Smart Data Intelligence: Proceedings of ICSMDI 2025(Algorithms for Intelligent Systems)

About the Book

This book presents high-quality research papers presented at 5th International Conference on Smart Data Intelligence (ICSMDI 2025) organized by Kongunadu College of Engineering and Technology at Trichy, Tamil Nadu, India, during January 2025. This book brings out the new advances and research results in the fields of algorithmic design, data analysis, and implementation on various real-time applications. It discusses many emerging related fields like big data, data science, artificial intelligence, machine learning, and deep learning which have deployed a paradigm shift in various data-driven approaches that tends to evolve new data-driven research opportunities in various influential domains like social networks, health care, information, and communication applications.

Table of Contents:
Emerging Trends in Alzheimer's Disease Classification: Modalities and Methods.- A Comparative Analysis of Open-Source Large Language Models for the Analysis of Documents.- Exploring Skin Cancer Types and Stages: A Comprehensive Review.- Deep Learning for Short-Term Weather Nowcasting: A Critical Review of Recent Advances.- A Comprehensive Review of Design and Parameters Influencing Low Noise Amplifier.- Hybrid Encryption for Enhanced Data Security: A Synergistic Approach with ECC and AES.- Sustainable Urban Mobility: Reducing Carbon Footprint with Advanced Autonomous Fleet Optimization.- Hybrid Deep Learning Approach for Wildlife Monitoring from Aerial Forestry Images.- A Review of Traffic Congestion Prediction Using Machine Learning.- A Review of Traffic Congestion Prediction Using Machine Learning.- Comparative Analysis of RNN and its Variants for Speaker Recognition.- Leveraging Trusted Platform Modules and OCR for Robust, HITL-Enabled Transportation Management Systems.- Uncertainty Detection in Sentiment Analysis using Machine Learning Techniques.- Enhancing Rice Leaf Disease Detection Using CMR-Based Augmentation with DenseNet201.- Unraveling the Complexities: The Patentability of Artificial Neural Networks.- Artificial Intelligence Driven Natural Language Processing Innovations for Enhancing Multimodal Translation and Communication Applications.- Advanced Geant4 Simulation Approach for Evaluating Energy Deposition in Multicellular Boron Neutron Capture Therapy.- Skin lesion segmentation on ISIC Melanoma images by employing Artificial Gorilla Troops Optimizer.- Cutting-Edge Diagnostics: Transfer Learning and U Net-Based Segmentation for Micronutrient Deficiency Identification in Fingernails.- Dermalytics High Performance CNN Models For Skin Cancer Diagnosis.- Real-Time Lane Object Detection Using Clrernet, Scnn And Yolo.- A Survey of ADAS deployment in automobile navigation using deep learning.- Real-Time Sign Language Recognition and Translation Using Enhanced Inception V5 Architecture.- Real-Time Sign Language Recognition and Translation Using Enhanced Inception V5 Architecture.- IOT Based Smart Livestock Monitoring For Rural Communities.- Implementing Vision Transformers for Precision Disease Detection in Spinach Cultivation.- Leveraging Optimized BiLSTM and XGBoost for Early Detection of Alzheimer Disease.- CNN-Based Automated Disaster Damage Assessment Using Satellite Imagery.- VerbaVision Pro - Platform for Document Management.- A Comparative Study of CNN Models for Real-Time Driver Drowsiness Detection.- Flex Talk: Gesture-to-Speech Converter.- AISCliteracy: Assessing AI and Cybersecurity Literacy Levels and Learning Needs of Students.- Optimizing Classification Algorithms with Hybrid Deep Learning Models for Enhanced Disease Prediction.- Hybrid Worm Detection Systems for Enhanced Network Security.- Automated Immune Cell Classification Using Machine Learning.- Enhanced Yolo Based Traffic Monitoring System For Efficient Vehicle Tracking In Urban Environment.- A Comparative Evaluation of AI Tools for Voice Cloning and Lip Syncing in Multimedia Content Creation.- Enhanced Brain Tumor Classification with a Weighted Ensemble of Pre-Trained Models.- Integrated Monitoring, Image Capture, And Real-Time Alerts For Smart Agricultural Wildlife Management.

About the Author :
Asokan Ramasamy received his BE degree in electronics and communication from Bharathiar University and MS degree in electronics and control from Birla Institute of Technology. He obtained MTech degree in electronics and communication from Pondicherry Engineering College, with distinction. He obtained PhD in information and communication engineering from Anna University, Chennai. He is currently the Principal, Kongunadu College of Engineering and Technology, Thottiyam, TamilNadu, India. He has published more than 65 papers in national and international journals and conferences. He has over 25 years of teaching experience. He is a member of various scientific and professional societies. His areas of interest include wireless networks, network security and image processing. Diego P. Ruiz received the M.S: and Ph.D. degrees in Physics from the University of Granada, Spain, in 1991 and 1995 respectively. He was a teacher in the University of Malaga in 1994-95 and he is currently full professor at the Faculty of Sciences of the University of Granada. He is also coordinator of courses in the open training classroom of the University of Granada. He is interested and participates in several commissions and projects for the assessment of teaching quality and teaching innovation, and collaborates in science dissemination activities through the Scientific Culture Unit of the University of Granada. His current research interest are environmental pollution and its modeling, especially acoustics and electromagnetic pollution, signal processing and energy efficiency in buildings. His main contributions have been in the development of signal processing algorithms for the automatic classification of radar objects, analysis and modeling of data coming from physical agents (noise and vibrations), development of indoor air quality indices and new technologies for pollution control. Vasile Daniel Păvăloaia has a PhD in AIS since 2008 and a post-doctoral diploma since 2012. He is Professor at the Department of Accounting, Business Informatics and Statistics and since 2019 he is a PhD advisor. His teaching subjects and research interests include AI for Business, Enterprise Resource Planning, IT for Business Administration and Public Sector, Intelligent informatics and big data analytics. He is a Fulbright grantee and performed a graduate program at Washington State University between 2003-2004.