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Produktbild: Data Fusion Techniques and Applications for Smart Healthcare
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Data Fusion Techniques and Applications for Smart Healthcare

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

18.03.2024

Herausgeber

Amit Kumar Singh + weitere

Verlag

Elsevier Science & Technology

Seitenzahl

442

Maße (L/B)

23,5/19,1 cm

Gewicht

910 g

Sprache

Englisch

ISBN

978-0-443-13233-9

Beschreibung

Portrait

Amit Kumar Singh is an associate professor at the Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India. Dr. Singh have been recognized as "World Ranking of Top 2% Scientists" in the area of "Biomedical Research" (for Year 2019) and "Artificial Intelligence & Image Processing" (for the Year 2020 and 2021) according to the survey given by Stanford University, USA. Currently, Dr. Singh is the Associate Editor of IEEE Trans. on Multimedia, ACM Trans. Multimedia Comput. Commun. Appl., IEEE Trans. Computat. Social Syst., IEEE Trans. Ind. Informat., IEEE J. Biomed. Heal. Informatics Etc. His research interests include multimedia data hiding, image processing, compression, biometrics, Cryptography.

Prof. Berretti is an associate professor at the Media Integration and Communication Center (MICC) and Department of Information Engineering (DINFO) of the University of Florence (UNIFI), Florence, Italy. In 2017, he obtained habilitation as full professor in Computer Engineering. Prof. Berretti has worked on image databases for effective and efficient image retrieval based on color, shape attributes and spatial relationships. He also investigated the problem of retrieval from repositories distributed on the net using resource selection and results fusion. More recently, his research interests focused on deep learning methods for face recognition, and to their generalization to non-Euclidean domains (i.e., graphs, meshes, manifolds, etc.). He is information director and associate editor of the ACM Transactions on Multimedia Computing, Communication and Applications, and of the IET Computer Vision journal. He is a member of the ACM, and senior member of the IEEE.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

18.03.2024

Herausgeber

Verlag

Elsevier Science & Technology

Seitenzahl

442

Maße (L/B)

23,5/19,1 cm

Gewicht

910 g

Sprache

Englisch

ISBN

978-0-443-13233-9

EU-Ansprechpartner

Zeitfracht Medien GmbH
Ferdinand-Jühlke-Straße 7
99095 Erfurt
DE
produktsicherheit@zeitfracht.de

Herstelleradresse

Elsevier Science & Technology
London Wall 125
EC2Y 5AS London
GB
tradeorders@elsevier.com

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  • Produktbild: Data Fusion Techniques and Applications for Smart Healthcare
  • Editors' Preface to Data Fusion Techniques and Applications for Smart Healthcare
    1. Retinopathy Screening from OCT Imagery via Deep Learning
    2. Multi-sensor data fusion in digital twins for smart healthcare
    3. Deep Learning for Multi-source Medical Information Processing
    4. Robust watermarking algorithm based on multimodal medical image fusion
    5. Fusion based Robust and Secure Watermarking Method for e-Healthcare Applications
    6. Recent Advancements in Deep Learning-based Remote Photoplethysmography Methods
    7. Federated Learning in Healthcare Applications
    8. Riemannian Deep Feature Fusion with auto-encoders for MEG Depression Classification in Smart Healthcare applications
    9. Epileptic Spike Localization using MEG MRI modality Fusion for Intelligent Smart Healthcare
    10. Early classification of time series data: Overview, Challenges, and Opportunities
    11. Deep Learning based multimodal medical image fusion
    12. Data fusion in internet of medical things: Towards trust management, security and privacy
    13. Feature fusion for medical data
    14. Review on Hybrid Feature Selection and Classification of Microarray Gene Expression Data
    15. MFFWmark: Multi focused fusion based image watermarking for telemedicine applications with BRISK feature authentication
    16. Distributed Information Fusion for Secured Healthcare
    17. Deep Learning for Emotion Recognition using Physiological Signals