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Titlebook: Data Science and Artificial Intelligence; Second International Chutiporn Anutariya,Marcello M. Bonsangue,Opim Sal Conference proceedings 20

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發(fā)表于 2025-3-21 16:26:07 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Science and Artificial Intelligence
副標(biāo)題Second International
編輯Chutiporn Anutariya,Marcello M. Bonsangue,Opim Sal
視頻videohttp://file.papertrans.cn/285/284450/284450.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Data Science and Artificial Intelligence; Second International Chutiporn Anutariya,Marcello M. Bonsangue,Opim Sal Conference proceedings 20
描述.This book constitutes the proceedings of the Second refereed?proceedings of the Second?International Conference on Data Science and Artificial Intelligence, DSAI 2024, held in Medan, Indonesia, during November 13–15, 2024...The 18 full papers, 2 short papers and 3 invited talks were included in this book were carefully reviewed and selected from 69 submissions. They are organized in the following topical sections:?Keynote Presentations; Natural and Sign Language Processing; Applications of Data Science and Artificial Intelligence; Affective Computing and AI Games; Embedded AI and Applications; Data Science; AI and Healthcare..
出版日期Conference proceedings 2025
關(guān)鍵詞Foundations of Data Science and Artificial Intelligence; Data Mining; Machine Learning; Deep Learning; I
版次1
doihttps://doi.org/10.1007/978-981-97-9793-6
isbn_softcover978-981-97-9792-9
isbn_ebook978-981-97-9793-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Die Seele in der romantischen Medizin,ng a dysarthria dataset comprising individuals with Cerebral Palsy (CP), a prevalent cause of dysarthria. Our findings demonstrate the superior performance of our speech recognition system compared to ASR system trained on normal speech data. The system was evaluated in three scenarios: initial, per
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data on employment and educational background. Various machine learning algorithms, such as Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, and Adaptive Boosting are utilized and compared. Cross-validation techniques, including stratified 10-fold cross-validation, are app
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,Die Energie-Massenbeziehung: E?=?Mc2,ating test case generation. This automation aims to enhance both the breadth and accuracy of tests and facilitate the creation of complex test scenarios. Experimental results indicate significant advancements in software testing methodologies compared to traditional methods such as Boundary Value An
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https://doi.org/10.1007/978-3-663-06822-8 questionnaires. The questionnaire was divided into two parts: the first part collected general demographic information, such as age, gender, academic background, and job tenure; the second part focused on capturing data for the Mathematical-Logic based evaluation of the efficiency levels in collect
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https://doi.org/10.1007/978-3-663-19713-3employ transfer learning on a curriculum from small to larger levels. We empirically validated this in a 3D vector-based environment, where the objective is to generate free-form tracks that facilitate a rider to move between designated points in the same vain as the flash game hit “Linerider”.
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ents in artificial general intelligence offer promising solutions to address computational challenges and improve coordination, the issue of generalization remains a significant hurdle for AI. Our research highlights the role of human experience and knowledge in interacting with open world games. By
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e heterogeneously ensembled to create this framework. It was compared with individual ML models built from same algorithms, which are considered cost insensitive as they neglect the varying misclassification costs. EEG data that classifies students as confused and non-confused during learning were u
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solution optimized for resource-constrained devices by leveraging a lightweight variant of stochastic gradient descent (SGD) called L-SGD. L-SGD is tailored for maximum speed and minimal memory usage on the Arm Cortex-M MCU family, including Arm Cortex-M7, Arm Cortex-M4, and Arm Cortex-M0+. For full
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