 61,3; K. F. Johansson, Idg. F. II 205. (—
sa.pcchati, Pischel, Gr. § 480.) *acchara, f. @ snap with the fingers;
°-sadda, m, ~ena (imstr.) yat the snapping of the fingers“ 18,17.
acchariya, mfn. (sa. agcarya) marvellous, wonderful, astonishing; /.
~& (Buddhanam katha) 86,%; x. wath (in exclamstions) how
wonderful! 79,25. 98,82; 8. 7. a wonder, a marvel; acc. wath 3,22.
5,19; pl, ace. ~wani 25,9. (cp, accheraka). acchadana, v. (sa. acch-
°) covering, clothes; ~arh 31,s-9, — samika-° the protection of a
husband, ~arn (acc.) 31,7-8. 13 ajjhavisayi acchadeti, vb. caus.
(sa,a-Vchad) to array in (acc. & instr.), to put on (clothes, acc.); ger,
wetva (tam dibbavatthehi) 20,8; ~(ahatavatthani) 33,3, *vecheraka,
mfn. (fr. acchariya w, suff. -ka), ati-acch-° mfn, admirable,
extraordinary; 2. wat 3,22, aja, mm. (== sa.) a goat, a ram; no
64,8; voce, aja, 54.12; pl. wa 54,12, — aja-raja (voc.) 64,26. —
ajika, a she-goat; acc. ~am 54,8, (cp. ajina). Ajatasattu, m, nom. pr.
(sa. Ajita-catru o: having no enemy) a son of king Bimbisira (q. 0).
kuMara, m, the prince A, wo 76,1; wath (ace.) 75,2. ajika, v. aja.
ajina, . (= 8a.) a skin (of a goat(?) esp. of the black antelope, used
by ascetics). °-sati, /. a garment of skins; instr, wiya 106,10. — Dh,
394. ajja, adv. (sa. adya) to-day, now, 2,30. 3,14; ajjipi tava ,until
this day“ (w. pr. of the verb) 10,13; ajj’eva »this very day“ 65,13;
ajj’aham Dh. 326. *ajjatagge, adv. (fr. ajjato (sa. *adya-tas] + agge,
v. agga‘)) from this day forth, henceforth, 69,30. (cp. Weber, Ind.
Str, III. 150.). ajjatana, mfn. (sa, adyatana) of to-day, modern (opp.
porana); ”. ~am Dh, 227 (metri causa ~am). ~aya, adv, (dat. or loc.
f.?) to-day 70,10. ajjhaga, ajjhagu, v. adhi-gacchati. ajjhatta,n. (sa.
adhy-dtman) the soul, individual thought. °-samutthana, mfn.
originating from internal (intellectual) cultivation, f. ~a (hiri) 10,16
(opp. bahiddha-samutthana).— °-rata, mfn, delighting inwardly, m.
~o Dh, 362. ajjhattika, mfn. (sa, adhy-atmika), belonging to the soul
or to the individual; ». pl. ~ani dyatanani, the internal senses, 82,11.
ajjhabhasi, v. adhi-bhasati. ajjhavasayi, v. adhi-vaseti.
 ajjhaya ajjhaya, m. (¢a,adhyaya) reading, v. sajjhaya. ajjha-
vasati, ¢b. (sa, adhy-ayvas) to inhabit (acc.j; fut. 3. sg. ~issati
(agdram) ,to live a household lifes 61,31. *ajjhasaya, m. (fr. sa. adhi
+ acaya (1/¢i)) meaning, intention; sabbesam °-gahanattham (cp.
attha), io order to hear the meaning of the assembly, 11,4.
*ajjhokasa, m. (fr.ddhi-+- okdsa, q.v.) the open air, an open place;
loc. we (cafikamati) 68,9, *ajjhottharati, pr. (fr. *adhiava-y/str) to
strew about, to throw on the ground (acc.) ger. ~witva (turiyéni)
65,3, *ajjhoharati, vb. (fr. adhi-avaVhr) to eat, to swallow (ace.) inf.
ewiturh (ambaphalam) 37,25. aijana, n, (-= 8a.) »lack pigment. %-
vanna, mfn. bluck-colcured, gen. pl. wanam (kesdinam) 44,24.
efijali, m (= 8c.) the two palms joined; vcc, ~im paggayha, raising
(thair) joined hanis (as a mark of supplication) 22,4; ~.im
pagganhitva, id. (respectfully) 30,¢; vim pandmetva, ad. 74,20.
atiiia, pron. (st. anya) m. x0, f. wa, mn. wath, ace. mfn, wam, instr.
oR wena, gen. mn wassa, f wissi; pl. m. we, instr, mon. vehi, gon,
+n. wesam, Joc, mom. weBU. 1) cther, another (uct the same,
different or similar) 6,35, 7,8, 6l,aa, 74,3; 7.9 (wassa, opp. ekassa),
99,2 (~0, opp. 80 eva); Dh. 168 (Xam, opp. attanam), cp. Db. 252.
355; ajiio pi, 5,31; ~assa purisassa (a paramour) 9,13, ~am (se.
purisam, id.) 9,28; wena pariyayena, 9111 — wenikarena, 91,32 (in
another way 0: wrong); comp. aiia-purisam 48,12. — “) another, a
second, a new (by way of addition) 4,33, 18,9; ~ehi dvihi (still two)
34.9. — 5) the rest, the others (pl. & n. 89.) 33,16, 34,24; ~esu
divasesu (on the preceeding days) 13,10. 14 65,21; afifie satta
(other mortals) 62,25 ; n. aiifiam (everything else, opp. idam eva)
89,25. — *) with a negation: the only one, none but; ~o
gamanamaggo n’atthi, 3,14; «2 patittha n’atthi (thapetva tini
sarandni) 28,35. —°) pleonastically: ~amh sarnvaccharam (a whole
year) 33,17; ~am aphasukam n atthi (no sickness) 49,28, — °)
repeated: *) one, .. another (in different way) 67,39. 67,30. 99,10;
~wam jivam am sariram (opp. tat) 89.28, ep. Dh. 75. ») reciprocally:
one-another (one towards or with another etc.) ~o warm Dh, 165;
often comp.: aiiiamaihan, adv, 11,90, 1l.a7, 19,14. 33,2021. 74,5. *)
combined with other pron.: yo aifio (every other who) 34,21; ~am
kim (anything further) 41,7; na afiio koci (nobody else) 51,8; ~am
kijei kathetva (,,told some lie“) 53,9; ma ~wam kifici asamkittha
(,.you ought not to suppose that there is anything behind this“)
7,11; ~am kifica yathicchitam (,,every other service according to
your desire‘) 111,28, — cp. para, apara, itara, aiifatara. *aiifia-
khantika, (fn). (fr. affia + khanti) ,belonging to another faith“; instr.
