This book explores the emerging field of Intelligent Data Science and its transformative impact on smart end-user applications. Contributing to the increasing need for expanded research, this book presents the intricate relationship between Intelligent Data Science and its application in smart cities and industries, focusing on the opportunities and challenges within. By showcasing the widespread role of Intelligent Data Science in augmenting the autonomous systems and applications, this book stands as an innovative force for advancing knowledge and propelling the field forward. Tailored for a diverse audience encompassing students, scholars, professionals, and policymakers, this comprehensive volume includes various research methods, offering research insights on the multifaceted dimensions of Intelligent Data Science including big data analytics, smart data grids, knowledge discovery, social networks visualization and analysis, cognitive data analysis frameworks, deep learning, multimedia data analysis, etc. As a dynamic and indispensable contribution to the evolving domain of data analysis, this book helps the readers to embark on a journey through the cutting-edge intersection of data analysis technology and innovation, where each included chapter unfolds possibilities at the forefront of data science.

Presents research works in the field of smart data intelligence Provides original works presented at ICSMDI 2026 held in Trichy, India Serves as a reference for researchers and practitioners in academia and industry

Autorentext

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 M.Tech. degree in electronics and communication from Pondicherry Engineering College, with distinction. He obtained Ph.D. 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 Pablo Ruiz Padillo 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 an associate 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 acoustic and electromagnetic pollution, signal processing and energy efficiency in buildings.

Selwyn Piramuthu is a professor of Information Systems at the University of Florida, where he has taught since Fall 1991. Trained in machine learning, his research interests also include cryptography with applications related to IoT/RFID, privacy/security, supply chain management, among others. His (co-authored with Wei Zhou) book titled, RFID and Sensor Network Automation in the Food Industry was published by Wiley in 2016. He received his B.Tech., M.S., and Ph.D., respectively, from IIT Madras, University of Arizona, and University of Illinois at Urbana-Champaign.

Inhalt

Deep Representation Learning and Split Step Fourier Method for Ultrashort Pulse Dynamics in Fiber Lasers.- Leveraging Segment Anything Model for Adaptive and Accurate Medical Image Segmentation.- Bi Directional Sign Language Translator with Integrated Speech to Sign Conversion.- Balancing the Bias Deep Imbalanced Learning through Heterogeneous Feature Synthesis.- A Multimodal Deep Learning Framework for Freshness Detection and Grading in Fruits and Vegetables.- Hybrid Deep Learning and Interpretable AI for Classifying PCOS through Ultrasound Imaging.