{"product_id":"handbook-of-intelligent-computing-and-optimization-for-sustainable-development-hardback-9781119791829","title":"Handbook of Intelligent Computing and Optimization for Sustainable Development (Hardback) 9781119791829","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHandbook of Intelligent Computing and Optimization for Sustainable Development\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eMukhdeep Singh Manshahia (Edited by), MS Manshahia (Author), Valeriy Kharchenko (Edited by), Elias Munapo (Edited by), J. Joshua Thomas (Edited by), Pandian Vasant (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119791829, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 25 March 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e944 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eDieses Buch bietet einen umfassenden Überblick über die neuesten Entwicklungen und Fortschritte im Bereich der nachhaltigen intelligenten Computertechnologie, entsprechender Anwendungen und Optimierungstechniken in verschiedenen Branchen.\u003cbr\u003e \u003cbr\u003e Mit der rasant zunehmenden Nutzung von Kommunikationstechnologie und der Entwicklung von benutzerfreundlicher Software und künstlicher Intelligenz ist Optimierung ein wesentlicher Faktor geworden. Bei fast allen menschlichen Tätigkeiten besteht der Wunsch, mit möglichst geringem Aufwand möglichst gute Ergebnisse zu erzielen. Darüber hinaus ist die Optimierung schon lange ein Schwerpunkt in den verschiedensten Anwendungsbereichen, von Problemen bei der Routenberechnung bis zur medizinischen Behandlung, über das Bau- und Finanzwesen, die Buchhaltung, das Ingenieurwesen und Wartungspläne für Industrieanlagen. Bei der Optimierung von realen Problemen kann es helfen, die Art des Problems zu verstehen und es in eine geeignete Klasse einzuordnen, damit der Entwickler die geeigneten Techniken für eine effiziente Problemlösung anwenden kann. Bei vielen intelligenten Optimierungstechniken lassen sich auch ohne Verwendung einer Zielfunktion optimale Lösungen finden, wobei auch die lokalen Bedingungen eine geringere Rolle spielen.\u003cbr\u003e \u003cbr\u003e Die 41 Kapitel des Handbook of Intelligent Computing and Optimization for Sustainable Development wurden von Fachleuten aus verschiedenen Bereichen, darunter Mathematik und Informatik, Elektronik und Elektrotechnik, Neuro- und Kognitionswissenschaften, Medizin und Sozialwissenschaften, verfasst und vermitteln den Leserinnen und Lesern ein umfassendes Verständnis davon, welche Bedeutung intelligente Computertechnologie für die nachhaltige Entwicklung der modernen Gesellschaften hat. Erörtert werden zudem die neuesten Forschungsarbeiten an den theoretischen und praktischen Aspekten der erfolgreichen Implementierung neuer, innovativer intelligenter Techniken in den verschiedensten Bereichen, darunter beim IoT, in der Fertigung, bei der Optimierung und im Gesundheitswesen.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xxxi\u003c\/p\u003e \u003cp\u003ePreface xxxv\u003c\/p\u003e \u003cp\u003eAcknowledgment xlv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Intelligent Computing and Applications 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSouvik Das, Kintada Prudhvi and J. Maiti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Data Acquisition Method 4\u003c\/p\u003e \u003cp\u003e1.3 Feature Extraction 4\u003c\/p\u003e \u003cp\u003e1.4 Deep Learning Models 5\u003c\/p\u003e \u003cp\u003e1.5 Results 8\u003c\/p\u003e \u003cp\u003e1.6 Discussion 10\u003c\/p\u003e \u003cp\u003e1.7 Advantages and Disadvantages of the Study 11\u003c\/p\u003e \u003cp\u003e1.8 Limitations of the Study 11\u003c\/p\u003e \u003cp\u003e1.9 Conclusion 11\u003c\/p\u003e \u003cp\u003eReferences 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates 13\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMandrita Mondal and Kumar S. Ray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 13\u003c\/p\u003e \u003cp\u003e2.2 Biological Neurons 15\u003c\/p\u003e \u003cp\u003e2.3 Artificial Neural Networks 17\u003c\/p\u003e \u003cp\u003e2.4 DNA Neural Networks 22\u003c\/p\u003e \u003cp\u003e2.5 DNA Logic Gates 28\u003c\/p\u003e \u003cp\u003e2.6 Advantages and Limitations 