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Handbook of Intelligent Computing and Optimization for Sustainable Development [Kõva köide]

Edited by (Punjabi University, Patiala, Punjab, India), Edited by (UOW Malaysia KDU Penang University College, Malaysia), Edited by (Federal Scientific Agroengineering Center, VIM, Moscow, Russia), Edited by (Universiti Teknologi PETRONA), Edited by (NWU, Mafikeng Campus, South Africa)
  • Formaat: Hardback, 944 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 25-Mar-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119791820
  • ISBN-13: 9781119791829
Teised raamatud teemal:
Handbook of Intelligent Computing and Optimization for Sustainable DevelopmentSuurem pilt
  • Formaat: Hardback, 944 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 25-Mar-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119791820
  • ISBN-13: 9781119791829
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119791829.html
Märksõnad:
HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries.

Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions.

The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare.

Audience

It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.
Foreword xxxi
Preface xxxv
Acknowledgment xlv
Part I: Intelligent Computing and Applications 1(322)
1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models
3(10)
Souvik Das
Kintada Prudhvi
J. Maiti
1.1 Introduction
3(1)
1.2 Data Acquisition Method
4(1)
1.2.1 Data Acquisition Experiment
4(1)
1.3 Feature Extraction
4(1)
1.4 Deep Learning Models
5(3)
1.4.1 Artificial Neural Network
5(1)
1.4.1.1 Training of a Neural Network
6(1)
1.4.2 Bernoulli's Restricted Boltzmann Machines
7(1)
1.5 Results
8(2)
1.6 Discussion
10(1)
1.7 Advantages and Disadvantages of the Study
11(1)
1.8 Limitations of the Study
11(1)
1.9 Conclusion
11(1)
References
12(1)
2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates
13(36)
Mandrita Mondal
Kumar S. Ray
2.1 Introduction
13(2)
2.2 Biological Neurons
15(2)
2.3 Artificial Neural Networks
17(5)
2.3.1 McCulloch-Pitts Neural Model
17(1)
2.3.2 The Perceptron
18(1)
2.3.3 ANN With Continuous Characteristics
18(2)
2.3.4 Single-Layer Neural Network
20(1)
2.3.5 Multilayer Neural Network
20(1)
2.3.6 Learning Process
21(1)
2.4 DNA Neural Networks
22(6)
2.4.1 Formation of Axon by DNA Oligonucleotide and Generation of Output Sequence
23(1)
2.4.2 Design Strategy of DNA Perceptron
24(1)
2.4.2.1 Algorithm
25(1)
2.4.2.2 Implementation of the Algorithm
27(1)
2.5 DNA Logic Gates
28(17)
2.5.1 Logic Gates Using Deoxyribozymes
29(1)
2.5.1.1 Catalytic Activity of Deoxyribozyme
30(1)
2.5.1.2 Controlling Deoxyribozyme Logic Gate
31(1)
2.5.1.3 YES Gate
32(1)
2.5.1.4 NOT Gate
33(1)
2.5.1.5 AND Gate
33(1)
2.5.1.6 ANDANDNOT Gates
34(1)
2.5.2 Enzyme-Free DNA Logic Circuits
35(1)
2.5.2.1 Construction of Enzyme-Free DNA Logic Gate
36(1)
2.5.2.2 DNA Logic Circuits
38(1)
2.5.3 DNA Logic Circuits Using DNA Polymerase and Nicking Enzyme
39(1)
2.5.3.1 AND Reaction
39(1)
2.5.3.2 OR Reaction
41(1)
2.5.3.3 PROPAGATE (PROP) Reaction
42(1)
2.5.4 Applications of DNA Logic Gate
43(1)
2.5.4.1 Playing Tic-Tac-Toe by DNA
43(1)
2.5.4.2 Medical Application of the Concept of DNA Logic Gate
45(1)
2.6 Advantages and Limitations
45(2)
2.7 Conclusion
47(1)
Acknowledgment
47(1)
References
47(2)
3 Intelligent Garment Detection Using Deep Learning
49(20)
Aniruddha Srinivas Joshi
Savyasachi Gupta
Goutham Kanahasabai
Earnest Paul Ijjina
3.1 Introduction
49(1)
3.2 Literature
50(2)
3.3 Methodology
52(7)
3.3.1 Obtaining the Foreground Information
52(1)
3.3.1.1 GMG Background Subtraction
54(1)
3.3.1.2 Person Detection
54(2)
3.3.2 Detection of Active Garments
56(1)
3.3.3 Identification of Garments of Interest
57(1)
3.3.3.1 Centroid Tracking
57(1)
3.3.3.2 Pose Estimation
57(1)
3.3.3.3 Calculation of Confidence Score
59(1)
3.4 Experimental Results
59(5)
3.4.1 Dataset Used
60(1)
3.4.2 Experimental Results and Statistics
60(3)
3.4.3 Analysis of the Proposed Approach
63(1)
3.5 Highlights
64(1)
3.6 Conclusion and Future Works
65(1)
Acknowledgements
65(1)
References
66(3)
4 Intelligent Computing on Complex Numbers for Cryptographic Applications
69(12)
Ni Ni Hla
Tun Myat Aung
4.1 Introduction
69(1)
4.2 Modular Arithmetic
70(1)
4.2.1 Introduction
70(1)
4.2.2 Congruence
70(1)
4.2.3 Modular Arithmetic Operations
70(1)
4.2.4 Inverses
70(1)
4.3 Complex Plane
71(1)
4.3.1 Introduction
71(1)
4.3.2 Complex Number Arithmetic Operations and Inverses
71(1)
4.4 Matrix Algebra
71(2)
4.4.1 Introduction
71(1)
4.4.2 Matrix Arithmetic Operations
72(1)
4.4.3 Inverses
72(1)
4.5 Elliptic Curve Arithmetic
73(1)
4.5.1 Introduction
73(1)
4.5.2 Arithmetic Operations on E(GF(p))
73(1)
