This book emerges from the exchange of research insights and innovative ideas in the domains of Computing, Communication, and Sustainable Energy Technologies, with a focused emphasis on Communication Techniques and Intelligent Systems. It brings together contemporary research addressing smart computing paradigms, intelligent communication frameworks, and sustainable energy-driven technological solutions. With special attention to intelligent networks, signal processing, IoT-enabled systems, machine learning applications, and smart energy management, this proceedings volume serves as a comprehensive resource on technologies driving next-generation sustainable and intelligent systems. Key features of this book include:
- A broad exploration of current research trends in communication techniques, intelligent systems, and sustainable energy technologies.
- Detailed presentations of system models, algorithms, and architectures demonstrating real-world applications of intelligent communication and computing systems.
- In-depth discussions on the role of intelligent systems in areas such as wireless communication, smart grids, IoT, automation, and energy-efficient technologies.
- Analytical insights into challenges, emerging opportunities, and future research directions in sustainable communication and intelligent computing solutions.
This book is intended for research scholars, academicians, undergraduate and postgraduate students, Ph.D. candidates, industry professionals, technologists, and researchers seeking to enhance their understanding of intelligent communication systems and sustainable technological innovations.
Table of Contents:
Track 1: Signal, Image & Multimedia Processing. Scalable deep learning framework for big data-driven pattern recognition using transformer architectures and adaptive feature fusion. Image processing for ovarian cancer: from classical methods to deep learning and multimodal AI - a systematic review and comparative synthesis. SENTITRUST: a revolutionary trust paradigm for decentralized online social media. Analysis and prediction of crime hotspots using ml with stacked generalization approach. Evaluation of objective measures of optimal mask generation based enhanced speech signal. Adaptive multi-degradation image restoration using deep learning: a unified network. Land cover classification of satellite and drone images using EA-UNet. Track 2: Embedded Systems, IoT & Cyber-Physical Networks. Image caption generator using CNN and LSTM. Hybrid reinforcement approach for customized learning and path suggestion. IoT enabled smart livestock health monitoring system. Density-based traffic light monitoring system using AI and ML. Track 3: Antennas, Propagation & MicrowaveTechnologies. Neuro-fuzzy hybrid network-based system for the detection of FDIA on LFC. Design and analysis of phased array beam-steering antenna for UAV relays in 5G network. A compact Fractal loaded monopole antenna with reconfigurable band rejection capability characteristics for SWB applications. High isolation & high gain multilayered SIW based MIMO antenna for sub-THz applications. Metamaterial based MIMO antenna array for 5G/6G communication with AI-powered isolation and performance prediction. A.I. assisted beamforming with beam steering butler matrix for next gen wireless system. Analysis of a circular slot loaded square patch antenna for 5.8GHz WLAN/WiMAX band. Track 4: Cybersecurity, Blockchain & Information Technologies. AI-Powered deepfake detection and biometric identity verifier using zk-STARKs: A zero - knowledge approach to secure digital authentication. Role of artificial intelligence in detecting and preventing phishing attacks. Customer churns prediction in banking using machine learning. The Use of Blockchain in Blood Bank Management Systems: Blood Chain. Hybrid machine learning techniques for real-time threat detection in wireless sensor networks: a cybersecurity perspective. Dual mode hybrid encrypted file sharing system. Track 5: Artificial Intelligence, Machine Learning & Data Science. Smart farming in the food industry using artificial intelli-gence and machine learning. A novel spatio-temporal machine learning model for high-accuracy PM2.5 Concentration Forecasting. Designing an ensemble of deep learning models for enhanced content-based image retrieval. Ensemble-based multi-modal framework for predicting rheumatoid arthritis progression. A Hybrid Bi-LSTM–CNN framework for sentiment classification of E-commerce reviews. GAN-based data augmentation for improved brain tumor classification in medical imaging. Automated music genre recognition using convolutional neural networks and audio feature engineering. Classification of tyre health conditions using CNN based image classifier. Quantum optimization algorithms for wheat harvest classification: a hybrid computational approach. GesNova: real-time Indian sign language translation through hand gesture recognition to text and speech. Role of artificial intelligence and fintech in simulating investor behavior. A machine learning predictive model to determine the binary result status of a student. Comparative analysis of machine learning and deep learning-based approaches for pedestrian detection. Early prediction of autism spectrum disorder in adults using supervised machine learning techniques. Advancements in human-machine integration through deep learning-based brain-computer interfaces. A study of machine learning methods for detecting sentiment in social media content. AI-driven personalization and its influence on the e-commerce customer journey. Adaptive multimodal fusion for stable emotion recognition in the presence of modality failure. AI-based symptom interpretation for disease detection and personalized treatment recommendations. A contextual approach to privacy risk and adaptive epsilon allocation in Privaware-AI using an LLM-powered RAG pipeline. Comparative analysis of custom CNN architectures and pretrained models for pneumonia detection using chest x-rays. A comprehensive review on cotton plant growth monitoring using mobile imagery and deep learning. Continuous health monitoring through wearable devices and machine learning. Stacking ensemble based automated detection of wine quality. Predicting the market: a critical review of machine and deep learning approaches for housing price prediction. Deep generative models for