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Kashfia Sailunaz – författare

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3 produkter

  1. Mehmet Kaya, Sleiman Alhajj, Kashfia Sailunaz, Min-Yuh Day - Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection, Inbunden. Tillgänglighet: Lägg i varukorg

    Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection

    Av Mehmet Kaya, Sleiman Alhajj m. fl.

    Inbunden, 2024

    1690 kr

    Lägg i varukorg

    This book is an excellent source of knowledge for readers interested in the latest developments in social network analysis and mining, particularly with applications in healthcare and anomaly detection. It covers topics such as sensitivity to noise in features, enhancing fraud detection in …

  2. Min-Yuh Day, Kashfia Sailunaz, Sleiman Alhajj, Mehmet Kaya - Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection, E-bok. Tillgänglighet: Lägg i varukorg

    Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection

    Av Min-Yuh Day, Kashfia Sailunaz m. fl.

    E-bok, 2024

    2111 kr

    Lägg i varukorg

    This book is an excellent source of knowledge for readers interested in the latest developments in social network analysis and mining, particularly with applications in healthcare and anomaly detection. It covers topics such as sensitivity to noise in features, enhancing fraud detection in …

  3. Mehmet Kaya, Sleiman Alhajj, Kashfia Sailunaz, Min-Yuh Day - Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection, Häftad. Tillgänglighet: Lägg i varukorg

    Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection

    Av Mehmet Kaya, Sleiman Alhajj m. fl.

    Häftad, 2025

    1641 kr

    Lägg i varukorg

    This book is an excellent source of knowledge for readers interested in the latest developments in social network analysis and mining, particularly with applications in healthcare and anomaly detection. It covers topics such as sensitivity to noise in features, enhancing fraud detection in …