4.6 Cryptographic Applications
74(4)
4.6.1 Hill Cipher
74(1)
4.6.2 Elliptic Curve Cryptography
74(1)
4.6.2.1 Elliptic Curve Encryption Scheme
76(1)
4.6.2.2 Elliptic Curve Signature Scheme
77(1)
4.6.3 Quantum Cryptography
77(1)
4.7 Conclusion
78(1)
References
79(2)
5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies
81(20)
Satyendra Singh Yadav
Shrishail Hiremath
Pravallika Surisetti
Vijay Kumar
Sarat Kumar Patra
5.1 Introduction
82(1)
5.2 Machine/Deep Learning for Future Wireless Communication
83(4)
5.2.1 Automatic Modulation Classification
84(1)
5.2.2 Resource Allocation (RA)
85(1)
5.2.3 Channel Estimation/Signal Detection
86(1)
5.2.4 Millimeter Wave
86(1)
5.3 Case Studies
87(8)
5.3.1 Case Study 1: Automatic Modulation Classification
87(1)
5.3.1.1 System Model
87(1)
5.3.1.2 CNN Architectures for Modulation Classification
88(1)
5.3.1.3 Results and Discussion
90(1)
5.3.2 Case Study 2: CSI Feedback for FDD Massive MIMO Systems
91(1)
5.3.2.1 Proposed Network Model
92(1)
5.3.2.2 Results and Discussion
93(2)
5.4 Major Findings
95(1)
5.5 Future Research Directions
95(1)
5.6 Conclusion
96(1)
References
96(5)
6 Designing of Routing Protocol for Crowd Associated Networks (CrANs)
101(34)
Rabia Bilal
Bilal Muhammad Khan
6.1 Introduction
101(2)
6.1.2 Challenges
102(1)
6.1.2.1 Limitation of Research
102(1)
6.2 Background Study
103(14)
6.2.1 AdHoc Network
103(1)
6.2.2 WSN
103(1)
6.2.3 MANETs
104(1)
6.2.4 VANETs
104(1)
6.2.5 FANETs
104(1)
6.2.6 Overview of Routing Protocols
104(1)
6.2.6.1 MANET
104(1)
6.2.6.2 VANET
107(1)
6.2.6.3 FANETs
108(1)
6.2.6.4 MANETs
110(1)
6.2.6.5 VANETs
110(1)
6.2.6.6 FANETs
111(1)
6.2.7 Mobility Model
112(1)
6.2.7.1 MANETs
112(1)
6.2.7.2 VANETs
112(1)
6.2.7.3 FANETs
112(2)
6.2.8 Types of Communication
114(1)
6.2.8.1 MANET
114(1)
6.2.8.2 VANET
114(1)
6.2.8.3 FANET
115(1)
6.2.9 QOS
116(1)
6.2.9.1 Packet Loss
116(1)
6.2.9.2 Bandwidth
116(1)
6.2.9.3 Data Rate
116(1)
6.2.9.4 Frequency
116(1)
6.2.9.5 Packet Delivery Ratio (PDR)
116(1)
6.2.9.6 Bit Noise Ratio
116(1)
6.3 CrANs
117(6)
6.3.1 Structure of CrANs
117(1)
6.3.2 Routing
118(1)
6.3.2.1 Single Hop
118(1)
6.3.2.2 Multiple Hop
119(1)
6.3.3 Classification of Routing Protocol
119(1)
6.3.3.1 Location Base Protocol
119(1)
6.3.3.2 Route Discovery
120(1)
6.3.3.3 Protocol Operation
120(1)
6.3.4 Challenge
120(1)
6.3.4.1 Latency
120(1)
6.3.4.2 Reliability
121(1)
6.3.4.3 Security
121(1)
6.3.4.4 Power Consumption
121(1)
6.3.4.5 Energy
121(1)
6.3.5 Applications
121(1)
6.3.5.1 Monitoring
121(1)
6.3.5.2 Control and Automation
121(1)
6.3.5.3 Safety
122(1)
6.3.5.4 Nuclear Power Plants
122(1)
6.3.5.5 Deserts
122(1)
6.3.5.6 Recovery Structure
122(1)
6.3.5.7 Earth Quake
122(1)
6.3.5.8 Road Blasts
123(1)
6.4 Simulation of MANET Network
123(3)
6.4.1 Deployment Area
123(1)
6.4.2 Divide Deployment Area Into Equal Zones
123(1)
6.4.3 Getting Positions of the Sensor Nodes
124(1)
6.4.4 Mesh Formation
124(1)
6.4.5 Getting the Minimum Spanning Tree for the Whole Placement Area
124(1)
6.4.6 Energy Calculation
124(1)
6.4.7 Average Delay
125(1)
6.4.8 Throughput
126(1)
6.5 Simulation of VANET Network
126(4)
6.5.1 Placement of Nodes
126(1)
6.5.2 Sender Node and Receiver Node
126(1)
6.5.3 Euclidean Distance Between Two Coordinates
126(1)
6.5.4 Separation of Faulty Nodes
127(1)
6.5.5 Best Match of the Node
127(1)
6.5.6 Cases of Simulation
128(1)
6.5.7 Delay
128(1)
6.5.8 Packet Delivery Ratio
128(1)
6.5.9 Throughput
129(1)
6.6 CrANs
130(2)
6.6.1 Deploy of Nodes
130(1)
6.6.2 Info Transfer 1
131(1)
6.6.3 Calculate the Fitness Function
131(1)
6.6.4 Routing Nodes
131(1)
6.7 Conclusion
132(1)
References
132(3)
7 Application of Group Method of Data Handling-Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane
135(14)
Anirban Banik
Mrinmoy Majumder
Sushant Kumar Biswal
Tarun Kanti Bandyopadhyay
7.1 Introduction
135(3)
7.1.1 Motivation, Background and Literature Review
136(1)
7.1.2 Novelty and Objective of the
Chapter
137(1)
7.1.3 Research Contribution
137(1)
7.1.4 Organization of the
Chapter
138(1)
7.2 Experimental Procedure
138(1)
7.3 Methodology
139(3)
7.3.1 Generation of Data
139(1)
7.3.2 Group Method of Data Handling-Based Neural Network
139(1)
7.3.3 Artificial Neural Network
140(1)
7.3.4 Normalization of the Data
141(1)
7.3.5 Analysis of Error
141(1)
7.3.6 Advantages and Disadvantages of the Study
141(1)
7.4 Results and Discussions
142(4)
7.4.1 Development of GMDH Model
142(2)
7.4.2 Analysis of Error
144(1)
7.4.3 Comparative Study
145(1)
7.4.4 Sensitivity Analysis
145(1)
7.4.5 Major Findings
145(1)
7.5 Conclusions
146(1)
7.5.1 Limitation
146(1)
7.5.2 Future Research Direction
147(1)
Acknowledgements
147(1)
References
147(2)
8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach
149(22)