m. wena (tava) 94.28, afifatara, pron. (compar. fr. ania, sa.
anyatara). ') a certain, some; m. wo 32,9; acc. wam 3,30; gen.
wassil 9,0; loc, wasinim 30,99; acc. f. am 30,28. — ?) one of a
certain number (w. gen, of the numeral) Dh, 137, 157, — 5)
another; gen, m. wassa purisassa (another man’s) 100,11; afifatara-
vesena 55,29 (,in disguise’ cp. vesa; perhaps we have to read:
afiiataka-° as 43,19). “afiia-titthiya, m(fn). (sa. anya + tirtha),
heretical; pi, .@, the heretics, 72,28; instr. wehi 74,9 ep. titthiya).
anhiiattha, adv. (sa, anyatra) else: where, to another place, 12,35,
49,15 (cp. next), aifatra, ')adv.(—prec.)elsewhere, except, save;
afiatra Tathagatassa
 {,save by the T.“, the gen. being due to the prec, tassa)
78,17, — *) prp. besides (w. acc.) 97,28. — *afiiatrayoga, m(fn).
having another discipline; instr, m, wena (taya) 94,27, {cp. yoga).
afifiathatta, nm. (sa, anyathatva cp. next) variation, difference;
warm 114,29, aniatha, adv, (sa,anyatha) otherwise. —
*anfathacariyaka, m(fn having another teacher (cp, acariya wena
(tayd) 94,27. *aiiila-ditthika, m(f). belonging to another sect (cp.
ditthi); ~ena {taya) 94,26, afiiamainiam, adv. v, afiia®) *aiifia-
rucika, m(fn). having another inclination (ep, ruci); wena (tayi)
94,26-27. aiid, f. (sa. aja) knowledge, — samma-d-aiiia-vimutta, mfn,
who has become free through perfect knowledge; gen. ~assa Dh,
96, pl. wanam, Dh, 57. (cp. ajanati). anhaya, aifasi, v. ajanati. atavi,
f. (= 8a.) u forest; Joe, ewiyath 30,50; ~i-mukhe ,on the outskirt of
a forest“ 30,29, (cp. mukha), atta}, mfn. (sa. arta, cp. attiyati, yard.)
afflicted, pained, suffering, — attassara, m. a cry of pain or distress,
man (acc.) 40,21 (cp. sara’). — vedanatta, mfn. oppressed by pain,
im, ~0 50,20, atta’, m. (su. artha, cp. attha? & attha®), case, cause,
lawsuit, litigation; acc, ~am 59,4; attatthaya (uparavo) on account
of litigations 42,30. — kutatta, false suit (g. v.). attaka, m. (dimin. fr.
atta, a watchtower, — 8a.) a tower, a platform; acc, ~ath 73,33. cp,
Morrie, JPTS. *86 104. *attiyati, vb, (also written attiyati or addh®-,
add, denom. fr. atta!, cp. yard & yrt) to feel annoyed or bored, to be
incommodated or tormented; part, f. ~mana 50,1. (cp. Morris, JPTS.
’86,104-05.]. y 15 atthi-karoti attha’, num, (sa, ashta-) eight. 1)
indecl, 23,22. 82,19. — #) comp, atthusabha-matta, mfn. of a
measure of 8 usabhas (q. ».) ~am thanam 27,27 (acc.). — satt’-
attha-divas’-accayena (seven or eight days) 35,1. (cp. atthafigika,
atthama, attharasama). attha?-attha! (q. v.) in the comp, *attha-
katha, f. a commentary, the commentary on the Buddhist holy
scriptures; nom. ~a (opp, Pali) 113,26; ace, wam 114,7; instr, waya
114,25, — comp. w, the prefix sa- (adj.) : satthakatha pali (the text
with the com. mentary) 102,3. — parittatthakatham (acc, a concise
or compendious come’ mentary) 113,24, — Sihalatthakatha (the
Sinhalese A.) 113,28; ace, pl, ~a& (sabba) 114,97. (cp, atta®).
*atthaiigika, efn. (fr. attha! + anga w. pref, -ka, cp. sa. ashtafiga)
consisting of 8 parts, eightfold; m: ~o (maggo) 67,3. 82,12. Dh,
273; aco, ~am (maggath) Dh, 191. atthama, m/fn. (8a. aslitama)
the eighth; m. ~o 103,28 (0: atthami (/.) sena Marassa). atthadrasa,
num. (sa, ashtadaca-) eighteen. — atthdrasama, m/n, (sa,
ashtadaca) the eighteenth; m, ~o (Malavaggo) Dh, XVIII, atthi, m.
(sa, asthi) 1) a bone; nom, ~i 13,11; coll, (bones) 82,3 == 97,20;
acc, wim 13,14; pl. ~ini Dh. 149; gen, ~inam Dh. 150, — *) the
stone of a fruit; wi 37,6; ace, -im 36,35; abl. ~wito 37,5, — atthi-
koti, f. the end of a bone; acc. ~im 13,20, ~ atthi-minja, f. (q. v.)
(ep. nezt). atthika, n. (sa. asthika) a bone; hanukatthikena (instr.) by
the jawbone, 40,18 (v. hanu(ka)). *atthi-karoti, ob. (perhaps fr.
artha, cp. 8a. kad-arthi- /kr, (Tr.)) to attend, to pay attention to
(synon, w. manasi-karoti, q. v.); ger. ~katva 71,2. [cp. Morris, JPTS.
