Budati Anil Kumar, PhD, is an associate professor at the Faculty of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation (Deemed University), Aziz Nagar Campus, Hyderabad, Telangana, India. His research interests include cognitive radio networks, software-defined radio networks, artificial intelligence, etc. He has published 53 research articles in highly reputed publishing journals and conferences. Singamaneni Kranthi Kumar, PhD, Faculty of Computer Engineering and Technology, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India. He has authored at least 30 SCI journal articles and received the prestigious “Global Teachers Award” in 2020. Li Xingwang, PhD, is an associate professor at the School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, China. He is on the editorial board of many IEEE journals and his research interests include wireless communication, intelligent transport systems, artificial intelligence, and the Internet of Things.

• Preface xvAcknowledgment xvii1 Performance Evaluation of Avionics System Under Hardware-In- Loop Simulation Framework with Implementation of an AS9100 Quality Management System 1Rajesh Shankar Karvande and Tatineni Madhavi1.1 Introduction 21.2 HILS Process and Quality Management System 41.3 HILS Testing Phase 71.4 AS9100 QMS Integrated with HILS Process 81.5 Conclusion and Suggestions 10References 102 YouTube Comment Summarizer and Time-Based Analysis 13Preeti Bailke, Rugved Junghare, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar2.1 Introduction 132.2 Literature Review 162.3 Methodology 182.3.1 YouTube Comments Data Collection 182.3.1.1 YouTube Data API Integration 182.3.1.2 get_video_comments Function 192.3.1.3 Comment Processing 192.3.1.4 Handling Pagination with get_all_video_ comments 202.3.1.5 Excel File Creation with save_to_excel 202.3.2 Datasets 202.3.3 Extractive Summarization 212.4 Result 302.5 Performance 302.6 Conclusion 31References 313 Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions 33Premanand Ghadekar, Aadesh Chawla, Sakshi Bodhe, Sharvari Bawane and Dhruv Kshirsagar3.1 Introduction 343.2 Related Works 343.3 Methodology 363.4 Comparision with Existing Systems 413.5 Future Scope 483.6 Conclusion 48Acknowledgment 49References 494 An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction 51Ramya Palaniappan, Maha Lakshmi, Namitha, Nirmala Devi and Naga Phani4.1 Introduction 524.2 Related Works 534.3 Proposed Framework 564.3.1 ML Models and Ablation Study 564.3.2 Building Ensemble Model Using AdaBoost 574.4 Experimental Setup 584.4.1 Dataset 584.4.2 Data Visualization 594.4.3 Data Pre-Processing Phase 604.4.4 Proposed Methodology 614.4.5 Performance Metrics 624.5 Results and Discussion 634.5.1 Comparison with Baseline Models 634.5.2 Comparison with Existing Literature Works 664.6 Existing Works 674.7 Conclusion and Future Work 69Dataset 69References 695 Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics 73Shelly Prakash and Vaibhav Vyas5.1 Introduction 745.1.1 Cloud Computing 755.1.1.1 Fault, Error, Failure 755.2 Related Work 775.3 Proposed Proactive Fault Detection Architecture 815.4 Conclusion 95References 956 FlowGuard: Efficient Traffic Monitoring System 99Varsha Dange, Atharva Bonde, Om Borse, Harshal Chaudhari and Sanskar Chaudhari6.1 Introduction 996.2 Literature Review 1006.3 Methodology 1136.3.1 Theory 1136.3.2 Requirement 1146.3.2.1 Hardware Requirements 1146.3.2.2 Software Requirements 1166.3.3 Workflow 1176.3.4 Flowchart 1186.4 Results and Discussions 1186.5 Conclusion 1216.6 Future Scope 121Acknowledgment 122References 122References for Pictures