M. Sunil Kumar
A. Harika
C. Sushama
P. Neelima
8.1 Introduction
149(4)
8.1.1 Requirements Descriptions are as Follows
150(1)
8.1.1.1 Business Requirements
150(1)
8.1.1.2 Requirements of Users
150(1)
8.1.1.3 System Requirements
150(1)
8.1.1.4 Functional Requirements
150(1)
8.1.1.5 Non-Functional Requirements
150(1)
8.1.2 Examples of Non-Functional Requirements
151(1)
8.1.2.1 Benefits of Non-Functional Requirements
151(1)
8.1.2.2 Drawbacks of Non-Functional Requirements
151(2)
8.1.3 Problem Statement
153(1)
8.1.4 Research Objectives
153(1)
8.1.5 Scope of Work
153(1)
8.2 Literature Survey
153(3)
8.3 Methodology
156(9)
8.3.1 Search String Planning
156(5)
8.3.2 Classifier Configuration
161(1)
8.3.2.1 Supervised Machine Learning
163(1)
8.3.2.2 Supervised Learning Algorithms
163(2)
8.4 Dataset
165(1)
8.5 Evaluation
166(3)
8.6 Conclusion
169(1)
References
170(1)
9 Image Classification by Reinforcement Learning With Two-State Q-Learning
171(12)
Abdul Mueed Hafiz
9.1 Introduction
171(2)
9.2 Proposed Approach
173(1)
9.3 Datasets Used
174(2)
9.3.1 ImageNet
174(1)
9.3.2 Cats and Dogs Dataset
175(1)
9.3.3 Caltech-101
176(1)
9.4 Experimentation
176(2)
9.5 Conclusion
178(1)
References
178(5)
10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process
183(36)
Pankaj Mohindru
Pooja
Vishwesh Akre
10.1 Introduction
184(3)
10.1.1 Programmable Logic Controller
184(1)
10.1.1.1 Merits of PLC Over Relay Logic
185(1)
10.1.1.2 Various Modules of PLC
185(1)
10.1.1.3 Working of PLC
187(1)
10.2 Distributed Control System
187(5)
10.2.1 DCS Evolution
188(1)
10.2.2 DCS Provisions
188(1)
10.2.3 Main Components of DCS
188(2)
10.2.4 Different Hierarchy Levels of DCS
190(1)
10.2.5 Advantages of DCS
191(1)
10.3 Fuzzy Logic
192(1)
10.4 Artificial Neural Network
193(1)
10.4.1 Artificial Neural Network's Framework
194(1)
10.5 Neuro-Fuzzy
194(3)
10.5.1 Neural Network, Fuzzy Logic, and Neuro-Fuzzy Concepts
194(1)
10.5.2 Design of Neuro-Fuzzy Controller
195(1)
10.5.3 Software Used
195(2)
10.6 Case Study
197(6)
10.6.1 Objective
197(1)
10.6.2 Description
197(1)
10.6.3 Formulation of Control Problem
198(1)
10.6.4 Formulation of Control Strategy
198(1)
10.6.5 Level of Instrumentation in Case Study
199(4)
10.7 Software Implementation on Graphical User Interface
203(9)
10.8 Results and Discussion
212(2)
10.9 Discussion
214(1)
10.10 Conclusion
214(1)
10.11 Scope for Future Work
215(1)
References
215(2)
Appendix 10.1 MATLAB Simulation Configuration Using Sugeno
217(1)
Appendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model
218(1)
Appendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model
218(1)
11 Artificial Neural Network in the Manufacturing Sector
219(30)
Navriti Gupta
11.1 Introduction
219(2)
11.2 Optimization
221(2)
11.3 Artificial Neural Network: Optimization of Mechanical Systems
223(5)
11.3.1 Injection Molding Processes
224(1)
11.3.2 Additive Manufacturing: 3D Printing
225(1)
11.3.3 Welding
226(1)
11.3.4 Foundry and Casting Technology
226(1)
11.3.5 Machining
227(1)
11.4 ANN vs. Human Brain
228(1)
11.5 Architecture of Artificial Neural Networks
229(6)
11.5.1 Modular Neural Networks
229(1)
11.5.2 Feed Forward
230(1)
11.5.3 Convolutional Neural Network
231(1)
11.5.4 Recurrent or Feedback Neural Network
231(2)
11.5.5 Radial Neural Network
233(1)
11.5.6 Multilayer Perceptron
233(1)
11.5.7 Kohonen Self-Organizing Neural Network
234(1)
11.5.8 LSTM
235(1)
11.6 Learning Algorithm(s)
235(2)
11.6.1 Conjugate Gradient
236(1)
11.6.2 Quick Propagation
236(1)
11.6.2.1 Levenberg-Marquardt Method
237(1)
11.7 Different Type of Data
237(1)
11.8 Case Study: Hard Machining of EN 31 Steel
238(4)
11.8.1 Design of Experiments
238(1)
11.8.2 Construction of ANN Feed Forward Model
238(1)
11.8.2.1 5-5-1 Feed Forward Network
239(1)
11.8.2.2 ANN Predicted Values vs. Experimental Values
239(1)
11.8.3 Major Findings of Above Study
240(2)
11.9 Advantages of Using ANN in Manufacturing Sectors
242(1)
11.10 Disadvantages of Using ANN in Manufacturing Sectors
242(1)
11.11 Applications
242(1)
11.12 Conclusions
243(1)
11.13 Future Scope of ANN in Manufacturing Sectors
244(1)
References
245(4)
12 Speech-Based Multilingual Translation Framework
249(12)
Saloni
Williamjeet Singh
12.1 Introduction
249(1)
12.2 Literature Survey
250(2)
12.3 Phases of ASR
252(1)
12.4 Modules of ASR
253(1)
12.5 Speech Database for ASR
253(2)
12.5.1 Data Collection
254(1)
12.6 Developing ASR
255(1)
12.7 Performance of ASR
256(1)
12.7.1 Accuracy
257(1)
12.7.1.1 Word Error Rate (WER)
257(1)
12.7.2 Speed
257(1)
12.8 Application Areas
257(1)
12.9 Conclusion and Future Work
258(1)
References
258(3)
13 Text Summarization: A Technical Overview and Research Perspectives
261(26)
Korrapati Sindhu
Karthick Seshadri