’86,107; Fausb6ll, Sn. vol. II,38 (fr. sa. ashti (vac) yteaching“);
Windisch, Mara, p. 100 (= sa, asthamkrtva ,Acht geben“);
 atthi-miija Warren, Buddhism, p. 349 ,to be convinced +],
“atthi-mifija, f. (sa. *asthimajjan) the marrow of bones, 82,3 —
97,20, (cp. Morris, JPTS, '85,2v-80.} atthtsabha-matta, »v. atthal.
addha, m. én, (ulso written addha (q.v.), sa. ardha) a half, °-
nalikamatta, mfn. of the measure of a half nalika (q.v.), acc.m. wath
(tandulam) 57,18. — °-ratta-samaye (doc.) at midnight, 40,8. cp.
upaddha, diyaddha & next. addhatiya, m/n., (a shortened form of
addha-teyya, or from *addha-tatiya with elision of -ta- (like
viiianaica-, gq. v.)) two and a half; n. pl. ~ani (purisa-satani) 23,2, ~
addhatiyasata, mfn. 250%; m. acc, pl. ne (jane) 34,7. addhateyya,
m/fn, (a prakritic formation from sa. ardha-trtiya) two and a half, —
sata, mfn. 250%; m. pl. wa 21,31, ace. we 21,83. anu (or anu) mfn.
(— 8a.) fine, small (opp. thiila). *anumthila, (m/)n. small and large,
Dh. 409; ~am (saiiflojanam) Dh. 31; . pl. wani (papeni) Dh. 265.
anumatta, mfx. (sa. anu-matra small, atomic, m. ~0 pi (vanatho
yeven the smallest* Dh, 284 [anu-]; ace, ~am (dubbhasitat padam)
110,13; instr, n. wena (puiiiena) ,even the least (good work)“ 103,14
[but here the Birman realing anumatto (se. attho) ought to be
preferred]. anda, ”. (= sa.) an egg. °-bhiita, mn, (cp. bhavati)
fragile) weak; f, ~& (bhata bhar.ya) {rom her childhood“ 51,4, --
Andabhita-jitaka, m. 52,11. (cp. andha-bbuta), Z ati, indeci. (before
vowels usually acc-, v. accanta, avceya ete, = 6a.) preix') to verbs,
expressing , beyond, over“; #) to noun: ,,excessive(ly),
extraordinary(-ily), too much“ (== ativiya, gq. v.). : *ati-accheraka,
mfn wai (2) very wonderful thing, 3,22. 16 *ati-karuna, mfn. very
pitiable or miserable; m, «0 (ravo) 60,10; O-gara, m. (v. sara’), acc.
~al 27,14. i atikkama, m, (sa. ati-krama) overcoming, conquering,
acc. ~am (dukkhassa) ,,the destruction (of pain) “ 107,10 — Db,
191. atikkamati, vb. (sa. ati- ykram) 1) to pass, cross, *) to surpass,
overcome (w. acc.). part. m. pl. wanta 26,32; an-atikkamanto (m.)
not surpassing 0: accompagnying (gitassaram tantissarena) 19,32.
pot. 3. ag. weyya (sainojanam sabbam) Dh. 221. pp. n. pl.
atikkantani (tini sarhvaccharani) 21,11. ger. ~itva (samuddam) 26,2;
(simam) 39,18; atikamma (Kasiriittham) ,baving left’ 38,21. caus.
atikkameti (4. v.) atikkamana, » (sa. atikramae na) overstepping. —
“atikkamanaka, mfn. exceeding (w. acc.) : pannasaiifiam °-migo,
8,10. atikkameti, pr. (caus, atikkamati) to cause to pass or tc be
passed over; imp, 2. sg. ~ehi (mayham varam) 6,34. fut. 1. sg.
~essami (te varam) 7,2. *ati-khina, mfn. (fr.ati + khina, pp. Vkshi?)
destroyed, broken; capatikhina va (mt, pl.) like broken bows Dh.
156. ati-ga, mfn. (= sa.) overcoming, surmounting, mm, pafica-
saigitigo (bhikkhu) Dh, 370; ace, safigitigam, Dh. 397, utigacchati,
pr. (sa. ati- gam & \/ga) to overcome. aor. 3. 8g. acea-gama (mam)
75,%8; acc-a-ga (mos ham) Dh, 414. uti-giilha, mfn, (sa. ati-gaidha,
pp. Vgah) very tight or close, intensive; f. ~a (kappana) 65,21. *ati-
citra, mfn. (sa. *ati -+ citra) excellent, brilliant; n. pl, ~ ani
(pafhapatibhanani 98,ss. *ati-tutthi,/. (fr.8a.ati-+ tushti) extreme
joy; énstr. wiya 10,13. ati-dura, mfn. (- 8a.) very di.
 stant, too far; loc, n, (adv.) we 12,29, 83,2 (natidtre). *ati-
dhona-carin, mfn, ‘wandere ing in transgression’, sinful; acc. m.
~inam 106,20 — Dh. 240, (The etymology of this word is a little
doubtful, but it seems to be preferable to take it -~ *ati-dhavana-
carin (/dhav', to run), Dforris, JPTS. '87,100 and Franke, WZ, 1901
derive it from “dhona (pure, Vdhav? to wash) sa, dhauta: ,practising
impurity, transgressing purity“, ,der wider die Reinheit verstdsst"),
atipata, m, (— sa.) neglect, transgression, injuring, panatipata,
destroying life (q. 2.) *ati-bahala, mfn. (fr. ati + bahala) very thick; f.
A& yagu? ,is the rice-gruel thick enough?“ 56,29 (the questioner
seems to think that the rice-gruel is very thin or weak (natibahala)
and gets that enigmatical answer: udakam na laddham ,it has not
got any water“), *ati-bhagini-putta, m. (fr. ati + bhagini-putta, g.v.)
a very dear nephew (ironically), ~o 5,5. atimaiifiati, ob. (sa. ati-
\/man) to despise; pr. 3. ag. ~wati Dh. 366; pot, 3. 8g. ~eyya Dh.