45\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 47\u003c\/p\u003e \u003cp\u003eAcknowledgment 47\u003c\/p\u003e \u003cp\u003eReferences 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Intelligent Garment Detection Using Deep Learning 49\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAniruddha Srinivas Joshi, Savyasachi Gupta, Goutham Kanahasabai and Earnest Paul Ijjina\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 49\u003c\/p\u003e \u003cp\u003e3.2 Literature 50\u003c\/p\u003e \u003cp\u003e3.3 Methodology 52\u003c\/p\u003e \u003cp\u003e3.4 Experimental Results 59\u003c\/p\u003e \u003cp\u003e3.5 Highlights 64\u003c\/p\u003e \u003cp\u003e3.6 Conclusion and Future Works 65\u003c\/p\u003e \u003cp\u003eAcknowledgements 65\u003c\/p\u003e \u003cp\u003eReferences 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Intelligent Computing on Complex Numbers for Cryptographic Applications 69\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNi Ni Hla and Tun Myat Aung\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 69\u003c\/p\u003e \u003cp\u003e4.2 Modular Arithmetic 70\u003c\/p\u003e \u003cp\u003e4.3 Complex Plane 71\u003c\/p\u003e \u003cp\u003e4.4 Matrix Algebra 71\u003c\/p\u003e \u003cp\u003e4.5 Elliptic Curve Arithmetic 73\u003c\/p\u003e \u003cp\u003e4.6 Cryptographic Applications 74\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 78\u003c\/p\u003e \u003cp\u003eReferences 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Application of Machine Learning Framework for Next-Generation Wireless Networks: Challenges and Case Studies 81\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSatyendra Singh Yadav, Shrishail Hiremath, Pravallika Surisetti, Vijay Kumar and Sarat Kumar Patra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 82\u003c\/p\u003e \u003cp\u003e5.2 Machine\/Deep Learning for Future Wireless Communication 83\u003c\/p\u003e \u003cp\u003e5.3 Case Studies 87\u003c\/p\u003e \u003cp\u003e5.4 Major Findings 95\u003c\/p\u003e \u003cp\u003e5.5 Future Research Directions 95\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 96\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Designing of Routing Protocol for Crowd Associated Networks (CrANs) 101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRabia Bilal and Bilal Muhammad Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 101\u003c\/p\u003e \u003cp\u003e6.2 Background Study 103\u003c\/p\u003e \u003cp\u003e6.3 CrANs 117\u003c\/p\u003e \u003cp\u003e6.4 Simulation of MANET Network 123\u003c\/p\u003e \u003cp\u003e6.5 Simulation of VANET Network 126\u003c\/p\u003e \u003cp\u003e6.6 CrANs 130\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 132\u003c\/p\u003e \u003cp\u003eReferences 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Application of Group Method of Data Handling–Based Neural Network (GMDH-NN) for Forecasting Permeate Flux (%) of Disc-Shaped Membrane 135\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnirban Banik, Mrinmoy Majumder, Sushant Kumar Biswal and Tarun Kanti Bandyopadhyay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 135\u003c\/p\u003e \u003cp\u003e7.2 Experimental Procedure 138\u003c\/p\u003e \u003cp\u003e7.3 Methodology 139\u003c\/p\u003e \u003cp\u003e7.4 Results and Discussions 142\u003c\/p\u003e \u003cp\u003e7.5 Conclusions 146\u003c\/p\u003e \u003cp\u003eAcknowledgements 147\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Automated Extraction of Non-Functional Requirements From Text Files: A Supervised Learning Approach 149\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eM. Sunil Kumar, A. Harika, C. Sushama and P. Neelima\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 149\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 153\u003c\/p\u003e \u003cp\u003e8.3 Methodology 156\u003c\/p\u003e \u003cp\u003e8.4 Dataset 165\u003c\/p\u003e \u003cp\u003e8.5 Evaluation 166\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 169\u003c\/p\u003e \u003cp\u003eReferences 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Image Classification by Reinforcement Learning With Two-State Q-Learning 171\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAbdul Mueed Hafiz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 