Multimodal and Tensor Data Analytics for Industrial Systems Improvement
Language: English
Published by Springer, Springer International Publishing Mai 2025, 2025
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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This item is printed on demand - Print on Demand Titel. Neuware -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 technologies and the shift toward the Internet of Things (IoT) has transformed and will continue to transform data analytics by producing new requirements and more complex forms of data. The abundance of data creates an unprecedented opportunity to design more efficient systems and make near-optimal operational decisions. On the other hand, the structural complexity and heterogeneity of the generated data pose a significant challenge to extracting useful features and patterns for making use of the data and facilitating decision-making. Therefore, continual research is needed to develop new statistical and analytical methodologies that overcome these data challenges and turn them into opportunities.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 404 pp. Englisch.…
Seller Inventory # 9783031530944
- Title
- Multimodal and Tensor Data Analytics for Industrial Systems Improvement
- Author
- Nathan Gaw
- Publisher
- Springer, Springer International Publishing Mai 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3031530942
- ISBN 13
- 9783031530944
- Item weight
- 610 grams
- Dimensions
- 235x155x22 mm
"Synopsis" may belong to another edition of this title.
About the Author
Nathan Gaw is an Assistant Professor of Data Science in the Department of Operational Sciences at Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA. His research develops new statistical machine learning algorithms to optimally fuse high-dimensional, multi-modal data sources to support decision making in military, healthcare and remote sensing. He received his B.S.E. and M.S. in biomedical engineering and a Ph.D. in industrial engineering from Arizona State University (ASU), Tempe, AZ, USA, in 2013, 2014, and 2019, respectively. Dr. Gaw was a Postdoctoral Research Fellow at the ASU-Mayo Clinic Center for Innovative Imaging (AMCII), Tempe, AZ, USA, from 2019-2020, and a Postdoctoral Research Fellow in the School of Industrial and Systems Engineering (ISyE) at Georgia Institute of Technology, Atlanta, GA, USA, from 2020-2021. He has also served as chair of the INFORMS Data Mining Society, and a member of IISE and IEEE.
Mostafa Reisi Gahrooei is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Florida. His research interests focus on data-driven modelling and monitoring complex and distributed systems by developing efficient methodologies and algorithms for modelling high-dimensional and multimodal data. The applications of his work are in precision agriculture, manufacturing, healthcare, and transportation systems. He is a co-director of the Data Informatics for Systems Improvement and Design (DISIDE) lab. Dr. Reisi is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial and Systems Engineers (IISE).
"About the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
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