365 (w. ace. salabham). *ati-manorama, mfn. (fr. ati + mano-rama,
g.v.) very charming; instr, n. wena (sirisobhaggena) 64,10. *ati-
mahanta. mfn. (fr. ati + mahanta (sa. mahat)) very great (big or
large); loc. m. natimahante (sare) 3,32. *atimapeti, vb. (caus. *ati-
mi (mapayati)) to injure, to destroy (acc.); pr. 3. 8g. ~eti Dh, 246
(panam). *ati-muduka, mfn. (fr. ati + muduka, g.v.) very soft, mild
or feeblo; m w~O (raja) 38,24. atirocati, vb. (sa. ati-yruc) to shine
forth; pr. 3. sg. wati Dh. 59. ativattati, vb. (8a. vr to overcome
(acc.); pr. 3. 8g. wati (dittham) 3,37. P4li Glossary. . 17 atta-ghaiiia
“ati-vasa, mfn, (fr. ati + vasa (sa. vaca)) thoroughly subject to or
dependent on (gen.); m. pl, wa (mama) Dh. 74, *ati-vaikya, mn. (fr.
ati + vakya, cp. sa. ati-/vac) abuse; acc, ~am Dh, 320. ativiya, adv.
(sa. ativa) very, excessively; ~stro hutva 38,31; ~dhammiko raja
39,8; ~pabbajjaya cittam nami 65,19. *ati-sitala, mfn, (fr. ati + sitala
ue citala)) very cold; m. ~o (aggi) 6,10. atiharati, vb. (sa. ati-hr) to
carry over, to bring; aor. 3. 8g. wri (dhuttam manavikaya santikam)
50,5. atita, mfn. (sa, pp. ati-yi) 1) past, passed away, dead; atita-
jati, f. a former existence, loc. wiyath 85,12; *atita-satthuka, mfn.
having no master more, ”. ~am pavacanam (,the holy word has no
announcer more“) 79,3; khanatita, mfn. who allows the right
Moment to pass, m. pl. ~& 108,7 = Dh. 315. — *) act, who has
neglected or transgressed, m. gen. ~assa (ekarb dhammam) 106,14
— Dh. 176, 5) subst, m. the past, an event of the past, a tale; doc.
(adv.) atite, formerly, in the times of past, 1,2, 2,17 ete; atitinagate
(opp. etarahi) in the past and in the future, 56,1 (cp. an-agata); acc.
~am ahari (told a tale of the past) 28,17. Atula, m. nom, pr. an
Upasaka; voc. ~a Dh. 227, atta}, mfn. (sa. atta, pp. a-yda) seized, v,
atta-danda, atta-mana. atta®, in comp. — attan (,self*) q. v. cp.
sayam. *atta-kilamatha, m. (fr. atta? +-kilamatha (sa. klamatha))
mortification; °-Anuyoga, mfn, given to mortification, m. ~ 66,27
(cp. anuyoga). *atta-gutta, mfn. (fr. atta? 4gutta (sa. gupta, pp.
Vgup)) selfprotected; m. ~o Dh. 379. “atta-ghaiiia, n. (fr. atta? +
ghaiiiia (cp. sa, ghanya, Vhan)) de2
 atta-ja struction of one’s self; dat. ~aya »to his own
destruction Dh, 164. atta-ja, mfa. (fr. atta? + ja, sa, dtmaja) born
from one’s self; n. «am (papam) Dh. 161. *atta-danda mfn. (fr. atta!
+ danda, q.v.) using the stick, violent (opp. ribbuta); m. ol, loc. ~esu
Dh. 406. *attadattha, m. (fr. atta? + attha!, with d eupuorically
inserted) one’s vwn advant+ge, what is useful to one’s self (with
regard to one’s moral improvement or to the development of one’s
spiritual faculties; opp. parattha. q.v.); acc, ~am Dh. 166 (cp. SBE. X
46). cp. sadattha-pasuta. “atta-danta, mfn. (fr. atta? + danta, vp.
dam) having tamed one’s aelf; m. ~o Dh. 822; gen. wassa (posassa)
Dh, 104. attan (in comp. atta-) m. ag. (sa. itman) ') the iadivicual
soul, self, person, the Hgo (the real existence of which is denied, cp.
puggala, namarips. jiva); nom. atta 55,2, Dh. 62. 104, 160; ‘atti
me'ti ,a so-called (imagined) myself“ or ,thinking that I have a soul*
96,18; énstr. uttana Dh, 161; attanad sudantena ,by his own
weiltamed self* Dh, 160. 323; abl, attand anno piyataro n’atthi
54,33. — *) instr. attana is frequently used like nom. (in apposition
to the grammatical subject) yhimself* (lit, ,by himself“) : 34,16
(attanipi) 34,29-95. 38,18, 42,1, 49,21. 54,8. Dh. 379. — 5) ace,
uttanam (contracted attaia) and the other oblique cases (esp. gen.
attano) are used as pron. reflex, referring to the gramm. subject in
all persons, genders, and numbers — myself (ourselves), yourself
(~selves), himself (herself, itself, one’s self, themselves), attanath: 3.
8g. 12,27. 64,31. 55,1 (attam) Dh, 159, 355 (attanaih metri cavsa)
379 (attam); 1. sg. 3,15. 27 22; 3. pl. 106,28 — Dh. 80. instr. attuna
: 3. sg. 17,4 (kata-kammath) 20,27 (mui uddhari); 2. sg. 29,3 = 18
(dinna-dane). gen. attano : 3. 89. 2,14. 10,5. 52,82. Dh. 160; 2. 89.
9,23. 12,85; 1. 8g. 7,9; 3. pl. 59. 73,243 2. pl. 17,1. 41,32, attano
attano (,each ... his own") 1411-14 (3. pl.); 41,2 (referring to the
gramm, object). — atta-vetanabhata, mfn, ,supporting one’s self by
one’s own earnings“ 105,5. — an-atta, mfn. destitute of a self (q.v.).