171\u003c\/p\u003e \u003cp\u003e9.2 Proposed Approach 173\u003c\/p\u003e \u003cp\u003e9.3 Datasets Used 174\u003c\/p\u003e \u003cp\u003e9.4 Experimentation 176\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 178\u003c\/p\u003e \u003cp\u003eReferences 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Design and Development of Neural-Fuzzy Control Model for Computer-Based Control Systems in a Multivariable Chemical Process 183\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePankaj Mohindru, Pooja and Vishwesh Akre\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 184\u003c\/p\u003e \u003cp\u003e10.2 Distributed Control System 187\u003c\/p\u003e \u003cp\u003e10.3 Fuzzy Logic 192\u003c\/p\u003e \u003cp\u003e10.4 Artificial Neural Network 193\u003c\/p\u003e \u003cp\u003e10.5 Neuro-Fuzzy 194\u003c\/p\u003e \u003cp\u003e10.6 Case Study 197\u003c\/p\u003e \u003cp\u003e10.7 Software Implementation on Graphical User Interface 203\u003c\/p\u003e \u003cp\u003e10.8 Results and Discussion 212\u003c\/p\u003e \u003cp\u003e10.9 Discussion 214\u003c\/p\u003e \u003cp\u003e10.10 Conclusion 214\u003c\/p\u003e \u003cp\u003e10.11 Scope for Future Work 215\u003c\/p\u003e \u003cp\u003eReferences 215\u003c\/p\u003e \u003cp\u003eAppendix 10.1 MATLAB Simulation Configuration Using Sugeno 217\u003c\/p\u003e \u003cp\u003eAppendix 10.2 MATLAB Window Displaying Desired Training-Data Fed to Neuro-Fuzzy Model 218\u003c\/p\u003e \u003cp\u003eAppendix 10.3 MATLAB Window Displaying Checking-Data Fed to Neuro-Fuzzy Model 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Artificial Neural Network in the Manufacturing Sector 219\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNavriti Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 219\u003c\/p\u003e \u003cp\u003e11.2 Optimization 221\u003c\/p\u003e \u003cp\u003e11.3 Artificial Neural Network: Optimization of Mechanical Systems 223\u003c\/p\u003e \u003cp\u003e11.4 ANN vs. Human Brain 228\u003c\/p\u003e \u003cp\u003e11.5 Architecture of Artificial Neural Networks 229\u003c\/p\u003e \u003cp\u003e11.6 Learning Algorithm(s) 235\u003c\/p\u003e \u003cp\u003e11.7 Different Type of Data 237\u003c\/p\u003e \u003cp\u003e11.8 Case Study: Hard Machining of EN 31 Steel 238\u003c\/p\u003e \u003cp\u003e11.9 Advantages of Using ANN in Manufacturing Sectors 242\u003c\/p\u003e \u003cp\u003e11.10 Disadvantages of Using ANN in Manufacturing Sectors 242\u003c\/p\u003e \u003cp\u003e11.11 Applications 242\u003c\/p\u003e \u003cp\u003e11.12 Conclusions 243\u003c\/p\u003e \u003cp\u003e11.13 Future Scope of ANN in Manufacturing Sectors 244\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Speech-Based Multilingual Translation Framework 249\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSaloni and Williamjeet Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 249\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 250\u003c\/p\u003e \u003cp\u003e12.3 Phases of ASR 252\u003c\/p\u003e \u003cp\u003e12.4 Modules of ASR 253\u003c\/p\u003e \u003cp\u003e12.5 Speech Database for ASR 253\u003c\/p\u003e \u003cp\u003e12.6 Developing ASR 255\u003c\/p\u003e \u003cp\u003e12.7 Performance of ASR 256\u003c\/p\u003e \u003cp\u003e12.8 Application Areas 257\u003c\/p\u003e \u003cp\u003e12.9 Conclusion and Future Work 258\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Text Summarization: A Technical Overview and Research Perspectives 261\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKorrapati Sindhu and Karthick Seshadri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 262\u003c\/p\u003e \u003cp\u003e13.2 Summarization Techniques 263\u003c\/p\u003e \u003cp\u003e13.3 Evaluating Summaries 279\u003c\/p\u003e \u003cp\u003e13.4 Datasets and Results 281\u003c\/p\u003e \u003cp\u003e13.5 Future Research Directions 281\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 282\u003c\/p\u003e \u003cp\u003eReferences 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery 287\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSitendra Tamrakar, B. K. Madhavi and V. Mohan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 287\u003c\/p\u003e \u003cp\u003e14.2 Literature Review 289\u003c\/p\u003e \u003cp\u003e14.3 Understanding