Artificial Intelligence and Industrial Applications

Amid the dynamic growth of artificial intelligence, this book presents a collection of findings and advancements from the second edition of the A2IA-Artificial Intelligence and Industrial Applications conference. The conference, hosted by ENSAM-Meknès at Moulay Ismail University, Morocco, fosters knowledge exchange in AI, focusing primarily on its industrial applications. Covering a wide range of topics, the book highlights the adaptable nature of AI and its increasing impact on industrial sectors. It brings together contributions from an international cohort of researchers, discussing themes such as intelligent manufacturing and maintenance, intelligent supply chain management, various modes of learning including supervised, unsupervised, reinforcement, semi-supervised, and graph-based, as well as neural networks, deep learning, planning, and optimization. A defining feature of this edition is its extensive scope and emphasis on the practical applications of AI, along with its foundational elements. It facilitates an understanding of AI's current state and potential future direction, showcasing recent developments that bridge the gap between theory and practice.

Designed for a diverse readership, this book is of interest to AI practitioners, academics, and enthusiasts, as well as to those new to the field. It provides an opportunity to explore AI's critical role in industrial applications, and the practical insights it offers are likely to be beneficial for decision-making within industrial settings.

Covers recent research on artificial intelligence techniques for cyber-physical and digital twin systems Includes the proceedings of the Artificial Intelligence & Industrial Applications (A2IA2023) Written by experts in the field

Contenu
Learning to Irrigate - A model of the plant water balance.- Free and Unfree Weed Classification in Young Palm Oil Crops using Artificial Neural Network.- A Method for Bengali Author Detection using State of the Arts Supervised Machine Learning Classifiers.- Robust State of Charge Estimation and Simulation of Lithium-ion Batteries Using Deep Neural Network and Optimized Random Forest Regression Algorithm.- Advancing Lithium-Ion Battery Management with Deep Learning: A Comprehensive Review.- Contribution to Solving the Cover Set Scheduling Problem and Maximizing Wireless Sensor Networks Lifetime using an Adapted Genetic Algorithm.- Comparative Study between Double Vector Quantization Using SOM and GMM for Prediction Time Series Problem.