13.1 Introduction
262(1)
13.1.1 Motivation
262(1)
13.1.2 Challenges in ATS
263(1)
13.2 Summarization Techniques
263(16)
13.2.1 Feature-Based Summarization Approaches
264(1)
13.2.1.1 Word Level Features
264(1)
13.2.1.2 Sentence Level Features
266(1)
13.2.1.3 Document Level Features
268(1)
13.2.2 Indicator Representation-Based Approaches
269(1)
13.2.2.1 Graph-Based Approaches
270(3)
13.2.3 Machine Learning-Based Approaches
273(1)
13.2.3.1 Naive Bayes Methods
273(1)
13.2.3.2 Decision Tree-Based Methods
274(1)
13.2.3.3 Neural Network-Based Approach
275(1)
13.2.4 Deep Learning-Based Approaches
275(2)
13.2.5 Semantic-Based Approaches
277(2)
13.3 Evaluating Summaries
279(2)
13.3.1 Intrinsic Evaluation Measures
279(1)
13.3.2 Extrinsic Evaluation Measures
280(1)
13.4 Datasets and Results
281(1)
13.5 Future Research Directions
281(1)
13.6 Conclusion
282(1)
References
282(5)
14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery
287(18)
Sitendra Tamrakar
B.K. Madhavi
V. Mohan
14.1 Introduction
287(2)
14.1.1 Organization of
Chapter
289(1)
14.2 Literature Review
289(2)
14.3 Understanding the Google Cloud Platform
291(3)
14.3.1 Outline of CLOUD Platforms Provided by Google
292(1)
14.3.2 Advantages of Google Cloud Platform
293(1)
14.3.3 Key Characteristics of Google Cloud Platform
293(1)
14.4 Using BigQuery in the Google Cloud Console
294(1)
14.5 Sentiment Analysis
294(1)
14.6 Turning to Google BigQuery Analysis
295(2)
14.6.1 Architecture
296(1)
14.7 Proposed Method
297(3)
14.7.1 Sub-Modules
298(1)
14.7.1.1 Downloading Hashtag Data From Twitter Streaming API
298(1)
14.7.1.2 Stream Twitter Data into BigQuery With Cloud
298(1)
14.7.1.3 Deploying the Application on Google App Engine
299(1)
14.7.1.4 Querying and Loading Large Sets of Tweets onto BigQuery
299(1)
14.7.1.5 Review Tweet Dataset as Positive, Neutral, and Negative and Employ NLP
300(1)
14.8 Experimental Setup and Results
300(2)
14.8.1 Requirements
300(1)
14.8.2 Configuration and Setup
301(1)
14.9 Conclusion
302(1)
References
303(2)
15 A Review of Topic Modeling and Its Application
305(18)
R. Sandhiya
A.M. Boopika
M. Akshatha
S.V. Swetha
N.M. Hariharan
15.1 Introduction
305(1)
15.1.1 Linguistic
306(1)
15.1.2 Computer Science
306(1)
15.2 Objective of Topic Modeling
306(1)
15.3 Motivations and Contributions
307(1)
15.4 Detailed Survey of Research Articles
308(11)
15.4.1 Methodology-LDA
308(1)
15.4.1.1 Generative Process for LDA
308(1)
15.4.2 A Brief Summary of Articles Based on Latent Dirichlet Allocation
309(1)
15.4.2.1 Topic Analysis of Climate Change News
309(1)
15.4.2.2 Evaluation of Document Clustering and Topic Modeling in Online Social Networks: Twitter and Reddit
310(1)
15.4.2.3 Topic Modeling on Historical Newspaper
310(1)
15.4.2.4 Data Analysis and Visualization of Newspaper Articles on Thirdhand Smoke
311(1)
15.4.2.5 Hierarchical User Profiles for Interactive Visual User Behavior Analytics
311(1)
15.4.2.6 Front Page News Selection Algorithm
312(1)
15.4.2.7 Identifying Spatial Interaction Patterns of Vehicle Movements on Urban Road Networks by Topic Modeling
312(1)
15.4.2.8 Identifying Topic Relations in Scientific Literature Using Topic Modeling
313(1)
15.4.2.9 Probabilistic Topic Decomposition of an Eighteenth-Century American Newspaper
313(1)
15.4.3 A Brief Summary of Articles Based on Support Vector Machines
314(1)
15.4.3.1 Predicting Personality Traits of Chinese Facebook Posts
314(1)
15.4.3.2 Crowdsourcing the Character Level Networks Using Text
314(1)
15.4.4 A Brief Summary of Articles Based on Visualization Techniques
315(1)
15.4.4.1 Termite: Visualization Techniques for Assessing Textual Topic Models
315(1)
15.4.5 A Brief Summary of Articles Based on Gibbs Sampling
316(1)
15.4.5.1 Scan Order in Gibbs Sampling
316(1)
15.4.5.2 Incorporating Non-Local Information Into Information Extraction Systems by Gibbs Sampling
316(1)
15.4.6 A Brief Summary of Articles Based on LDA and Markov Chain Monte Carlo Algorithm
316(1)
15.4.6.1 Finding Scientific Topics
316(1)
15.4.7 Other Related Research Articles
317(1)
15.4.7.1 Reading Tea Leaves: Human Interpretation of Topic Models
317(1)
15.4.7.2 Mining Hidden Knowledge for Drug Safety Assessment: A Case Study
317(1)
15.4.7.3 The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
317(1)
15.4.7.4 Analysis of Publication Activity of Computational Science Society
318(1)
15.4.7.5 Studying the History of Ideas Using Topic Models
318(1)
15.5 Comparison Table of Previous Research
319(1)
15.6 Expected Future Work
320(1)
15.7 Conclusion
320(1)
References
321(2)
Part II: Optimization 323(138)
16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification
325(14)
Fasanya
Oluwafunmibi O.
Adediran
Adetola A.
Ewemooje
Olusegun S.
Adebola
Femi B.