the Google Cloud Platform 291\u003c\/p\u003e \u003cp\u003e14.4 Using BigQuery in the Google Cloud Console 294\u003c\/p\u003e \u003cp\u003e14.5 Sentiment Analysis 294\u003c\/p\u003e \u003cp\u003e14.6 Turning to Google BigQuery Analysis 295\u003c\/p\u003e \u003cp\u003e14.7 Proposed Method 297\u003c\/p\u003e \u003cp\u003eStreaming API 298\u003c\/p\u003e \u003cp\u003e14.8 Experimental Setup and Results 300\u003c\/p\u003e \u003cp\u003e14.9 Conclusion 302\u003c\/p\u003e \u003cp\u003eReferences 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Review of Topic Modeling and Its Application 305\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Sandhiya, A. M. Boopika, M. Akshatha, S. V. Swetha and N. M. Hariharan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 305\u003c\/p\u003e \u003cp\u003e15.2 Objective of Topic Modeling 306\u003c\/p\u003e \u003cp\u003e15.3 Motivations and Contributions 307\u003c\/p\u003e \u003cp\u003e15.4 Detailed Survey of Research Articles 308\u003c\/p\u003e \u003cp\u003eInformation Extraction Systems by Gibbs Sampling 316\u003c\/p\u003e \u003cp\u003eMonte Carlo Algorithm 316\u003c\/p\u003e \u003cp\u003e15.5 Comparison Table of Previous Research 319\u003c\/p\u003e \u003cp\u003e15.6 Expected Future Work 320\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 320\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Optimization 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 ROC Method for Identifying the Optimal Threshold With an Application to Email Classification 325\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFasanya, Oluwafunmibi O., Adediran, Adetola A., Ewemooje, Olusegun S. and Adebola, Femi B.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 325\u003c\/p\u003e \u003cp\u003e16.2 Related Works 326\u003c\/p\u003e \u003cp\u003e16.3 Methodology 328\u003c\/p\u003e \u003cp\u003e16.4 Results and Discussion 334\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 337\u003c\/p\u003e \u003cp\u003eReferences 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Optimal Inventory System in a Urea Bagging Industry 339\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Vijayalakshmi, R. Subramani and N. Anitha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 339\u003c\/p\u003e \u003cp\u003e17.2 Continuous Review Policy 345\u003c\/p\u003e \u003cp\u003e17.3 Inventory Optimization Techniques 345\u003c\/p\u003e \u003cp\u003e17.5 Numerical Calculations 353\u003c\/p\u003e \u003cp\u003e17.6 Conclusion 354\u003c\/p\u003e \u003cp\u003eReferences 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Design of a Mixed Integer Linear Programming Model for Optimization of Supply Chain of a Single Product With Disruption Scenario 357\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Vijayalakshmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 357\u003c\/p\u003e \u003cp\u003e18.2 Mixed Integer Programming Methods 359\u003c\/p\u003e \u003cp\u003e18.3 Introduction to Supply Chain Management System 359\u003c\/p\u003e \u003cp\u003e18.4 Mathematical Model Formulation 362\u003c\/p\u003e \u003cp\u003e18.5 Conclusion 368\u003c\/p\u003e \u003cp\u003eReferences 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Development of Base Tax Liability Insurance Premium Calculator for the South African Construction Industry—A Machine Learning Approach 371\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBlanche Mabusela-Motsosi, Senzosenkosi Myeni and Elias Munapo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 372\u003c\/p\u003e \u003cp\u003e19.2 Literature Review 373\u003c\/p\u003e \u003cp\u003e19.3 The Aim and Objectives of the Study 374\u003c\/p\u003e \u003cp\u003e19.4 Research Methodology 374\u003c\/p\u003e \u003cp\u003e19.5 Study Results and Discussions 376\u003c\/p\u003e \u003cp\u003e19.6 Conclusions 381\u003c\/p\u003e \u003cp\u003eReferences 382\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 A 90-Degree Schiffman Phase Shifter and Study of Tunability Using Varactor Diode 385\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePartha Kumar Deb, Tamasi Moyra and Bidyut Kumar Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 385\u003c\/p\u003e \u003cp\u003e20.2 Designing of 90° SPS 386\u003c\/p\u003e \u003cp\u003e20.3 