東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Proceedings of the third ICMDS'24/ edited by Amine Laghrib, Abdelghani Ghazdali.
其他題名:	machine learning, inverse problems and related fields /
其他題名:	ICMDS 2024
其他作者:	Laghrib, Amine.
團體作者:	International Conference on Mathematical and Computational Sciences
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	ix, 250 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-94802-2
ISBN:	9783031948022

Proceedings of the third ICMDS'24 = machine 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 ;1466..

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

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

576368
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Proceedings of the third ICMDS'24 = machine learning, inverse problems and related fields /
LDR:02614nmm a2200361 a 4500 001 2413533
003 DE-He213
005 20250612130259.0
006 m d
007 cr nn 008maaau
008 260205s2025 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 $a International Conference on Mathematical and Computational Sciences $n (3rd : $d 2024 : $c Khouribga. Morocco) $3 3789681
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 $x Congresses. $3 576368
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Engineering. $3 3409361
700 1 $a Laghrib, Amine. $3 3522335
700 1 $a Ghazdali, Abdelghani. $3 3789682
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in networks and systems ; $v 1466. $3 3789683
856 4 0 $u https://doi.org/10.1007/978-3-031-94802-2
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)