16.1 Introduction
325(1)
16.1.1 ROC Curve
326(1)
16.2 Related Works
326(2)
16.3 Methodology
328(6)
16.3.1 Dataset
330(1)
16.3.2 Classification
330(1)
16.3.2.1 Naive Bayes
330(1)
16.3.2.2 KNN
331(1)
16.3.2.3 Support Vector Machine
331(1)
16.3.2.4 Artificial Neural Network
332(1)
16.3.3 Performance Metrics
333(1)
16.4 Results and Discussion
334(3)
16.4.1 Performance Comparison
336(1)
16.5 Conclusion
337(1)
References
338(1)
17 Optimal Inventory System in a Urea Bagging Industry
339(18)
C. Vijayalakshmi
R. Subramani
N. Anitha
17.1 Introduction
339(6)
17.1.1 Reasons for Carrying Inventory
340(1)
17.1.2 Inventory Metrics
341(1)
17.1.3 Performance Measures for Inventory Control
342(1)
17.1.4 Service Performance Measures
342(1)
17.1.4.1 Service Level
342(1)
17.1.4.2 Fill Rate
342(1)
17.1.5 Cost Performance Measures
342(1)
17.1.5.1 Ordering Cost, A
342(1)
17.1.5.2 Purchase Cost, P
343(1)
17.1.5.3 Inventory Holding Cost, h
343(1)
17.1.5.4 Shortage Cost, β
343(1)
17.1.5.5 Total Cost
344(1)
17.1.6 Inventory Models
344(1)
17.1.7 Deterministic Inventory Model
344(1)
17.1.8 Stochastic Inventory Model
344(1)
17.1.9 Inventory Policies
344(1)
17.2 Continuous Review Policy
345(1)
17.2.1 Periodic Review Policy
345(1)
17.3 Inventory Optimization Techniques
345(4)
17.3.1 Classical Optimization Approaches
346(1)
17.3.2 Heuristic Optimization Techniques
346(1)
17.3.3 Simulated Annealing
346(1)
17.3.4 Genetic Algorithm
347(1)
17.3.5 Tabu Search
348(1)
17.3.6 Particle Swarm Optimization
348(1)
17.3.7 Ant Colony Optimization
349(1)
17.4 Model Formulation
349(4)
17.4.1 Model Description
351(1)
17.4.2 Steady-State Probability Equations
351(1)
17.4.3 Optimal Inventory Level
352(1)
17.5 Numerical Calculations
353(1)
17.6 Conclusion
354(1)
References
354(3)
18 Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario
357(14)
C. Vijayalakshmi
18.1 Introduction
357(2)
18.2 Mixed Integer Programming Methods
359(1)
18.3 Introduction to Supply Chain Management System
359(3)
18.4 Mathematical Model Formulation
362(6)
18.4.1 Sets an Indices
362(1)
18.4.2 Parameters
363(1)
18.4.3 Decision Variables
364(1)
18.4.4 Constraints
365(1)
18.4.4.1 Constraints Related to Inventory
365(1)
18.4.4.2 Constraints Related to Emergency Orders
365(1)
18.4.4.3 Constraints Related to Quality
365(1)
18.4.4.4 Constraints Related to Delivery
366(1)
18.4.4.5 Constraints Related to Supplier Capacity
366(1)
18.4.4.6 Non-Negativity Restrictions
366(1)
18.4.5 Objective Function
367(1)
18.5 Conclusion
368(1)
References
368(3)
19 Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry-A Machine Learning Approach
371(14)
Blanche Mabusela-Motsosi
Senzosenkosi Myeni
Elias Munapo
19.1 Introduction
372(1)
19.2 Literature Review
373(1)
19.3 The Aim and Objectives of the Study
374(1)
19.4 Research Methodology
374(2)
19.5 Study Results and Discussions
376(5)
19.5.1 The Model
376(5)
19.5.2 The Pricing Calculator
381(1)
19.6 Conclusions
381(1)
References
382(3)
20 A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode
385(16)
Partha Kumar Deb
Tamasi Moyra
Bidyut Kumar Bhattacharyya
20.1 Introduction
385(1)
20.2 Designing of 90° SPS
386(5)
20.3 Designing of Tunable Schiffman Phase Shifter
391(7)
20.4 Major Finding and Limitation
398(1)
20.5 Conclusion
398(1)
References
399(2)
21 Optimizing Manufacturing Performance Through Fuzzy Techniques
401(46)
Chandan Deep Singh
Harleen Kaur
Rajdeep Singh
21.1 Introduction
401(2)
21.1.1 Manufacturing Competency
402(1)
21.1.2 Green Sustainability
402(1)
21.1.3 Methodology
403(1)
21.2 Literature Review
403(5)
21.2.1 Competency
403(1)
21.2.1.1 Competency Development
404(1)
21.2.1.2 Competency Management
404(1)
21.2.1.3 Economic Effects
405(1)
21.2.1.4 Technological Competency
406(1)
21.2.1.5 Competition
406(1)
21.2.2 Green Sustainability
407(1)
21.3 Performance Optimization through Fuzzy Techniques
408(33)
21.3.1 Fuzzy Logic
408(1)
21.3.1.1 Fuzzy Logic for Manufacturing Competency
411(1)
21.3.1.2 Fuzzy Logic for Green Sustainability
417(3)
21.3.2 Fuzzy AHP
420(1)
21.3.2.1 Fuzzy AHP for Manufacturing Competency
421(1)
21.3.2.2 Fuzzy AHP for Green Sustainability
423(1)
21.3.3 Fuzzy MDEMATEL
423(1)
21.3.3.1 Fuzzy MDEMATEL for Green Sustainability
428(1)
21.3.4 Modified Fuzzy TOPSIS
428(1)
21.3.4.1 For Manufacturing Competency
428(7)
21.3.5 Modified Fuzzy VIKOR
435(1)
21.3.5.1 For Manufacturing Competency
435(6)
21.4 Conclusions
441(2)
21.4.1 Major Findings
441(1)
21.4.2 Limitations
442(1)
21.4.3 Suggestions for Future Research
443(1)
References
443(4)
22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products
447(14)
Thiripura Sundari P.R.
Vijayalakshmi C.
