Proceedings of the International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

lt;p>Dr. E. S. Gopi has solely authored 9 books and edited 1 book in the area of signal processing and pattern recognition. He has got several research papers published in the reputed journals, reviewed book chapters and conference proceedings. He has 25 years of teaching and research experience. He is the coordinator for pattern recognition and the computational intelligence laboratory. He is currently Associate Professor, Department of Electronics and Communication Engineering, National Institute of Technology, Trichy (Government of India). His books are widely used all over the world. His book on "Pattern recognition and Computational intelligence using Matlab", Springer, was recognized as one of the best eBooks under the "pattern recognition" and "Matlab" categories by the Book authority, Authorities' leading site for book recommendations by thought leaders. He is the series editor for the series "Signals and Communication Technology", a Springer publication. He has completed the project offered by GTRE (DRDO) as the principal investigator on "Hunting representative sensors and constructing regression model for between sensor outcomes using ML". His video course on "Pattern recognition", "Statistical theory of Communication" and "Linear algebra and stochastic process" are well appreciated by his fellow students. He is also serving as one of the Workshops, Tutorials & Symposia officers for Machine Learning For Communications Emerging Technologies Initiative (IEEE ComSoc). He organized the first virtual international conference (MDCWC2020) at NIT, Tiruchirappalli and edited the proceedings published by Springer. He has organized many workshops, which include MDCWC2021 as the special session during the IEEE Conference on ICIAfS2021, MDCWC2022, the first long virtual workshop at NIT, Tiruchirappalli. Recently, the second International conference on MDCWC2023 was organized by him. He was the convener for the conference. He has delivered many invited talks, which include the talk under the Global Initiative of Academic Networks (GIAN) course on "Machine learning for wireless communication" and Speaker for the IEEE Training school in Machine learning for wireless communication, TOMSK Polytechnic University. His research interests include Machine intelligence, pattern recognition, Statistical signal processing and Computational intelligence.

Dr. P. Maheswaran obtained his PhD from IIITDM Kancheepuram in the area of wireless communication. At present, he is serving as an Assistant Professor in the Dept. of ECE, National Institute of Technology, Tiruchirappalli. Between 2011 and 2013, he was with Tata Consultancy Services as a Systems Engineer. He briefly served as an Assistant Professor in the Dept. of ECE, SRM Institute of Science and Technology, Chennai from 2017 to 2018. From 2019 to 2020, he was with IIT Madras as a postdoctoral fellow. His research areas include wireless communication, signal processing, cooperative communication, MIMO communication system, index modulation, spatial modulation, and OTFS.