Designing of Tunable Schiffman Phase Shifter 391\u003c\/p\u003e \u003cp\u003e20.4 Major Finding and Limitation 398\u003c\/p\u003e \u003cp\u003e20.5 Conclusion 398\u003c\/p\u003e \u003cp\u003eReferences 399\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Optimizing Manufacturing Performance Through Fuzzy Techniques 401\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChandan Deep Singh, Harleen Kaur and Rajdeep Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 401\u003c\/p\u003e \u003cp\u003e21.2 Literature Review 403\u003c\/p\u003e \u003cp\u003e21.3 Performance Optimization through Fuzzy Techniques 408\u003c\/p\u003e \u003cp\u003e21.4 Conclusions 441\u003c\/p\u003e \u003cp\u003eReferences 443\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Implementation of Non-Linear Inventory Optimization Model for Multiple Products 447\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThiripura Sundari P.R. and Vijayalakshmi C.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 447\u003c\/p\u003e \u003cp\u003e22.2 Literature Review 448\u003c\/p\u003e \u003cp\u003e22.3 Symbols and Assumptions 449\u003c\/p\u003e \u003cp\u003e22.4 Model Formulation 451\u003c\/p\u003e \u003cp\u003e22.5 Conclusion 459\u003c\/p\u003e \u003cp\u003eReferences 459\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Meta-Heuristics: Applications and Innovations 461\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Pufferfish Optimization Algorithm: A Bioinspired Optimizer 463\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMehmet Cem Catalbas and Arif Gulten\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 An Introduction to Optimization 463\u003c\/p\u003e \u003cp\u003e23.2 Optimization and Engineering 465\u003c\/p\u003e \u003cp\u003e23.3 Meta-Heuristic Optimization 469\u003c\/p\u003e \u003cp\u003e23.4 Torquigener Albomaculosus 471\u003c\/p\u003e \u003cp\u003e23.5 Pufferfish and Circular Structures 471\u003c\/p\u003e \u003cp\u003e23.6 Results 475\u003c\/p\u003e \u003cp\u003e23.7 Conclusion 483\u003c\/p\u003e \u003cp\u003eReferences 483\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 A Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization for Global Optimization 487\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHisham A. Shehadeh and Nura Modi Shagari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 487\u003c\/p\u003e \u003cp\u003e24.2 Background on Sperm Swarm Optimization (SSO) and Grey\u003c\/p\u003e \u003cp\u003eWolf Optimizer (GWO) 489\u003c\/p\u003e \u003cp\u003e24.3 Hybrid Grey Wolf Optimizer and Sperm Swarm Optimization\u003c\/p\u003e \u003cp\u003e(HGWOSSO) 493\u003c\/p\u003e \u003cp\u003e24.4 Experimental and Results 494\u003c\/p\u003e \u003cp\u003e24.5 Discussion 504\u003c\/p\u003e \u003cp\u003e24.6 Conclusion 505\u003c\/p\u003e \u003cp\u003eReferences 505\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 State-of-the-Art Optimization and Metaheuristic Algorithms 509\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVineet Kumar, R. Naresh, Veena Sharma and Vineet Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 509\u003c\/p\u003e \u003cp\u003e25.2 An Overview of Traditional Optimization Approaches 511\u003c\/p\u003e \u003cp\u003e25.3 Properties of Metaheuristics 512\u003c\/p\u003e \u003cp\u003e25.4 Classification of Single Objective Metaheuristic Algorithms 514\u003c\/p\u003e \u003cp\u003e25.5 Applications of Single Objective Metaheuristic Approaches 519\u003c\/p\u003e \u003cp\u003e25.6 Classification of Multi-Objective Optimization Algorithms 519\u003c\/p\u003e \u003cp\u003e25.7 Hybridization of MOPs Algorithms 521\u003c\/p\u003e \u003cp\u003e25.8 Parallel Multi-Objective Optimization 521\u003c\/p\u003e \u003cp\u003e25.9 Applications of Multi-Objective Optimization 525\u003c\/p\u003e \u003cp\u003e25.10 Significant Contributions of Researchers in Various\u003c\/p\u003e \u003cp\u003eMetaheuristic Approaches 526\u003c\/p\u003e \u003cp\u003e25.11 Conclusion 528\u003c\/p\u003e \u003cp\u003e25.12 Major Findings, Future Scope of Metaheuristics and Its Applications 529\u003c\/p\u003e \u003cp\u003e25.13 Limitations and Motivation of Metaheuristics 529\u003c\/p\u003e \u003cp\u003eAcknowledgements 530\u003c\/p\u003e \u003cp\u003eReferences 