"Artificial Intelligence Annual Volume 2024" ed. by George A. Papakostas, Marco Antonio Aceves-Fernández, Mehmet Emin Aydin

This volume deals with three key areas of the advancements in AI: machine learning and data mining, computer vision, and multi-agent systems. This book should serve as a valuable resource not just to scientists dealing with AI research but also to anyone interested in its broad application areas across various disciplines.

The academic interest in artificial intelligence (AI) has grown exponentially in recent years. The rapid development of AI technologies and the interdisciplinary nature of research in AI and its applications have contributed considerably to the global popularity of this research field. The increasing availability of vast data sets and powerful computing resources has enabled the development of more complex algorithms and models to address real-world challenges. In addition, deep learning has revolutionized the field of artificial intelligence, with computer vision being at the forefront of innovations. Multi-agent systems (MAS) have also proven to be the best fitting state-of-the-art within the AI framework for raising distributed AI technologies and applications such as smart cities and the Internet of (every)thing(s). Extended with machine learning, MAS have become very popular for researchers in every field, especially in autonomous vehicular technologies.

Contents
1. Visual Recognition of Food Ingredients: A Systematic Review
2. Enhanced Lung Cancer Detection and Classification Using YOLOv8
3. Friction and Wear in Journal Bearings: Accurate Testing and Simulation with an Outlook on Predictive Maintenance with Machine Learning
4. Predicting Student Performance in Flipped Learning through Machine Learning Techniques: A Bibliometric Analysis with R
5. A Transformer-Based Architecture for Airborne Particles Forecasting: Case Study – PM2.5 in Mexico City
6. Algorithmic Innovations in Multi-Agent Reinforcement Learning: A Pathway for Smart Cities
7. Intelligent Multi-Agent Systems for Advanced Geotechnical Monitoring


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