22.1 Introduction
447(1)
22.2 Literature Review
448(1)
22.3 Symbols and Assumptions
449(2)
22.3.1 Symbols
449(1)
22.3.2 Assumptions
449(2)
22.4 Model Formulation
451(8)
22.4.1 The Objective Function for a Single Product
452(1)
22.4.2 The Service Level Constraint for a Single Product
452(1)
22.4.3 Mathematical Model for Multiple Products
452(1)
22.4.4 Solution Procedure
453(1)
22.4.4.1 The Solution Without Service Level Constraint (SLC)
453(1)
22.4.4.2 The Solution With SLC for Multiple Products
457(2)
22.5 Conclusion
459(1)
References
459(2)
Part III: Meta-Heuristics: Applications and Innovations 461(144)
23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer
463(24)
Mehmet Cem Catalbas
Arif Gulten
23.1 An Introduction to Optimization
463(2)
23.2 Optimization and Engineering
465(4)
23.3 Meta-Heuristic Optimization
469(2)
23.3.1 Genetic Algorithm
469(2)
23.4 Torquigener Albomaculosus
471(1)
23.5 Pufferfish and Circular Structures
471(4)
23.6 Results
475(8)
23.7 Conclusion
483(1)
References
483(4)
24 A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization
487(22)
Hisham A. Shehadeh
Nura Modi Shagari
24.1 Introduction
487(2)
24.2 Background on Sperm Swarm Optimization (SSO) and Grey Wolf Optimizer (GWO)
489(4)
24.2.1 Sperm Swarm Optimization (SSO)
489(2)
24.2.2 Grey Wolf Optimizer (GWO)
491(2)
24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization (HGWOSSO)
493(1)
24.4 Experimental and Results
494(10)
24.5 Discussion
504(1)
24.6 Conclusion
505(1)
References
505(4)
25 State-of-the-Art Optimization and Metaheuristic Algorithms
509(28)
Vineet Kumar
R. Naresh
Veena Sharma
Vineet Kumar
25.1 Introduction
509(2)
25.2 An Overview of Traditional Optimization Approaches
511(1)
25.3 Properties of Metaheuristics
512(2)
25.4 Classification of Single Objective Metaheuristic Algorithms
514(5)
25.4.1 Local and Global Search
514(1)
25.4.2 Single Solution Approach and Population-Based Algorithm
514(1)
25.4.2.1 Swarm-Based Optimization Algorithms
516(1)
25.4.2.2 Evolutionary Algorithms
516(1)
25.4.2.3 Physics-Based Algorithms
517(1)
25.4.2.4 Ecology-Based Algorithms
518(1)
25.4.3 Hybridization of Algorithms and Memetic Algorithms
518(1)
25.4.4 Parallel Metaheuristic Approaches
519(1)
25.5 Applications of Single Objective Metaheuristic Approaches
519(1)
25.6 Classification of Multi-Objective Optimization Algorithms
519(2)
25.6.1 Scalarizing
519(1)
25.6.2 Priori Method
519(1)
25.6.3 No Preference Method
520(1)
25.6.4 A Posteriori Method
520(1)
25.6.5 Interactive Method
520(1)
25.7 Hybridization of MOPs Algorithms
521(1)
25.7.1 Low and High Level
521(1)
25.7.2 Relay or Teamwork
521(1)
25.8 Parallel Multi-Objective Optimization
521(4)
25.8.1 Single Walk Parallelism
522(1)
25.8.2 Multiple Walk Parallelism
522(1)
25.8.3 The Master/Slave Model
522(1)
25.8.4 The Distributed Island Model
523(1)
25.8.5 The Cellular Model
523(1)
25.8.6 Uncertain Pareto Optimization
523(2)
25.9 Applications of Multi-Objective Optimization
525(1)
25.9.1 Economics
525(1)
25.9.2 Finance
525(1)
25.9.3 Optimal Control and Optimal Design
525(1)
25.9.4 Process Optimization
525(1)
25.9.5 Radio Resource Management
526(1)
25.9.6 Inspection of Infrastructure
526(1)
25.9.7 Electric Power Systems
526(1)
25.10 Significant Contributions of Researchers in Various Metaheuristic Approaches
526(2)
25.11 Conclusion
528(1)
25.12 Major Findings, Future Scope of Metaheuristics and Its Applications
529(1)
25.13 Limitations and Motivation of Metaheuristics
529(1)
Acknowledgements
530(1)
References
530(7)
26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique
537(12)
Souvik Ganguli
Tanya Srivastava
Gagandeep Kaur
Prasanta Sarkar
26.1 Introduction
538(3)
26.2 Proposed Methodology
541(1)
26.3 Simulation Results
542(3)
26.4 Conclusions
545(1)
References
546(3)
27 A New Parameter Estimation Technique of Three-Diode PV Cells
549(56)
Shilpy Goyal
Parag Nijhawan
Yashonidhi Srivastava
Souvik Ganguli
27.1 Introduction
549(2)
27.2 Problem Statement
551(2)
27.3 Proposed Method
553(2)
27.3.1 Exploration Phase
554(1)
27.3.2 Turning From Exploration to Exploitation
555(1)
27.3.3 Exploitation Phase
555(1)
27.4 Simulation Results and Discussions
555(48)
27.5 Conclusions
603(1)
References
603(2)
Part IV: Sustainable Computing 605(148)
28 Optimal Quantizer and Machine Learning-Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network
607(30)
Saikat Majumder
Mukhdeep Singh Manshahia
28.1 Introduction
607(3)
28.1.1 State-of-Art
607(2)
28.1.2 Motivation
609(1)
28.1.3 Major Contributions
610(1)
28.2 System Model and Preliminaries
610(3)
28.2.1 Local Spectrum Sensing
611(1)
28.2.2 Conventional Decision Fusion Techniques
612(1)
28.3 Machine Learning Techniques of Decision Fusion
613(5)
28.3.1 K-Means Clustering
614(1)
28.3.2 Support Vector Machine
615(3)
28.4 Optimum Quantization of Decision Statistic and Fusion
618(3)
28.4.1 Optimum Quantization
618(2)
28.4.2 Decision Fusion
620(1)
28.5 Measurement Setup
621(2)
28.5.1 SU Nodes
622(1)
28.5.2 PU Node
622(1)
28.5.3 Distribution of SU Nodes
622(1)
28.5.4 Methodology
623(1)
28.6 Performance Evaluation
623(10)
28.6.1 ROC Analysis
624(3)
28.6.2 Effect of SNR and 19;
627(2)
28.6.3 Effect of Number of Samples
629(3)
28.6.4 Effect of Number of Cooperative SUs
632(1)