of Components Used 1247 A Survey on Heart Disease Prediction Using Ensemble Techniques in ml 125Sudhakar Vecha and M.V.P. Chandra Sekhara Rao7.1 Introduction 1257.2 Literature Survey 1277.3 Datasets 1287.4 Ensemble Learning in Heart Disease 1297.5 Challenges and Limitations 1347.6 Future Directions 1347.7 Conclusion 135References 1358 A Video Surveillance: Crowd Anomaly Detection and Management Alert System 139Anitha Ponraj, Umasree Mariappan, M. J. Sai Kiran, S. Tejeswar Reddy, N. Vinay and P. Bharath8.1 Introduction 1408.2 Related Work 1408.3 Dataset Description 1438.4 Problem Definition 1438.5 Proposed Methodology and System 1448.5.1 Proposed Methodology 1448.5.2 Proposed System 1468.6 Results 1488.7 Conclusion and Future Scope 1508.7.1 Conclusion 1508.7.2 Future Scope 151References 1519 Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances 153Shatakshi Bhusari, Aniket Badakh, Kalyani Daine, Nikita Gagare and Prasad Raghunath Mutkule9.1 Introduction 1549.1.1 Parallelism 1549.1.2 Quantum Speedup 1559.1.3 Quantum Entanglement 1559.1.4 Quantum Fourier Transform 1559.1.5 Quantum Machine Learning Algorithms 1559.1.6 Quantum Data Representation 1559.1.7 Quantum Sampling 1559.1.8 Quantum Annealing 1569.1.9 Hybrid Quantum-Classical Approaches 1569.2 Review of Literature 1569.2.1 Overview of Key Quantum Computing Principles 1569.2.1.1 Qubits (Quantum Bits) 1579.2.1.2 Quantum Gates 1579.2.1.3 Quantum Parallelism 1579.2.1.4 Quantum Measurement 1579.2.1.5 Quantum Fourier Transform 1589.2.1.6 Quantum Entanglement-Based Algorithms 1589.3 Basic Quantum Operations, Qubits, and Quantum Gates 1589.3.1 Basic Quantum Operations 1589.3.2 Quantum Bits (Qubits) 1589.3.3 Quantum Gates 1599.4 Quantum Machine Learning Algorithms 1599.4.1 Quantum Support Vector Machines (QSVM) 1619.4.2 Quantum Neural Networks (QNN) 1619.4.3 Quantum Clustering Algorithms 1619.4.4 Quantum Principal Component Analysis (QPCA) 1629.4.5 Quantum Boltzmann Machines 1629.4.6 Quantum Support Vector Clustering (QSVC) 1629.5 Quantum Hardware for Machine Learning 1629.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware 1639.6.1 Decoherence and Quantum Error Correction 1639.6.2 Quantum Gate Fidelity 1639.6.3 Scalability 1649.6.4 Qubit Connectivity and Crosstalk 1649.6.5 Material Science and Qubit Implementation 1649.6.6 Quantum Interconnects 1649.6.7 Thermal Management 1649.6.8 Error Mitigation Strategies 1649.7 Challenges and Limitations in Quantum Machine Learning 1659.7.1 Quantum Computational Overheads 1659.7.2 Hybrid Quantum-Classical System Integration 1659.7.3 Limited Quantum Expressibility 1659.7.4 Data Preprocessing Challenges 1659.7.5 Quantum Algorithm Verification 1669.7.6 Quantum Resource Requirements 1669.7.7 Adaptation to Quantum Hardware Constraints 1669.7.8 Limited Quantum Hardware Availability 1669.7.9 Algorithmic Complexity 1669.7.10 Quantum Model Interpretability 1669.8 Future Directions 1679.9 Conclusion 167References 16710 Multi-Band Self-Grounding Antenna for Wireless Technologies 169Ch. Siva Rama Krishna, P. Livingston, S. Jaya Chandra, J. Hari Babu and K. Sai Babu10.1 Introduction 17010.1.1 Literature Review 17010.2 Design of Antenna 17410.2.1 Design and Results at Primary Level of Antenna 17510.2.2 Design and Results at Secondary Level of Antenna 17510.3 Actual Design of Antenna 17610.4 Results of Antenna 17610.4.1 Mathematical Analysis 17810.4.2 3D Polar Plot 17810.5 Conclusions 179References 18011 Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies 183Sai Ramya, Smera C. and Sandeep J.11.1 Introduction 18411.2 Related Work 18611.3 Methodology 19411.4 Conclusion 