530\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Model Reduction and Controller Scheme Development of Permanent Magnet Synchronous Motor Drives in the Delta Domain Using a Hybrid Firefly Technique 537\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSouvik Ganguli, Tanya Srivastava, Gagandeep Kaur and Prasanta Sarkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 538\u003c\/p\u003e \u003cp\u003e26.2 Proposed Methodology 541\u003c\/p\u003e \u003cp\u003e26.3 Simulation Results 542\u003c\/p\u003e \u003cp\u003e26.4 Conclusions 545\u003c\/p\u003e \u003cp\u003eReferences 546\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 A New Parameter Estimation Technique of Three-Diode PV Cells 549\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShilpy Goyal, Parag Nijhawan, Yashonidhi Srivastava and Souvik Ganguli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 549\u003c\/p\u003e \u003cp\u003e27.2 Problem Statement 551\u003c\/p\u003e \u003cp\u003e27.3 Proposed Method 553\u003c\/p\u003e \u003cp\u003e27.4 Simulation Results and Discussions 555\u003c\/p\u003e \u003cp\u003e27.5 Conclusions 603\u003c\/p\u003e \u003cp\u003eReferences 603\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Sustainable Computing 605\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network 607\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSaikat Majumder and Mukhdeep Singh Manshahia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 607\u003c\/p\u003e \u003cp\u003e28.2 System Model and Preliminaries 610\u003c\/p\u003e \u003cp\u003e28.3 Machine Learning Techniques of Decision Fusion 613\u003c\/p\u003e \u003cp\u003e28.4 Optimum Quantization of Decision Statistic and Fusion 618\u003c\/p\u003e \u003cp\u003e28.5 Measurement Setup 621\u003c\/p\u003e \u003cp\u003e28.6 Performance Evaluation 623\u003c\/p\u003e \u003cp\u003e28.7 Conclusion 633\u003c\/p\u003e \u003cp\u003e28.8 Limitations and Scope for Future Work 633\u003c\/p\u003e \u003cp\u003eReferences 634\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Green IoT for Smart Agricultural Monitoring: Prediction Intelligence With Machine Learning Algorithms, Analysis of Prototype, and Review of Emerging Technologies 637\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eParijata Majumdar, Sanjoy Mitra and Diptendu Bhattacharya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 638\u003c\/p\u003e \u003cp\u003e29.2 Green Approaches: Significance and Motivation 638\u003c\/p\u003e \u003cp\u003e29.3 Machine Learning Algorithms for Prediction Intelligence in Smart Irrigation Control 639\u003c\/p\u003e \u003cp\u003e29.4 Green IoT–Based Smart Irrigation Monitoring 639\u003c\/p\u003e \u003cp\u003e29.5 Technology Enablers for GIoT–Based Irrigation Monitoring 642\u003c\/p\u003e \u003cp\u003e29.6 Prototype of the Layered GIoT Framework for Intelligent Irrigation 642\u003c\/p\u003e \u003cp\u003e29.7 Other Recent Developments on GIoT–Based Smart Agriculture 643\u003c\/p\u003e \u003cp\u003e29.8 Literature Review of Edge Computing–Based Irrigation Monitoring 645\u003c\/p\u003e \u003cp\u003e29.9 LPWAN for GIoT–Based Smart Agriculture 646\u003c\/p\u003e \u003cp\u003e29.10 Analysis and Discussion 647\u003c\/p\u003e \u003cp\u003e29.11 Research Gap in GIoT–Based Precision Agriculture 649\u003c\/p\u003e \u003cp\u003e29.12 Analysis of Merits and Shortcomings 650\u003c\/p\u003e \u003cp\u003e29.13 Future Research Scope 651\u003c\/p\u003e \u003cp\u003e29.14 Conclusion 651\u003c\/p\u003e \u003cp\u003eReferences 652\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Prominence of Sentiment Analysis in Web-Based Data Using Semi-Supervised Classification 655\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eB. Bazeer Ahamed and Z. A. Feroze Ahamed\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 655\u003c\/p\u003e \u003cp\u003e30.2 Related Works 656\u003c\/p\u003e \u003cp\u003e30.3 Proposed Approach 657\u003c\/p\u003e \u003cp\u003e30.4 Experimental Details and Results 660\u003c\/p\u003e \u003cp\u003e30.5 Conclusion 662\u003c\/p\u003e \u003cp\u003eReferences 662\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 A Three-Phase Fuzzy and A* Approach to Sensor Deployment and Transmission 