28.7 Conclusion
633(1)
28.8 Limitations and Scope for Future Work
633(1)
References
634(3)
29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies
637(18)
Parijata Majumdar
Sanjoy Mitra
Diptendu Bhattacharya
29.1 Introduction
638(1)
29.2 Green Approaches: Significance and Motivation
638(1)
29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control
639(1)
29.4 Green IoT-Based Smart Irrigation Monitoring
639(3)
29.5 Technology Enablers for GIoT-Based Irrigation Monitoring
642(1)
29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation
642(1)
29.7 Other Recent Developments on GIoT-Based Smart Agriculture
643(2)
29.8 Literature Review of Edge Computing-Based Irrigation Monitoring
645(1)
29.9 LPWAN for GIoT-Based Smart Agriculture
646(1)
29.10 Analysis and Discussion
647(2)
29.11 Research Gap in GIoT-Based Precision Agriculture
649(1)
29.12 Analysis of Merits and Shortcomings
650(1)
29.13 Future Research Scope
651(1)
29.14 Conclusion
651(1)
References
652(3)
30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification
655(10)
B. Bazeer Ahamed
Z.A. Feroze Ahamed
30.1 Introduction
655(1)
30.2 Related Works
656(1)
30.3 Proposed Approach
657(3)
30.3.1 Data Collection and Processing
658(1)
30.3.2 Creation of Sentiment Terms Matrix
659(1)
30.3.3 Sentiment Classification
660(1)
30.4 Experimental Details and Results
660(2)
30.5 Conclusion
662(1)
References
662(3)
31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission
665(12)
R. Deepa
Revathi Venkataraman
Soumya Snigdha Kundu
31.1 Introduction
665(1)
31.2 Related Work
666(1)
31.3 Proposed Model
667(4)
31.3.1 Input Phase
667(2)
31.3.2 Deployment Phase
669(1)
31.3.3 Transmission Phase
670(1)
31.4 Complexity Analysis of Algorithms for Data Transmission
671(1)
31.5 Experimental Analysis
672(3)
31.5.1 Deployment-Based Metrics
672(1)
31.5.1.1 Coverage vs. Iterations
672(1)
31.5.1.2 Mean Travel Distance vs. Number of Nodes
673(1)
31.5.1.3 Energy Utilization
673(1)
31.5.2 Simulation-Based Metrics
674(1)
31.5.2.1 Simulation of Shortest Paths
674(1)
31.5.3 Deployment and Transmission Testing
674(1)
31.6 Motivation and Limitations of Research
675(1)
31.7 Conclusion
675(1)
31.8 Future Work
675(1)
References
675(2)
32 Intelligent Computing for Precision Agriculture
677(16)
Priyanka Gupta
Kavita Jhajharia
Pratistha Mathur
32.1 Introduction
677(7)
32.1.1 Sampling of Soil
678(1)
32.1.2 Need for Watering
679(1)
32.1.3 Composting
680(1)
32.1.4 Pest Control Technique and Crop Disorder
681(1)
32.1.5 Harvesting, Monitoring, and Forecasting
681(1)
32.1.6 Nursery Cultivation
682(1)
32.1.7 Aquaculture or Tray/Tank Farming
682(1)
32.1.8 Tools and Mechanism Used
683(1)
32.2 Technology in Agriculture
684(7)
References
691(2)
33 Intelligent Computing for Green Sustainability
693(60)
Chandan Deep Singh
Harleen Kaur
33.1 Introduction
693(4)
33.1.1 Motivation for the Study
695(1)
33.1.2 Objectives
696(1)
33.1.3 Background of the Study
696(1)
33.1.4 Novelty of the Study
696(1)
33.2 Modified DEMATEL
697(9)
33.3 Weighted Sum Model
706(2)
33.4 Weighted Product Model
708(1)
33.5 Weighted Aggregated Sum Product Assessment
709(3)
33.6 Grey Relational Analysis
712(5)
33.7 Simple Multi-Attribute Rating Technique
717(4)
33.8 Criteria Importance Through Inter-Criteria Correlation
721(5)
33.9 Entropy
726(5)
33.10 Evaluation Based on Distance From Average Solution
731(8)
33.11 MOORA
739(1)
33.12 Interpretive Structural Modeling
739(9)
33.12.1 Structural Self-Interaction Matrix
741(1)
33.12.2 Final Reachability Matrix
741(2)
33.12.3 Level Partition
743(1)
33.12.4 MICMAC Analysis
743(5)
33.13 Conclusions
748(1)
33.14 Limitations of the Study
749(1)
33.15 Suggestions for Future Research
749(1)
References
750(3)
Part V: AI in Healthcare 753(126)
34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease
755(16)
Vivek Verma
Anita Verma
Ashwani Kumar Mishra
Hafiz T.A. Khan
Dilip C. Nath
Rajiv Narang
34.1 Introduction
756(1)
34.2 Methods
757(1)
34.2.1 Study Population
757(1)
34.3 Statistical Analysis
757(2)
34.4 Results
759(2)
34.4.1 Descriptive Characteristics
759(1)
34.4.2 Clinical Characteristics
759(2)
34.5 Discussion
761(6)
34.6 Conclusion
767(1)
Acknowledgements
767(1)
References
767(4)
35 Reconstruction of Dynamic MRI Using Convolutional LSTM Technique
771(14)
Shashidhar V. Yakkundi
Subha D. Puthankattil
35.1 Introduction
771(2)
35.2 Methodologies
773(1)
35.2.1 Convolutional Neural Network
773(1)
35.2.2 Convolutional Long Short-Term Memory
774(1)
35.3 Problem Formulation
774(2)
35.4 Network Architecture
776(2)
35.4.1 Network Architecture (Cascaded ConvLSTM Without Dilation)
776(1)
35.4.2 Network Architecture (Dense Cascaded ConvLSTM With Dilation)
777(1)
35.5 Results
778(2)
35.5.1 Datasets
779(1)
35.5.2 Performance Evaluation
779(1)
35.6 Discussion
780(2)
35.7 Conclusion
782(2)
References
784(1)
36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion
785(16)
Narayan Vetrekar
Aparajita Naik
R.S. Gad
36.1 Introduction
785(2)
36.2 Literature Review
787(4)
36.2.1 Visible Spectrum
787(3)
36.2.2 Cross-Spectral
790(1)
36.2.3 Multispectral
791(1)
36.3 Multispectral Face Database
791(1)
36.4 Methodology
792(2)
36.5 Experiments
794(1)
36.6 Results and Discussion
794(2)
36.6.1 Observation I-Based on Affine Hull Method
795(1)
