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Titlebook: Machine Learning, Optimization, and Data Science; 9th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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書目名稱Machine Learning, Optimization, and Data Science
副標(biāo)題9th International Co
編輯Giuseppe Nicosia,Varun Ojha,Renato Umeton
視頻videohttp://file.papertrans.cn/621/620734/620734.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning, Optimization, and Data Science; 9th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202
描述.This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023.?.The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state?of the art and the latest advances in the integration of machine learning, deep?learning, nonlinear optimization and data science to provide and support the?scientific and technological foundations for interpretable, explainable and trustworthy AI.?.
出版日期Conference proceedings 2024
關(guān)鍵詞computer security; evolutionary algorithms; fuzzy control; image processing; database systems; artificial
版次1
doihttps://doi.org/10.1007/978-3-031-53966-4
isbn_softcover978-3-031-53965-7
isbn_ebook978-3-031-53966-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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0302-9743 2023, which took place in Grasmere, UK, in September 2023.?.The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributio
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Exploring Image Transformations with?Diffusion Models: A Survey of?Applications and?Implementation Cions. The applications are presented in a practical and concise manner, facilitating the understanding of concepts behind diffusion models and how they function. Additionally, it includes a curated collection of GitHub repositories featuring popular examples of these subjects.
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Deep Learning Model of Two-Phase Fluid Transport Through Fractured Media: A Real-World Case Studyest the sensitivity of our method to the type of optimizer and learning rate, time step size and the number of timesteps, DNN architecture, and spatial resolution. The results of computational experiments on a real-world problem prove a good numerical stability of the solution, its computational efficiency and high precision of the PINN model.
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Reinforcement Learning for?Multi-Neighborhood Local Search in?Combinatorial Optimizationalready obtained remarkable results using offline tuning techniques. Experimental data show that our approach obtains better results than the analogous algorithm that uses state-of-the-art offline tuning on benchmarking datasets while requiring less tuning effort.
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Social Media Analysis: The Relationship Between Private Investors and?Stock Pricesing natural language processing (NLP), this paper examines reasons for the correlation between public sentiments and stock price fluctuations in the United States. Further, we demonstrate these correlations and provide promising directions for future research.
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