synthetic medical image augmentation to improve rare disease diagnosis. SOULENCE – An AI-powered emotional well-being support system. A comprehensive review on intelligent legal insight systems using artificial intelligence and natural language processing. Prognosis of liver cirrhosis by way of machine and deep learning techniques: a methodical review. dlib-CNN powered automated attendance system. Conversational AI in a New Era: a comprehensive review of models, applications, and evaluation in an AI-driven world. An integrative review of deep learning-based approaches for Indian sign language to speech translation. MailGen: LLM powered framework for automated job candidate matching and intelligent email outreach in recruitment. The impact of AI-powered chatbots on lead conversion in Instagram direct messaging. Leaf disease detection using efficient NetV2. Enhanced parking space detection system using CNN and python: a cost-effective scalable approach. An LLM-based system for technical interview practice for software engineers. A comparative performance review of YOLOvN architectures with a special focus on pothole detection for YOLOv8 -YOLOv12. A quality analysis on open-source data. Reality at risk: an analysis of deepfake video detection approaches. AI creditworthiness evaluator using agentic AI. Cerebo Shield: adversarially robust ai for brain tumor MRI analysis. A review of AI-driven interview preparation systems using generative AI. Cryptocurrency price prediction using bi-LSTM and GRU. Olympic data analyzer: a comprehensive tool for data-driven insights into olympic history and performance trends. A review of lungs cancer detection using deep learning. An optimised classification framework for employee attrition prediction. Robust attendance system using machine learning. A deep learning framework for real-time facial emotion recognition using CNN. Rainfall-induced landslide prediction using machine learning methods. Forecasting financial futures: a LSTM framework for stock market prediction. CLEAN: cyberbullying language evaluation AI network. Quantitative feedback mapping for enhanced accuracy in self-mixing interferometry. AIQA: agentic intelligent quality analyst. Enhanced heart disease prediction using an optimized lightGBM framework with adaptive winsorization. An optimized LightGBM framework for enhanced heart disease prediction using SMOTE–ENN and optuna optimization. Automatic liver cirrhosis detection using support vector machines. Track 6: Electronic Design, VLSI & Digital Circuits. Accident detection on roads and tunnels using deep learning technique. Standard cell library based processor design using MGDI technique. Track 7: Cloud, Quantum & Advanced Computing Paradigms. Quantum data security and transmission – classical approach for QKD. Track 8: Biomedical Systems, Bioelectronics & Oceanic Engineering. Hybrid deep learning and handcrafted feature approach for diabetic dry eye diagnosis via tear ferning analysis. Kosuri Srinivasa Rao, Neelapala Anil Kumar, GGS Pradeep, G. Ramana Murthy & Siva Rama Krishna Madeti. Efficient multimodal biometric authentication in healthcare using optimized fully homomorphic encryption and GPU acceleration. Track 9: Miscellaneous and Interdisciplinary Topics. Genetic pattern recognition for disease prediction using machine learning and fuzzy logic. Hand motion recognition system: an experimental comparative analysis of classical, deep learning, and hybrid models. Compact hexa-band nested microstrip antenna for X/Ku/K-band and radar applications. Optimizing soil nutrient prediction with SMOTE and stacking of machine learning models. AI-enhanced ultrasound diagnostics for detecting fetal abnormalities. Early identification of PCOD through artificial intelligence leveraging lifestyle and clinical indicators. Machine learning and AI frameworks for accurate diabetes risk assessment. Spatial attention driven CNN for breast cancer image classification with Grad-CAM visualization. True News: a comparative study of tripartite political bias classification models for news headlines. Innovative pedagogy and transformative practices of teaching learning for STEM facilitators.
About the Author :
Ram Lal Yadava, Ph.D., is a Professor in the Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology (GCET), Gr. Noida, U.P, INDIA. He received his doctorate in Electronics Engineering from the IIT-BHU University. He joined VIT University, Vellore, in 2001 and served as Head of the Microwave Division, Coordinator (R&D), and Coordinator (UG) of Electrical Sciences. During his service at VIT University, he was deputed as visiting faculty to Kigali Institute of Science and Technology (KIST), Kigali, Rwanda, Central Africa. He has served as coordinator of M.Tech, HOD, and Head Exams at GCET. Dr. Yadava has organized several guest lectures, short-term training programs, and conferences sponsored by DST and AICTE, Govt. of India, in the field of microwave antennas and wireless & optical communications. His research areas include: microwave antennas and communications. He has guided five Ph.D. scholars, 30 M.Tech students, and several B. Tech projects, and he has 150 publications to his credit.
Anil Kumar Sethi is a Professor in the Department of Mechanical Engineering at Galgotias College of Engineering & Technology, Greater Noida, with over 25 years of teaching and academic experience. He holds a Ph.D. in Mechanical Engineering and an M.E. in Welding Engineering from IIT Roorkee. His academic and research interests include thermal engineering, renewable energy, advanced materials, artificial intelligence applications in engineering, and manufacturing processes. Dr. Sethi has published extensively in reputed international journals and is a German patent holder. He has actively participated in numerous FDPs, workshops, and international conferences. His career reflects strong contributions to teaching, research, and academic leadership.
Amanpreet Singh Saini is an academic professional and researcher with interests in intelligent systems, computing technologies, and applied communication solutions. He has actively contributed to research, technical training, and academic coordination. His involvement in conference organization, editorial support, and scholarly activities reflects his commitment to promoting quality research and innovation in emerging technological domains.
