Proceedings of the Third ICMDS'24: Machine Learning, Inverse Problems and Related Fields

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Amine Laghrib Abdelghani Ghazdali

Proceedings of the Third ICMDS'24

Machine Learning, Inverse Problems and Related Fields

Amine Laghrib, Abdelghani Ghazdali

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English

Springer International Publishing AG
12 June 2025

Databases; Machine learning

Series: Lecture Notes in Networks and Systems

This book offers innovative insights into the integration of machine learning and inverse problems, showcasing cutting-edge methodologies that enhance computational efficiency and accuracy. By leveraging artificial intelligence, optimization techniques, and high-performance computing, it addresses complex challenges across various scientific and industrial domains. The contributions featured in this book encompass theoretical advancements and practical applications, highlighting diverse topics such as data-driven approaches, uncertainty quantification, and algorithmic innovations. This interdisciplinary collection is designed for researchers, practitioners, and students interested in the transformative potential of informatics and computational sciences. By presenting meticulously reviewed papers from the Third International Conference on Mathematical and Computational Sciences (ICMDS 2024), this issue serves as a valuable resource for fostering further research and development, inspiring new approaches to solving pressing problems through advanced computational methods.

Edited by: Amine Laghrib, Abdelghani Ghazdali
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Volume: 1466
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031948015
ISBN 10: 3031948017
Series: Lecture Notes in Networks and Systems
Pages: 250
Publication Date: 12 June 2025
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

HMM-GMM Acoustic Modeling for Arabic Speech Recognition System.- Machine Learning Prediction of Long Jump Performance Based on Biomechanical Factors.- Arabic Sign Language Classification using CNN-LSTM Integration for Enhanced Gesture Recognition.- Data Exploration by Unifying Clustering and Association Rule mining.