665\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Deepa, Revathi Venkataraman and Soumya Snigdha Kundu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 665\u003c\/p\u003e \u003cp\u003e31.2 Related Work 666\u003c\/p\u003e \u003cp\u003e31.3 Proposed Model 667\u003c\/p\u003e \u003cp\u003e31.4 Complexity Analysis of Algorithms for Data Transmission 671\u003c\/p\u003e \u003cp\u003e31.5 Experimental Analysis 672\u003c\/p\u003e \u003cp\u003e31.6 Motivation and Limitations of Research 675\u003c\/p\u003e \u003cp\u003e31.7 Conclusion 675\u003c\/p\u003e \u003cp\u003e31.8 Future Work 675\u003c\/p\u003e \u003cp\u003eReferences 675\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 Intelligent Computing for Precision Agriculture 677\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePriyanka Gupta, Kavita Jhajharia and Pratistha Mathur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e32.1 Introduction 677\u003c\/p\u003e \u003cp\u003e32.2 Technology in Agriculture 684\u003c\/p\u003e \u003cp\u003eReferences 691\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 Intelligent Computing for Green Sustainability 693\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChandan Deep Singh and Harleen Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e33.1 Introduction 693\u003c\/p\u003e \u003cp\u003e33.2 Modified DEMATEL 697\u003c\/p\u003e \u003cp\u003e33.3 Weighted Sum Model 706\u003c\/p\u003e \u003cp\u003e33.4 Weighted Product Model 708\u003c\/p\u003e \u003cp\u003e33.5 Weighted Aggregated Sum Product Assessment 709\u003c\/p\u003e \u003cp\u003e33.6 Grey Relational Analysis 712\u003c\/p\u003e \u003cp\u003e33.7 Simple Multi-Attribute Rating Technique 717\u003c\/p\u003e \u003cp\u003e33.8 Criteria Importance Through Inter-Criteria Correlation 721\u003c\/p\u003e \u003cp\u003e33.9 Entropy 726\u003c\/p\u003e \u003cp\u003e33.10 Evaluation Based on Distance From Average Solution 731\u003c\/p\u003e \u003cp\u003e33.11 MOORA 739\u003c\/p\u003e \u003cp\u003e33.12 Interpretive Structural Modeling 739\u003c\/p\u003e \u003cp\u003e33.13 Conclusions 748\u003c\/p\u003e \u003cp\u003e33.14 Limitations of the Study 749\u003c\/p\u003e \u003cp\u003e33.15 Suggestions for Future Research 749\u003c\/p\u003e \u003cp\u003eReferences 750\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: AI in Healthcare 753\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 Bayesian Estimation of Gender Differences in Lipid Profile, Among Patients With Coronary Artery Disease 755\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVivek Verma, Anita Verma, Ashwani Kumar Mishra, Hafiz T.A. Khan, Dilip C. Nath and Rajiv Narang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e34.1 Introduction 756\u003c\/p\u003e \u003cp\u003e34.2 Methods 757\u003c\/p\u003e \u003cp\u003e34.3 Statistical Analysis 757\u003c\/p\u003e \u003cp\u003e34.4 Results 759\u003c\/p\u003e \u003cp\u003e34.5 Discussion 761\u003c\/p\u003e \u003cp\u003e34.6 Conclusion 767\u003c\/p\u003e \u003cp\u003eAcknowledgements 767\u003c\/p\u003e \u003cp\u003eReferences 767\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 Reconstruction of Dynamic MRI Using Convolutional LSTM Technique 771\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShashidhar V. Yakkundi and Subha D. Puthankattil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e35.1 Introduction 771\u003c\/p\u003e \u003cp\u003e35.2 Methodologies 773\u003c\/p\u003e \u003cp\u003e35.3 Problem Formulation 774\u003c\/p\u003e \u003cp\u003e35.4 Network Architecture 776\u003c\/p\u003e \u003cp\u003e35.5 Results 778\u003c\/p\u003e \u003cp\u003e35.6 Discussion 780\u003c\/p\u003e \u003cp\u003e35.7 Conclusion 782\u003c\/p\u003e \u003cp\u003eReferences 784\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 Gender Classification Using Multispectral Imaging: A Comparative Performance Analysis Between Affine Hull and Wavelet Fusion 785\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNarayan Vetrekar, Aparajita Naik and R. S. Gad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e36.1 Introduction 785\u003c\/p\u003e \u003cp\u003e36.2 Literature Review 787\u003c\/p\u003e \u003cp\u003e36.3 Multispectral Face Database 791\u003c\/p\u003e \u003cp\u003e36.4 Methodology 792\u003c\/p\u003e \u003cp\u003e36.5 Experiments 