3rd International Conference on Artificial Intelligence and Smart Applications (AISA’25), Volume 1: Artificial Intelligence, IoT, and Smart Applications

Nyhet

Häftad, Engelska, 2026

4 189 kr

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This book presents a selection of peer-reviewed papers from the Third International Conference on Artificial Intelligence and Smart Applications (AISA’25), focusing on research topics related to artificial intelligence and data-driven technologies.This book covers a broad range of themes, including artificial intelligence, machine learning, deep learning, data analysis, smart applications, healthcare technologies, and Internet of Things (IoT) systems. The chapters address both theoretical approaches and applied solutions, highlighting innovative methods and real-world applications in intelligent and connected environments.Bringing together work from international researchers and practitioners, this book offers a comprehensive overview of current advances and emerging trends in AI-based systems. It is intended for researchers, engineers, professionals, and graduate students seeking insights into intelligent technologies and their applications in healthcare and smart environments.

Produktinformation

Utgivningsdatum2026-04-23
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Networks and Systems
Antal sidor264
FörlagSpringer Nature Switzerland AG
ISBN9783032187154

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Miljöekonomi inom Ekonomi och ledarskap
Artificiell intelligens inom Data och it
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Mohcine Kodad is an associate professor of Computer Science at the Higher School of Technology (ESTO), Mohammed First University, Oujda, Morocco, where he has been serving since 2017. He holds a Ph.D. in Computer Science (2016) and previously earned a Specialized Master’s degree in Software Quality. His career spans academia, applied IT engineering, and institutional leadership. Prior to joining the university, he worked for three years as a design and development engineer at an International IT company based in Oujda, Morocco, where he led the development and management of digital solutions for various industrial projects. His professional experience notably includes contributions to digital public platforms and corporate information systems. His research interests focus on Social Network Analysis, Machine Learning, Smart Cities, SIG, NLP, and Data Analysis.Omar Moussaoui is a full professor of Computer Science at the Higher School of Technology of Oujda (ESTO), University Mohammed Premier, Morocco, where he has been a member of the Computer Science Department since 2013. He obtained his Ph.D. in Computer Science from the University of Cergy-Pontoise, France, in 2006, and his Habilitation (Associate Professorship) in Computer Science from University Mohammed Premier in 2018. He is currently the director of the MATSI research laboratory. His research activities focus on cybersecurity, the Internet of Things (IoT), artificial intelligence, and emerging intelligent systems. He has authored and co-authored more than 60 scientific publications in indexed international journals and conferences, as well as several book chapters and edited volumes, with an h-index of 15 (Scopus). Professor Moussaoui has served on scientific and organizing committees of numerous international conferences and acts as a reviewer for several international journals.

An Analytical Study of Fine-Tuning Approaches for LLMs in Software Engineering.- Towards a New Evaluation Protocol for Grey Sheep User Detection Approaches.- The Role of Reasoning in LLMs: A Comparative Study on Machine Reading Comprehension using the SQuAD 2.0 and AdversarialQA Datasets.- Optimization of Deep Neural Networks for Complex Multivariate Data: An Overview.- Predicting VMAT Modulation Complexity Before Planning: A Comparative Analysis of Machine Learning Classifiers.- Specialized Approaches for Visual Instruction Grounding in GUI Automation.- Inspired-Logic Loss Functions for Deep Learning Using Sugeno-Weber Connective Systems.- Dual-Image-Guided Grounding of User Interfaces with MLLMs.- Image Segmentation using Covariance matrix and Dominant sets.