— ojittatta, mfn. having secured one’s self (v, ojita). — paccattarh,
adv. by one’s self (q.v.). — pahitatta. mfn, whose mind is intent upon
(v. pahita, cp. padhana). — bhavitatta, mfn. having trained one's self
(v. bhaveti). — attakilamatha ete. (qv.). — Atta-vagga, m. name of a
chapter of Dhammapada. Db, XII. atta-bhava, m. (fr, atta? + bhava,
sa. atmabhava) ') proper or peculiar nature, body, figure; acc. wain
62,29. 64,16. — 7) birth, existence; nom, ~o (paiicasatimo) 17.8;
paficasu Xsatesu in 500 of my former ex istences“ 17,7, atta-mana,
mfn. (fr. attat + manas, 8a. attamanas) joyful, delighted, happy; m.
~0 93,18. Dh, 328, f. wa 62.04. — an-attamana, mfn, displeased, m.
~0 74,30. atta-sambhava, m/fn. (fr. atta? -+ sambhava, sa.
atmasambhava) originating from one’s self; m. wath (papam) Dh.
161. *atta-hetu, adv, (fr.atta® + hetu (q. v.)) for one’s own sake.
Dh, 84 (upp. parassahetu). *attanuyogin, mfn. (fr. atta? + anuyogin)
who exerts himself in meditation, Dh, 209 (gen. pl. ~inath). attha},
m. (sa. artha) +) aim, purpose, sake, reason; instr, yen’ atthena
idhigato 103,13 (,,the reason for which you have come here“,
corresponding to the foll. attho (?); but »yena is probably an errer
for sena (sa. svenirthena)); dat, atthaya and acc, attham are
frequently used at the end of comp. (adv.) = ,for the sake of, on
account of, for“ : (dat.) 3.5,
 9,11, 15,30. 16,12. 21,38. 28,5, 32,39. 41,3, 42,50. 47,5.
58,1. 60,26. 111,29. (ace.) 8,7, 11,4, 21,3. 31,11. 57.93. 61,13.
62,31. 91,25; kimatthaya (,,why”) 33,1, kimatthath (do.) 3,12.
15,10. 33,8; dat. atthdya also separately (adv. w. gen.) : 49,14.
57,1, 60,14. 65,1. 108,21 (cp. 3) below). — ?) need, want, desire
(w, instr.) nom, ~0 18,9, 22,17-30, 33,2. 35,3-4. 55,15, 83,95,
103,14. 104,315 usirattha, mfn. ,be who wants Usira“ (q. v.) 108.4
(m. wo); ep, atthika & atthin. — 5) use, utility, advantage, gain,
wealth; acc. wath icchati 34,90; wath karissam 47,8; ~am anagatam
(pekkham) ,,foreseeing future advantage“ 112.4; bahinam vaya
(dat.) 108,21. — attha-samhita, mfr. useful, nm, wath 93,7; an-
attha-samhita, m/fn, & an-attha, m(fn). (v. h.); nir-attha(ka), mfn.
useless (q. v.); sattha (— sa-+ attha) v. appa-sattha & satthaka. cp.
attad-attha, m., parattha, m, & sadattha-pasuta, m/n. — 4) thing,
object, matter; acc. imam attham ,,this“ 2,8. 105,92; tam attham
,,the matter“ 7,1. 13,14; gen. imassa wassa 31,10; atthavasam
(acc.) ,the meaning of this“ (v. vasa) Dh, 289, — uttamattham (acc.)
a precious thing, 54,39, the best thing, Dh, 386 = 403. — 5) ==
atta ®, case, cause; acc.~am 101,9, Db, 256; loc. ~amhi Dh, 331.
S) sense, meaning, signification; ~o 52,7. 85,10. 89.2; ace. wath
90.30. 113,11-15; abl, (adv.) ~to (,according to the meaning*)
114,20. — attha-pada, nm. a word of sense (opp, Vaca anatthapada-
samhita) Dh. 100; antogadha-hetu-attha, mfn. containing a
causative meaning, ~am padam 85,9; paramatthato, adv. (abl.) ,in
the absolute sense“ 98,27 (cp. Paramatthadipani). For the comp.
attha-katha (a commentary) v. attha’, — “) the right, the truth; acc,
~am an-atthai ca, right and wrong Dh. 256; ~ath hitva, leaving the
real (aim of life) Dh. 209; in this sense attha is often opp. dhamma
(,,duty“) : ~am,dham19 atthi mafi ca, ll,jis. Dh. 363, cp. 58,25;
hence the name *attha-dhamminusasaka, mm. of a royal counsellor
or secretary (he must give the king information of what is ‘attha’ (0:
the real state of the case) and advice concerning the ‘dhamma’ (9:
what ought to be done)), a counsellor of right and justice, nom,
~0,37,36, attha? m, (sa. asta) disappearance, destruction; attham
(acc.) gacchati, to disappear, to cease, to perish, Dh, 226, 293. 384;
loc. suriye attham gate, at sunset 32,29. (cp. nezt), attha5, pr. 2 pl.
vo. atthi. *atthagama, m, (fr. attha® + gama) perishing, vanishing,
destruce tion; rupassa ~0 94,9, *atthafigama. m (fr. attharh, acc,
attha® + gama) — prec.; dat. ~waya (dukkha-domanassanat)
90,18. atthato, adv, (sa, arthatas) v. attha! (6). *attha-
dhamminusasaka, m, v, uttha? (7). *attha-pada, n. v. attha! (6).
