國立高雄大學圖資館 |

Language: English

Back

Proceedings of the third ICMDS'24machine learning, inverse problems and related fields /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Proceedings of the third ICMDS'24edited by Amine Laghrib, Abdelghani Ghazdali.
Reminder of title:	machine learning, inverse problems and related fields /
remainder title:	ICMDS 2024
other author:	Laghrib, Amine.
corporate name:
Published:	Cham :Springer Nature Switzerland :2025.
Description:	ix, 250 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learningCongresses.
Online resource:	https://doi.org/10.1007/978-3-031-94802-2
ISBN:	9783031948022$q(electronic bk.)

Proceedings of the third ICMDS'24machine learning, inverse problems and related fields /
Proceedings of the third ICMDS'24machine learning, inverse problems and related fields /[electronic resource] :ICMDS 2024edited by Amine Laghrib, Abdelghani Ghazdali. - Cham :Springer Nature Switzerland :2025. - ix, 250 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in networks and systems,14662367-3389 ;. - Lecture notes in networks and systems ;v.1..

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.

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.

ISBN: 9783031948022$q(electronic bk.)

Standard No.: 10.1007/978-3-031-94802-2doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Proceedings of the third ICMDS'24machine learning, inverse problems and related fields /
LDR:02615nmm a2200361 a 4500 001 683274
003 DE-He213
005 20250612130259.0
006 m d
007 cr nn 008maaau
008 251212s2025 sz s 0 eng d
020 $a 9783031948022$q(electronic bk.)
020 $a 9783031948015$q(paper)
024 7 $a 10.1007/978-3-031-94802-2 $2 doi
035 $a 978-3-031-94802-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .I61 2024
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Proceedings of the third ICMDS'24 $h [electronic resource] : $b machine learning, inverse problems and related fields / $c edited by Amine Laghrib, Abdelghani Ghazdali.
246 3 $a ICMDS 2024
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a ix, 250 p. : $b ill. (chiefly color), digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
490 1 $a Lecture notes in networks and systems, $x 2367-3389 ; $v 1466
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning $v Congresses. $3 384498
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Data Engineering. $3 839346
700 1 $a Laghrib, Amine. $3 908949
700 1 $a Ghazdali, Abdelghani. $3 997025
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in networks and systems ; $v v.1. $3 744793
856 4 0 $u https://doi.org/10.1007/978-3-031-94802-2
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)