Preference-leveled fuzzy AI for evaluating desirability in smart material classification systems

Title : Preference-leveled fuzzy AI for evaluating desirability in smart material classification systems

Abstract:

This research explores the use of Fuzzy Artificial Intelligence (AI) in Internet of Things (IoT) systems for material classification under uncertain conditions. IoT environments produce large volumes of data that are often imprecise and complex. Fuzzy AI manages this uncertainty through fuzzy rules, membership functions, and defuzzification methods. The approach combines Preference-Leveled Evaluation Functions with Fuzzy Artificial Neural Networks to classify material datasets, detect irregularities, and generate alerts. By modeling parameters such as texture, composition, and surface properties, Fuzzy AI supports context-aware and accurate decision-making, improving material identification, process reliability, and the overall performance of IoT-based industrial systems.   

Biography:

Dr. Firos A. is an Associate Professor in the Department of Computer Science and Engineering at Rajiv Gandhi University, Arunachal Pradesh, India. With over 16 years of teaching and research experience, his work focuses on Fuzzy Sets and Logics, Artificial Intelligence, Data Science, and Decision-Making Systems. He has published 146 research papers, including SCI and Scopus-indexed articles, and has contributed extensively to interdisciplinary studies linking AI, Big Data Analytics, and Cloud Computing. Currently, he serves as the Co-Principal Investigator of the Atal Community Innovation Centre (ACIC) project and continues to advance research in intelligent and sustainable computational systems.