197References 19712 File Fragment Classification: A Comprehensive Survey of Research Advances 201Teena Mary and Sreeja C.S.12.1 Introduction 20112.2 Methodology 20312.2.1 Selection Criteria 20312.2.2 Structure of the Paper 20412.3 Approaches for File Fragment Classification 20412.3.1 Signature-Based Approaches 20412.3.2 Content-Based Approaches 20612.3.3 Deep Learning-Based Approaches 20712.3.3.1 Convolutional Neural Networks (CNNs) 20812.3.3.2 Feed Forward Neural Networks (FFNNs) 20912.3.4 Hierarchical Classification Methods 20912.4 Survey Findings 21012.5 Challenges and Future Directions 21412.6 Conclusion 215References 21613 Deepfake Detection and Forensic Precision for Online Harassment 219K. Gouthami, K. Sunitha, D.U. Durgarani and M. Prathyusha13.1 Introduction 22013.2 Literature 22113.3 Theoretical Analysis and Software Simulation 22213.3.1 Theoretical Analysis 22213.3.2 Software Simulation 22313.3.3 Testing and Optimization 224References 22514 Design of Automatic Seed Sowing Machine 227Chiluka Ramesh, K. Sarada, V. Ajay Shankar and K. Ravi Kumar14.1 Introduction 22814.2 Literature Survey 22914.3 Proposed System 23214.4 Conclusions 235References 23515 In Motion: Exploring Urban Rides Through Data Analytics 237Rajkumar Sai Varun, Nimmagadda Narayana, Dudam Vipassana and Mohan Dholvan15.1 Introduction 23715.2 Literature Survey 23815.3 Proposed Methodology 24015.4 Result Analysis 24715.5 Conclusion 248References 24916 Design of Novel Chatbot Using Generative Artificial Intelligence 251Sk. Khader Zelani, Sk. Gousiya Begum, M. Chandana and N. Lakshmi Tirupatamma16.1 Introduction 25216.2 Conclusion and Future Scope 257References 25717 The Smart Nebulizer Cap for Enhanced Asthma Management 259Rossly Netala, Aadi Praharsha and Mohan Dholvan17.1 Introduction 25917.2 Literature Survey 26117.3 Methodology 26217.4 Conclusions 265References 26518 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 267Y. Bhaskara Rao, K. Rajitha, D. Vijay Harsha Vardhan, N. Naga Raja Kumari and D. Vijaya Saradhi18.1 Introduction 26818.2 Literature Survey 26918.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 27118.4 Result Analysis 27418.5 Conclusion 276References 27719 Intrusion Detection System Using Machine Learning 279Ballikura Dhanunjay, Earla Sanjay, Aakaram Karthik Raj and Mohan Dholvan19.1 Introduction 28019.2 Literature Survey 28019.3 Methodology 28119.4 Algorithm 28319.5 Implementation 28519.6 Results and Outputs 28919.6.1 User Interface 28919.7 Conclusion and Future Scope 290References 29120 Prediction of Arrival Delay Time in Freightage Rails 293Bobbala Shriya, Gudishetty Shrita, Vanga Pragnya Reddy and Nanda Kumar M.20.1 Introduction 29420.2 Literature Survey 29520.3 Methodology 29720.4 Experimental Results 30220.5 Conclusions 308References 30921 Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers 311L. Sai Nageswara Raju, T. Naman Krishn Raj, Raipole Manihas Goud and Mohan Dholvan21.1 Introduction 31121.2 Literature Survey 31221.3 Proposed Methodology 31421.4 Result Analysis 32221.5 Conclusion 322References 32322 Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study 325Venkata Rao Tirumalasetty, K. Babulu and G. Appala Naidu22.1 Introduction 32622.2 Implementation of RCA & CSA 32822.3 Simulation Results 33322.4 Conclusion 335References 33523 XGBoost Classifier Based Water Quality Classification Using Machine Learning 337Nagidi Nikhitha, Sudini Poojitha, Vooturi Arjun, K. Sateesh Kumar and D. Mohan23.1 Introduction 33823.2 Related Work 33823.3 Proposed Methodology 33923.4 Results and Discussion 34223.5 Conclusion 345References 345Index 347