*attha-vasa, m, (sa, *artha-vaga) v, attha! (4). *attha-samhita, mfn,
v, attha? 3). ( D eaietnae (sa, *artha-calini) xom, pr. name of a
commentary (by Buddhaghosa) on Dhamma-saiigani, the firat book
of the AbhidhammaePituka; acc. wim 113,23, atthi. ob. (sa. Vas, pr.
asti) to be, to exist; pr. 3, 8g. atthi 2,02, 96,16; n'atthi 3,14. 87,39;
atth’ 1,10. 43,20, 92,30. 2.89.$i 2,7-13, 3,12-18, 4,11. 98,13; asi
54,20. 88,9. 2. sg. amhi 12,11. 92,10; *mhi 4,4. 28,14. 45,4. 88,10;
asmi 16,12, 104,01; "smi 7,13. 49,99. 98,3. 3. pl. santi 11,14.
110,32, 2. pl. attha 21,9. 73,5 (attha ’ti), 1. pl. amha 21,3 (amha 'ti).
This verb is often used as copula with an adj, or subst. 2,7. 98,13,
and esp. with a pp, 2,18. 12,11. 21,s-9. 92,10 etc. "The 3. sg. atthi
is frequently used in the sense of ,to belong to“ (gen.): 12,1. 16,1-5.
105,11 Q*
 atthika e (atthi so, me), and this form may also be
combined even with the pl, of the subject (— santi) : 3,95. 12,1.
18,5. 43,9. 53,31. 82,2. 105,11. 109,11. Dh. 255 etc. tassa kira tam
divasam maranato mutti nina n’atthi, ,she could not be delivered
from death that day“ 87,53, — imp. 8. 8g. atthu : namo ty-atthu
homage to thee“ (voc.) 13,26. 108,11; dhi-r-atthu ,shame on*
103,53 (ace, jivitath),* 63,15 (gen. jatiya); astu (=~ sa. ustu) Lida,
— pot, 3. ay. wiyit (ae, wytit) B8u7, 7900, 104,145 sukki w yit ‘vould
bs possible’ 66,5; vattbabath w ,oughe to have been said“ 88,6; in
the phrase siya kho pana (w. pot. of the foll, verb) we have siyii
uxed adverbially like the Tata foraddene wit may De that’, 70,weun
Bowides siya we often find an older form avsa (sa. *usydt?) : tad
ussa (tw. dat. dukkhaya) 90,20 — bhaveyya 9117; avyikatam assa
92,6 foll. (ep. atha); suddho ass (silarukkho) 9,24; Dh, 124 (niissn)
260; ww, gen. tumbhukam evan. assa, (perhaps) you will think,
79,3; tatr’ assa ,suppose there were (in that town)" 90,32 (cp.
seyyacha), pot. 3. pl.assu (sa. *asyus) Dh. 74. — aor. Cimpf.) 1. sg.
asim 85,.5. 85,17 (,ain“ti == ahosim), 1')8,24, — part. ') sat, being;
Joc. sati (in loc. abs.) : examsera maritabbe sati (n.sg.), if (their)
death is necessary 5,24; mahdrajassa ruciyd. sati, ut the king’s
command 39,1; ditthiya sati, if you hold that view, 92,97-30; niccam
pajjalite sati, as (everything) is always burning, Db, 146. 2) santa,
mfn. m. ~0 13,29. 94,95; foc. n. sg. evar sante, in this case, 6,25,
99,7; evam sante pi, yet, notwithstanding this, 37,98, 44,28, 62,50;
loc. m. pl. ~esu (kbandhesu) 98,31 (,when the groups appear to
view“). °) samiina, mfn. m, ~o (andho) 25,15. (manussabhuto)
41,33. (puttho) 90,4. (vutto) 98,16-17; acc. m. pl. we (matte) 59,26.
The part. fr. atthi is frequently used as adj, v. sat, santa® (santaka)
« 20 samiina, (ep, wsal, aesanti), — atthie bhiiva, atthita & sotthi, g.
v. atthika, mfn. (fr. attha’, sa. arthika) wanting anything; _Tajjatthika,
mfn. who covets the kingdom, m. pl. w~& 29,17. (ep. atthin),
atthita, f. (fr. atthi, aa. ustitil) being, existence, reality (opp.
natthita); acc, wan ceva natthitafi ca. to be and not to be, 96,7;
(lokanirodham passato) yi loke wa 8% na hoti, (to him) thore is no
reality in existence (the world) 96,10. atthin, mfn. (fr. attha!, ea, ore
thin) desirous, wanting anything; v. mantatthin, vadatthin. (cp.
atthika). *atthi-bhava, m. (fr. atthi + bhava, q. 0.) existence; are,
wth (mnvnMn) Arius cattle Nati, lewd known this being the fact,
46,ze; ne no koci wam janati, nobody knows that we exist, 72,81.
atthu, imp. v. atthi. atha, indecl, (— sa.) 1) and, further, Dh, 55. *)
then, now aa the tule) 1.5. 3,15. dy18 (uth’), atha kho 66,3-5 etc.;
atha kena, why then? 54,97, 5) then (corresp. w. @ prec. yada),
66,21. 107.19-16 == Dh, 377-79. Dh, 69. 119-20. 384; (after prec.
pathamam:) Dh. 158. 4) but, 107,25 = Dh. 887, Dh, 85. 136; atha
kho leave on the contrary 90,36. 91,4; atha ca pana, but on the
other hand, 3,4 (cp. ca). cp. atho & next. athava, indecl, (<= 8a.) or
(corresp, w. prec, va, g. v.) Dh, 140. 271. atho, indecl. (= sa.) and,
also, likewise, Dh. 151. 234. 332. 423. aduth, pron. n. (sa, adas) v.
asu. addba = addha, half (q. v.); °-masaccayena, at the end of a
half month, 20,11; °-yojana, n. a half yojana (g. v.) 63,19. addhagu,
m. (fr. addhan + gu — ga, sa. adhva-ga) atraveller; nom. wu, Dh,
302 (sg. ¢ pl. 2) addhan, m, (sa. adhvan), a road, a journey, life-
time, time; acc. ~anam 44,01, 110,5. Dh. 207 (addhana). —
 *addba-gata, mfn. one who has accomplished his journey
0: old, m. ~o 74,21 — gataddhin, mfn. (q.v.). cp.addhika & prec,
addha, adv. (= sa.) certainly, truly; probably, 3,10. 60,20. *“addhika,
m(fn). (fr. addhan) travelling, a traveller; gen. pl. m.