Part 1: Machine Learning, Deep Learning and Computational Intelligence.- Chapter 1. A novel hierarchical clustering technique to analyse style and content factorization during image recognition.- Chapter 2. Exploring Model-Level Transfer Learning to Improve the Recognition of Sinhala Speech.- Chapter 3. Metaheuristics Feature Selection Algorithms for Identification and Classification of Mango Pests Diseases.- Chapter 4. An Integrated Optimization Technique with SVM for Feature Selection.- Chapter 5. A Constant Binomial Coefficient Difference Equation Based FIR Predictor System for Signal Processing  Data mining.- Chapter 6. Generating Synthetic Text Data for Improving Class Balance in Personality Prediction.- Chapter 7. CPSNet: Channel Pixel Spatial Feature Fusion Network for Image Dehazing.- Chapter 8. ATCBBC: A novel optimizer for neural network Architectures.- Chapter 9. Application of Dempster-Shafer Theory in Sensor Data Fusion.- Chapter 10.Examining Ant Colony Algorithm with The Cat Swarm Algorithm to Improve Energy Efficiency.- Chapter 11. Machine Learning based Online Visual Tracking with Multi-featured Adaptive Kernal Correlation filter.- Part 2: Wireless Communication.- Chapter 12. Integrating long-short term memory and particle swarm optimization for intrusion detection in 5G technologies.- Chapter 13. Performance Analysis of Polar Decoding Using Linear Code Mapping with Feedback Based Ann Architecture.- Chapter 14. Packet Classification Using Improved Random Forest Algorithm.- Chapter 15. A Machine Learning Perspective of optimal data transmission in Wireless Sensor Networks (WSN).- Chapter 16. Blockchain and IPFS based solution for KYC.- Chapter 17. Analysis and Comparison of BER Performance of OFDM-IM under various fading channels using Conventional Detectors.- Chapter 18. A Survey of Non-Orthogonal Multiple Access for Internet of Things and Future Wireless Networks.- Chapter 19. Improved 3D Wireless Indoor Localization with Deep Learning Algorithms.- Chapter 20. Implementing Machine Learning for design and evaluation of Antenna Parameters.- Chapter 21. A  MIMO dual mode homogenous OFDM IM system for next-generation communications.- Chapter 22. Probability Boosted Regression for Intrusion Detection in Cyber active Space.- Part 3: Mobile data application.- Chapter 23. Surface Water-body Extraction for Landsat-8 (OLI) Imagery using Water-Indices Methods and SCM Techniques.- Chapter 24. Automated Age-related Macular Degeneration Diagnosis in Retinal Fundus Images via ViT.- Chapter 25. Traffic Light Simulation Using ALP.- Chapter 26. DNN-ILD: A Transfer Learning-based Deep Neural Network for Automated Classification of Interstitial Lung Disease from CT Images.- Chapter 27. Image Manipulation Detection using Augmentation and Convolutional Neural Networks.- Chapter 28. Smart Women Safety Solution with Alert System Using Machine Learning Approach.- Chapter 29. Cyberbullying Detection  Using  BILSTM Model.- Chapter 30. Smart Agriculture using Internet of Things.- Chapter 31. Breast Cancer Detection from Mammograms using Deep Learning and Bio-Inspired Optimization Algorithm.- Chapter 32. Detection of Echinoderms Underwater using Deep Learning Network.- Chapter 33. Malaria Parasite Detection Using Deep Learning.- Chapter 34. FiltDeepNet: Architecture for Covid Detection based on Chest X-ray Images.- Chapter 35. Novel approaches to Automatic License Plate Reading (ALPR).- Chapter 36. Crop Prediction Expert System with Ensemble Machine Learning Technique.- Chapter 37. A Comparative Analysis of Single Image-Based Face Morphing Attack Detection.- Chapter 38. Classifiers For Sentiment Analysis of Youtube Comments - A Comparative Study.- Chapter 39. Traffic Monitoring System Detecting Overspeed and Accidents from Video Input using OpenCV and Digital Image Processing.- Chapter 40. An experimental comparative analysis of human abnormal action identification on "SAIAZ" video dataset using SVM, ResNet50, and LSTM model.- Chapter 41. Comparative study on malicious URL using classifiers and boosting algorithms.- Chapter 42. Automation in Natural Rubber Latex Harvesting Field: A Review.- Chapter 43. Feature Selection based Machine Learning Model for Malware Detection.- Chapter 44. Application of Machine learning algorithm for prediction of Diabetes and Heart diseases.- Chapter 45. Damage Detection on Historical Structure using Image Processing.- Chapter 46. Eye Disease Classification Using VGG-19 Architecture.- Chapter 47. Automatic Photo Enhancer Using Machine Learning and Deep Learning with Python.- Chapter 48. Robust Touch Free Palm Vein Sensor Authentication System.- Chapter 49. An Effective Machine Learning-based Malware Detection Approach.- Chapter 50. Exploring The Performance of Various Classification Algorithms for Masked Face Recognition Using Cnn Based Features.- Chapter 51. Chess Game using Assembly Language Programming.- Chapter 52. Deep Artificial neural Network for Software Effort Estimation.- Chapter 53. 'Legal Owl', An Application for Machine Generated Legal Aid Using NLP.