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Titlebook: Multimodal and Tensor Data Analytics for Industrial Systems Improvement; Nathan Gaw,Panos M. Pardalos,Mostafa Reisi Gahrooe Book 2024 The

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發(fā)表于 2025-3-21 19:41:59 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Multimodal and Tensor Data Analytics for Industrial Systems Improvement
編輯Nathan Gaw,Panos M. Pardalos,Mostafa Reisi Gahrooe
視頻videohttp://file.papertrans.cn/641/640770/640770.mp4
概述Demonstrates practical applications focusing on manufacturing, healthcare, agriculture, and other applications.Discussion of pros and cons for each methodology, providing a pathway for future research
叢書名稱Springer Optimization and Its Applications
圖書封面Titlebook: Multimodal and Tensor Data Analytics for Industrial Systems Improvement;  Nathan Gaw,Panos M. Pardalos,Mostafa Reisi Gahrooe Book 2024 The
描述This volume covers the latest methodologies for using multimodal data fusion and analytics across several applications. The curated content presents recent developments and challenges in multimodal data analytics and shines a light on a pathway toward new research developments. Chapters are composed by eminent researchers and practitioners who present their research results and ideas based on their expertise. As data collection instruments have improved in quality and quantity for many applications, there has been an unprecedented increase in the availability of data from multiple sources, known as modalities. Modalities express a large degree of heterogeneity in their form, scale, resolution, and accuracy. Determining how to optimally combine the data for prediction and characterization is becoming increasingly important.?Several research studies have investigated integrating multimodality data and discussed the challenges and limitations of multimodal data fusion. This volume provides a topical overview of various methods in multimodal data fusion for industrial engineering and operations research applications, such as manufacturing and healthcare..Advancements in sensing technol
出版日期Book 2024
關(guān)鍵詞multimodal data; multivariate statistics; tensor data analytics; Bayesian multimodal; spatio-temporal da
版次1
doihttps://doi.org/10.1007/978-3-031-53092-0
isbn_softcover978-3-031-53094-4
isbn_ebook978-3-031-53092-0Series ISSN 1931-6828 Series E-ISSN 1931-6836
issn_series 1931-6828
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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https://doi.org/10.1007/978-3-031-53092-0multimodal data; multivariate statistics; tensor data analytics; Bayesian multimodal; spatio-temporal da
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Book 2024ity data and discussed the challenges and limitations of multimodal data fusion. This volume provides a topical overview of various methods in multimodal data fusion for industrial engineering and operations research applications, such as manufacturing and healthcare..Advancements in sensing technol
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