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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Gaw, Nathan
Pardalos, Panos M.
Gahrooei, Mostafa Reisi

Hardcover

Series: Springer Optimization and Its Applications, Book 211

General MathematicsProbability & Statistics

ISBN10: 3031530918
ISBN13: 9783031530913
Publisher: Springer
Published: May 17 2024
Pages: 394
Weight: 1.64
Height: 0.94 Width: 6.14 Depth: 9.21
Language: English
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.

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