794\u003c\/p\u003e \u003cp\u003e36.6 Results and Discussion 794\u003c\/p\u003e \u003cp\u003e36.7 Conclusions 796\u003c\/p\u003e \u003cp\u003eAcknowledgments 797\u003c\/p\u003e \u003cp\u003eReferences 797\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 Polyp Detection Using Deep Neural Networks 801\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNancy Rani, Rupali Verma and Alka Jindal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e37.1 Introduction 801\u003c\/p\u003e \u003cp\u003e37.2 Literature Survey 803\u003c\/p\u003e \u003cp\u003e37.3 Proposed Methodology 806\u003c\/p\u003e \u003cp\u003e37.4 Implementation and Results 810\u003c\/p\u003e \u003cp\u003e37.5 Conclusion and Future Work 812\u003c\/p\u003e \u003cp\u003eReferences 813\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38 Boundary Exon Prediction in Humans Sequences Using External Information Sources 815\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNeelam Goel, Shailendra Singh and Trilok Chand Aseri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e38.1 Introduction 815\u003c\/p\u003e \u003cp\u003e38.2 Proposed Exon Prediction Model 817\u003c\/p\u003e \u003cp\u003e38.3 Homology-Based Exon Prediction 819\u003c\/p\u003e \u003cp\u003e38.4 Results and Discussion 827\u003c\/p\u003e \u003cp\u003e38.5 Conclusion 830\u003c\/p\u003e \u003cp\u003e38.6 Motivation and Limitations of the Research 831\u003c\/p\u003e \u003cp\u003e38.7 Major Findings of the Research 831\u003c\/p\u003e \u003cp\u003eReferences 832\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39 Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform 835\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJivan Parab, M. Sequeira, M. Lanjewar, C. Pinto and G.M. Naik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e39.1 Introduction 835\u003c\/p\u003e \u003cp\u003e39.2 Sample Preparation 837\u003c\/p\u003e \u003cp\u003e39.3 Methodology 839\u003c\/p\u003e \u003cp\u003e39.4 Results and Discussion 842\u003c\/p\u003e \u003cp\u003e39.5 Discussion 845\u003c\/p\u003e \u003cp\u003e39.6 Conclusion 846\u003c\/p\u003e \u003cp\u003e39.7 Future Scope 846\u003c\/p\u003e \u003cp\u003eAcknowledgement 847\u003c\/p\u003e \u003cp\u003eReferences 847\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40 GIS-Based Geospatial Assessment of Novel Corona Virus (COVID-19) in One of the Promising Industrial States of India—A Case of Gujarat 849\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAzazkhan I. Pathan, Pankaj J. Gandhi , P.G. Agnihotri and Dhruvesh Patel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e40.1 Introduction 849\u003c\/p\u003e \u003cp\u003e40.2 The Rationale of the Study 852\u003c\/p\u003e \u003cp\u003e40.3 Materials and Methodology 854\u003c\/p\u003e \u003cp\u003e40.4 GIS and COVID-19 (Corona) Mapping 859\u003c\/p\u003e \u003cp\u003e40.5 Results and Discussion 860\u003c\/p\u003e \u003cp\u003e40.6 Conclusion 865\u003c\/p\u003e \u003cp\u003eReferences 866\u003c\/p\u003e \u003cp\u003e\u003cb\u003e41 Mobile-Based Medical Alert System for COVID-19 Based on ZigBee and WiFi 869\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMunish Manas and Shivam Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e41.1 Introduction 869\u003c\/p\u003e \u003cp\u003e41.2 Hardware Design of Monitoring System 870\u003c\/p\u003e \u003cp\u003e41.3 Software Design of Monitoring System 873\u003c\/p\u003e \u003cp\u003e41.4 Working of ZigBee Module 874\u003c\/p\u003e \u003cp\u003e41.5 Developed App for the Monitoring of Health 874\u003c\/p\u003e \u003cp\u003e41.6 Google Fusion Table—Online Database 875\u003c\/p\u003e \u003cp\u003e41.7 Application Developed for Health Monitoring System 876\u003c\/p\u003e \u003cp\u003e41.8 Conclusion and Future Work 877\u003c\/p\u003e \u003cp\u003eReferences 877\u003c\/p\u003e \u003cp\u003eIndex 879 \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430957871384,"sku":"9781119791829","price":200.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119791829.jpg?v=1784766169","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/handbook-of-intelligent-computing-and-optimization-for-sustainable-development-hardback-9781119791829","provider":"Freshly Printed Books","version":"1.0","type":"link"}