This book is considered as an essential medium to introduce the up-to-date findings, new level research, and developing fields in the area of technology and computing for First International Conference on Emerging Technologies and Computing Innovations (ICETCI-2025). The objective of the ICETCI-2025 is to provide a platform to highlight state-of-the-art research work and innovative findings in technology and computing. The conference is focused on filling the gap between modern theoretical improvements and real-life issues by providing a platform for discussions among experts in different areas concerning innovative fields like AI, ML, big data, blockchain, IoT giant computing, etc. ICETCI-2025 benefits from being a multidisciplinary approach for a broad set of emerging technologies. The conference aims to foster the exchange of ideas and experiences by bringing together leaders, researchers, academics, industry practitioners as well as policymakers. Much of the diversity they provide aids unconventional thinking, applicable implementation, and strategic growth that tackles current as well as future technology hurdles. ICETCI-2025 proceedings present high quality research papers, case studies and reviews focusing on state of the art developments in domains like artificial intelligence, big data analytics, cybersecurity and privacy, IoT and blockchain and cloud computing. By collaborating with academia, industry leaders, and policymakers, these proceedings are essential for bringing attention to emerging trends and best practices. They play a critical role in promoting knowledge, stimulating innovation, and providing cross-disciplinary support to help drive technological development. This book is aimed at researchers, academics, graduate students, industry professionals, and policymakers who have an interest in the field of computing, information technologies, and their application.

Showcase next generation solutions Intersects academic-industrial perspective Is aimed at researchers, academics, graduate students

Autorentext

Dr Mangesh M. Ghonge is a founder and director of MG Aricent Educational Foundation & MG Aricent Pvt Ltd, Yavatmal, India. He received his Ph.D. in Computer Science and Engineering from Sant Gadge Baba Amravati University, Amravati, India in 2019. He authored/co-authored more than 110+ published articles in prestigious journals, books, and conferences. Besides, Dr. Mangesh Ghonge authored/edited more than 21 international books published by recognized publishers such as Elsevier, Springer, IGI Global, CRC Press Taylor & Francis, Wiley-Scrivener, Nova, De Gruyter, Bentham Science, etc. More than 1500 Google citations to his credit. He has been invited as a resource person for many workshops/FDP. He has organized and chaired many national/international conferences and conducted various workshops. He is a registered Ph.D. supervisor with SPPU, Pune.

Dr Haipeng Liu received his Bachelor and Master in Engineering degrees from Zhejiang University, China, in 2012 and 2015, respectively, and Doctor of Philosophy in Medical Sciences from the Chinese University of Hong Kong, in 2018. From 2019 to 2020, he was a research fellow with the Medical Technology Research Center, Anglia Ruskin University. Since 2020, he has been a research fellow with Coventry University, UK. He is the author of over 60 journal articles and 10 conference papers. He has delivered 12 invited talks for international conferences, universities, and research institutes. He is a receiver of two British Heart Foundation Travel Awards, a Director of Studies (Dos) of two Ph.D. students, and an editorial member of six academic journals.

Dr Mudassir Khan is a distinguished academician, researcher, author, and consultant with a robust background in computer science and data analytics. He earned his Master of Computer Applications (MCA) from Gautam Budh Technical University, India, and subsequently completed his Ph.D. in Big Data Analytics using Deep Learning from Noida International University, India. Currently, he is pursuing a postdoctoral fellowship in the Faculty of Computing Informatics at Multimedia University, Malaysia. He is a co-supervisor for Postgraduate Studies in the Centre of Postgraduate Studies, Department of Computer Science, Lincoln University College, Malaysia.

Dr Tien Anh Tran is a researcher at the Department of Electrical Engineering, University of Malta, Malta. He was a postdoctoral researcher at the Department of Naval Architecture and Ocean Engineering, Seoul National University (SNU), Seoul, South Korea (October 2022 - October 2023). He is an assistant professor (Lecturer) at the Department of Marine Engineering, Vietnam Maritime University, Haiphong City, Vietnam. He is an honorary professor at the School of Computing Science and Engineering, Galgotias University, India, and an honorary adjunct professor at the School of Computer Science and Engineering, Lovely Professional University (LPU), India. Additionally, he is an adjunct faculty at SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India. He received a B.Eng. and M.Sc. at Vietnam Maritime University, Vietnam in 2011 and 2014, respectively.

Inhalt

A Bayesian Network to Model the Influence of Energy Consumption on Greenhouse Gases in Italy.- ISIS: IoT Enabled Smart Irrigation System.-Advancing Educational Insights: A Review of Machine Learning and Deep Learning Approaches for Analyzing Students' Study Habits.- Exploring Deepfake Generation and Detection Techniques: Challenges, Datasets, and Emerging Solutions.- Advances in Retrieval-Augmented Generation Frameworks: A Comprehensive Review of NLP Applications for Women Empowerment and Social Justice.- Optimizing Indoor Positioning Systems with Machine Learning and RSS-based Algorithms.- Towards Precision in Skin Cancer Diagnosis: A Deep Learning Framework for Segmentation and Severity Analysis.- Biomarker Discovery in Alzheimer's Disease Using Machine Learning.- Role of Machine Learning Approaches to Enhance Security Mechanisms in Wireless Sensor Networks.- Evolution of Malicious URL Detection: A Review of Techniques for Malicious URL Detection and Classification.