36.6.2 Observation II-Based on Wavelet Average Fusion
795(1)
36.6.3 Observation III-Comparison of Affine Hull and Wavelet Average Fusion
796(1)
36.7 Conclusions
796(1)
Acknowledgments
797(1)
References
797(4)
37 Polyp Detection Using Deep Neural Networks
801(14)
Nancy Rani
Rupali Verma
Alka Jindal
37.1 Introduction
801(2)
37.2 Literature Survey
803(3)
37.3 Proposed Methodology
806(4)
37.3.1 Dataset
807(1)
37.3.2 Data Pre-Processing
807(1)
37.3.3 Classification
807(1)
37.3.3.1 Concept of Transfer Learning and Fine Tuning
808(1)
37.3.3.2 VGG16 and VGG19 Model Architecture
808(2)
37.3.4 Polyp Detection
810(1)
37.4 Implementation and Results
810(2)
37.5 Conclusion and Future Work
812(1)
References
813(2)
38 Boundary Exon Prediction in Humans Sequences Using External Information Sources
815(20)
Neelam Goel
Shailendra Singh
Trilok Chand Aseri
38.1 Introduction
815(2)
38.2 Proposed Exon Prediction Model
817(2)
38.2.1 Splice Site Prediction
818(1)
38.2.2 Translation Initiation Site Prediction
818(1)
38.2.3 Coding Region Prediction
818(1)
38.2.4 Exon Prediction
818(1)
38.3 Homology-Based Exon Prediction
819(8)
38.3.1 External Information Used
819(1)
38.3.2 External Information Collection
819(2)
38.3.3 Proposed Method
821(1)
38.3.3.1 Multiple Sequence Alignment
822(1)
38.3.3.2 Prediction of Coding Regions
823(1)
38.3.3.3 Merging and Chaining of Predicted Coding Regions
824(1)
38.3.4 Exon Prediction With Precise Boundaries
824(2)
38.3.5 Graphical User Interface
826(1)
38.4 Results and Discussion
827(3)
38.4.1 Datasets Used
827(1)
38.4.2 Results
827(3)
38.5 Conclusion
830(1)
38.6 Motivation and Limitations of the Research
831(1)
38.7 Major Findings of the Research
831(1)
References
832(3)
39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform
835(14)
Jivan Parab
M. Sequeira
M. Lanjewar
C. Pinto
G.M. Naik
39.1 Introduction
835(2)
39.1.1 Selection of Wavelength Region
836(1)
39.1.2 Motivation
837(1)
39.2 Sample Preparation
837(2)
39.3 Methodology
839(3)
39.3.1 Partial Least Square Regression (PLSR)
839(1)
39.3.2 Backpropagation Artificial Neural Network (BP-ANN)
840(2)
39.4 Results and Discussion
842(3)
39.4.1 Estimation of Glucose Prediction
842(1)
39.4.2 System Validation
843(1)
39.4.2.1 Bland-Altman Analysis
843(1)
39.4.2.2 Clarke Error Grid Analysis (CEGA)
844(1)
39.4.3 Regression Analysis
844(1)
39.4.4 Accuracy of the System
845(1)
39.5 Discussion
845(1)
39.6 Conclusion
846(1)
39.7 Future Scope
846(1)
Acknowledgement
847(1)
References
847(2)
40 GIS-Based Geospatial Assessment of Novel Corona Virus (COVID-19) in One of the Promising Industrial States of India-A Case of Gujarat
849(20)
Azazkhan I. Pathan
Pankaj J. Gandhi
P.G. Agnihotri
Dhruvesh Patel
40.1 Introduction
849(3)
40.1.1 Major Findings of the Study
852(1)
40.2 The Rationale of the Study
852(2)
40.3 Materials and Methodology
854(5)
40.3.1 Mapping
854(1)
40.3.2 Challenges in Using GIS With Spatiotemporal Big Data
855(1)
40.3.3 Data Acquisition, Sampling Design, and Data Analysis
855(1)
40.3.4 Methodology
856(1)
40.3.4.1 Data Collection and Analysis
856(1)
40.3.4.2 Pre-Processing
856(1)
40.3.4.3 Model Execution
856(3)
40.4 GIS and COVID-19 (Corona) Mapping
859(1)
40.4.1 Limitation of the Study
859(1)
40.4.2 Advantages of the Study
860(1)
40.5 Results and Discussion
860(5)
40.5.1 Future Scope of the Study
865(1)
40.6 Conclusion
865(1)
References
866(3)
41 Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi
869(10)
Munish Manas
Shivam Kumar
41.1 Introduction
869(1)
41.2 Hardware Design of Monitoring System
870(3)
41.3 Software Design of Monitoring System
873(1)
41.4 Working of ZigBee Module
874(1)
41.5 Developed App for the Monitoring of Health
874(1)
41.6 Google Fusion Table-Online Database
875(1)
41.7 Application Developed for Health Monitoring System
876(1)
41.8 Conclusion and Future Work
877(1)
References
877(2)
Index 879
Mukhdeep Singh Manshahia, PhD, is an assistant professor at Punjabi University Patiala, India. He has published more than 40 international and national research papers and edited 1 book.

Valeriy Kharchenko, PhD, is the Chief Scientific Officer at the Federal Scientific Agro Engineering Center VIM, Moscow, Russia.

Elias Munapo, PhD, is a full professor in the Department of Statistics & Operations Research, North West University, South Africa. He has published more than 100 research articles and book chapters and has edited several volumes.

J. Joshua Thomas, PhD, is a senior lecturer at UOW Malaysia KDU Penang University College, Malaysia. Currently, he is working with machine learning, big data, data analytics, deep learning, specifically targeting convolutional neural networks (CNN) and bi-directional recurrent neural networks (RNN) for image tagging with embedded natural language processing, end-to-end steering learning systems, and GAN. He has published more than 40 papers in leading international conference proceedings and peer-reviewed journals.

Pandian Vasant, PhD, is a professor at Universiti Teknologi PETRONAS, Malaysia. He has co-authored more than 250 research articles in journals, conference proceedings, presentations, special issues guest editor, book chapters, and is the Editor-in-Chief of International Journal of Energy Optimization & Engineering.