kapanaddhikanamh, poor travellers, 38,14 (v, kapana). adhama,
mfn. (= sa. superl. fr. adho, q.v.) lowest, vilest; purisidhame (acc, m,
pl.) low people, Dh, 78, (ep. next.) adhara, mfn. (= sa. compar. fr,
adho, q.v.) lower. adharotthe (doc.) the lower jaw 13,19 (v. ottha.
ep. prec.). adhi, indecl. (= sa.) prefix to verbs & nouns expressing
‘above, over, on, at, to’; before vowels (except ,,i“) it takes the form
ajjh-, ¢. g, adhibhasati, aor. ajjhabhasi. adhika, mfn. (fr. adhi, — 8a.)
exceeding, superior, — compar. adhikatara, mfn, id.; n. sam (assum)
w. abl, (cutunnath samuddanam udakato) 89,14. adhigacchati. vb.
(sa. adhiVgam) ‘to go to’, to attain, obtain, find, understand (w.
acc.); pr. 3. 8g. ~wati (ratitn) Dh. 187, (samadhim) Db, 365; 3. pl.
wanti (siram) Dh. 11-22; pot. 3. 8g. adhigacche (padam santam)
Dh, 368, ~weyya (seyyam, one who is better) Dh. 61; aor, 3. 89.
(a)dbigd (attham), could not understand, 113,15; w. augm. ajjhaga
(tanhanarh khayam) Dh. 154; aor. 3. pl, ajjhagi (= ~gu) (vyasanam)
34,21, cond. 1, sg. otaram nadhigacchissam wl should never find
faults“, 104,19 (cp. upessam, vicarissam, v. upeti & vicarati; Pan, II],
2,119 & the use of the Greek éueddov). adhigama, m. (= 8a.)
attainment, acquisition; dat, ~aya(w.gen. iayassa) 90,18. *adhi-
citta, n.'the higher thought’, meditation; loc. we (ayogo) Dh. 185,
adhitthati, vd. (sa. adhi-/stha) 21 adhiseti 1) to stand (on); ger.
~aya 54,8. *) to practise, to perform, to devote oneself to (acc.); pr.
3. ag, ~ati (upayupadanam, q. v.) 96,12; ger, waya (uposathaigani)
61,7. adhitthana, a. (sa. adhishthana) 1) determination, resolution,
®) adhering to, clinging to the world, comp. w. the synon.
abhinivesa (being a paraphrase to upayupadana, gq. v.) :
adhitthanabhinivesinusayam (cetaso), that inclination (of the mind)
which consists in clinging to the world, 96,12 (cp. anusaya). adhipa,
m, (= sa.) a master, lord; v, adhipacca, *adhipanna, pp. (adhi-y/pad)
assailed, seized; gen. m. ~assa (antakena-° ,,whom death has
seized“) Dh, 288. *adhippaya, m. (fr. adhi-pra-yi, ep. sa, abhi-praya)
intention, meaning; nom, ~0, 114,6. *adhibhasati, vb. (adhi-bhas)
to speak to, to adress (acc,); aor, 3, 9, ajjhabhasi 77,3, adhimutta,
pp. (sa. adhi-mukta (Ymuc)) inclined to (tw. ace, or comp.) ; m.
vanidhimutto, who gives oneself to desires, Dh, 344 (cp. vana?);
gen, m, pl. ~ainam (nibbanam) ,,who strive after Nibbana“, Dh. 226.
adhivattha, pp. (fr. adhi-yvas) living, inhabiting (doc); f. wa, 5,19.
*adhivasana, n. (fr. adhivaseti) consent, acceptance of an invitation;
ace, wath, 70,11, *adhivaseti, vb, (caus, adhiVvas) 1) to wait, to
wait for; imp. 2. 8g, wehi, 53,25; 2. pl. wetha, 33,155 ger. wetva (w.
acc, dve savand) 11,5. ~ ) tv bear, endure (acc.); ger. wetva tayo
pahare) 55,15; aor. 3. sg. ~wesi tai ec, vedand) 78,95 — ajjhavasayi
vedanath) 80,34. — °) to consent; aor, ~wesi, 70,10 — 77,99; cap.
to accept an invitation to dinner (bhattarh) : imp, 3. ag. ~etu, 70,9
— 77,98, (cp. adhivasana). — caus, IJ: adhivasapeti, to cause to
wait; pr. 2. 3g. ~wesi, 33,17. adhiseti, vb, (sa, adhi-\/yi) to
 adbuna . lie upon (ace.); fut. 3. sg. waessati {pathavim)
107,5 == Dh. 41. adhuna, adv, (— sa.) vow, °-Agata, Bie a new-
comer} m. w0 (uyyanapalo} 15. edho, indecl. (ea, adhas) down (w.
acc.); adho Gufiga:h, down the river G. 14,24 (or perhaps better
comp. adhogafigam, adv. ?) — compar. adhara, mfn., superl,
adhama. mfn. (q. ¥.). an-, ana-, negative prefix, v. a-4. *Anagata-
vamsa, m, ‘history of the future’, name of a non-canonical Pali work
(,,the Buddhist Apocalypse“), from which an extract is given
102,228, Anathapindika, m. nom. pr. (— sa.) ‘giver of food to the
poor’, name of a rich merchant; gen. ~ussa, 71,20. anika, md’ n, (=
sa.) an army, balanika, mfn. q. v. anu!, indecl.'!== sa.) before
vowels except ,u“ usually ‘anv-’ (v. anvaya etc.), prefix to verbs and
nouns, expressing ‘after, along, near to, accord. ing to’ etc, Inserted
in a dvandvacomp. of the same word repeated, »,
khuddinukhuddaka (cp. pati). anu’, mfn. = anu (g. v.) cp. anumatta.
anukantati. ob. (sa. anu-vkrt. 6.) to cut facc.); pr. 3. sg. wati (-
attham) Da, 311. arukampa, f. ‘= sa.) compassion; instr, waya (w.
gen. tava) out of pity (for you) 55,4. anukkama, m, (sa. anu-krama)
succession, order; instr, adv, wena, gradua!ly, 38,22. 48,9; ti wena
,and so on by degrees" 34,8. sahanukkama, mfn. (q. v.).
anukkamati, vb. (sa. anu-/kram) tc follow, to go along (acc.); part.
med, m. ~mano (-patham) 90,84. anukhuedaka, mfn. v,
khuddinukhuddaka. anigi, wfn. (se. anu-ga) follows ing; sattimacca-
satiauga, mfn. followed by 700 companions, 110,23 (an. ~0). 22
Yanugacchati, vb. (ea. anu-/gam) to follow (acc.); aor. 3. 39.
~gamasi tah yeva) 68,83; w. augm,. anv-a-ga eer 111,3. anuggaha,
m. (sa, anu-graha) favour, kindness, help, assistance; acc. wath
6,86, ; anucara, m, (= sa.) a companion, follower. — sdnucara. mfn,
v. ea’. anucinna, mfn, (sa. anu-cirna, pp. anu-y/car) having attained
(acc.); m. pl. ~& (samadhijhanarh) 109,21. *anucchavika, mfn. (fr.
anu + chavi) suitable, fit; m. wo (w. inf.) 24.24; (w. gen. pers.) 25,3
(rafiiio). anujanati, vb. (sa. anu-yjfia). 1) to permit, allow; pr. 1. 8g.
~ami (ekena (bhikkhuna) dve samanere upatthapetum) 81.16. 7) to
prescribe (acc.) 81,z0 (dasu sikkhapudani). (cp. next.) anufidta,
myn. (pp. anujanati, 8a, anu-jiidta) permitted, allowed, having
attained the permission of (instr.); m. wo (GtTh(1)) Lh, Lb yr9-a8;
m, pl. w@ (rani) 73,24. *anuniatatta,n.(sa.*anu-jfatatva) the being
permitted; abl. Aa, 11,12 (,,granting bim leave to speak").
anutappati, vb, pass, (sa. anutapyate, tap) to suffer, to repent; pr. 3.
sg. wati Dh. 67, 314. (cp. tapati?.) *anutire, adv. (fr. anu! + tira
(Joc.)) near the banks of a river (gen.) 104,21. *anutthunati, ob (fr.
*anuystan) to deplore, bewail (acc.); part. m, sg. wunath (puranam)
Dh, 166 (= anutthunanta (pl.) Comm.), The discordance between
the sg. anutthunam and the pl. of the verb is probably due to the
fact that senti has been influenced by capa-'tikhinad (like jhayanti in
the preceeding verse); cp. also the use of sg. anutappati Dh. 314.)
ep. Tr. PM. 76,10. *anudday a, f. compassion, mercy; in comp. this
word generally takes the form anuddaya- (cp. mutta): khanti 
 metta-’nuddaya-sampanna, m/n. (q.0.) 7,12. 38,15. (fr.
*anu + daya, although it is generally spelt with double ‘d’, perhaps
from analogy with niddaya?), *anu-dhamma-carin. mfn, liv ing
according to the law; m, ~1 Dh, 20 (cp. dhamma-carin). anudhavati,
vb. (sa, anu-V/dhav') to follow, pursue, seek (acc.); pr, 3. sg. ~ati
(tiram) Dh, 85; aor, 2, sg. anu-dhavi (kalikam) 47,10,
anupakkamena, 2, upakkama). *anupakhajja, ger. encroaching on
(ace,) 88,83 (there bhikkhia), This word seems to be ger. fr, *anu-
praVskand (-skadya) = to enter together with, disrespectfully
pushing oneself forward (= anu-pavisati, comm.), Hence the vb.
denom. anupakbajjati (Vin. V_ 163,4), Morris, JPTS. ‘86,115,
’89,201, derives it from y/khad. anupatati, vb. (sa, anu-ypat) to run
after, to follow (acc.); pr. 3. sg. ~anti (sotam) Dh, 347; pp. ~wita,
fullowed, m. dukkhanupatito. Dh. 302, pl.dukkhanupatit’ (0: ~a@
addhagu) ib. anuparigacchati. vb, (sa. anaupari-/ga) to walk (fly)
round (acc.); aor. 3, sg. anu-pariy-aga (pasanam) 104,13.
*anupariyaya, m. (fr. anu-pariVi) going round along; °patha, m. acc.
~am 90,33 = anupariyaya-namakam maggam, 91,28 (the path
round the town). *anupassin, mfn. (fr. anu-ypac) looking after,
looking for; para-vajja-°, looking after the faults of others, Dh, 253
(gen. m, ~wissa); subha-°, looking for pleasures. Db, 7 (acc, m,
~ith), Dh. 349 (gen. m. wino). anupucchati, wb. (sa. anuyprach) to
inquire after (ace.); pr. 2. ag. ~wasi (jivath) 103,17, anupubba, mfn.
(sa. anu-purva) regular; instr. adv, wena, gradually, by and by, in
course of time, 18,11. 37,20, 42,24. 81,8. 87.4. Dh, 239.
*anupubbikathd, f. (fr. prec, an- (cp, 23 anumodana + katha, g.v.) a
regulated exposition; acc. ~ath kathesi ,preached in due course“
68,19, anuppatta, pp. (sa, anu-prapta, anu-pra-V/ap) arrived to,
having reached, having attained (acc.); m, wo (vayo) 74,21,
(Lafikam) 110,23. acc, ~am_(uttamattham) Dh, 386, loc, we
(Alavim). anubandhati, vb. (sa, anuVbandh) to follow, to pursue
(acc.); aor, 8.89. wi 11,19. 12,98; 1,89. im 104,115 ger. witva 33,18,
anubodha, m, (== 8a.) comprehension, understanding, — dur-
anubodha, min. q. v. *anubriiheti, ob. (sa. *anuVvrnh) to ‘increase’,
to devote oneself to (acc.); pot. 3. 8g. waye (vivekarn) Dh. 76 (cp.
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