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    UID:
    almahu_9949301603502882
    Format: XXVI, 764 p. 345 illus., 269 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9789811694479
    Series Statement: Smart Innovation, Systems and Technologies, 281
    Content: This book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book is a collection of high-quality peer-reviewed research papers presented in the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021) held at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, during December 11-12, 2021. The book addresses the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.
    Note: Multi-sensor data fusion for Occupancy detection using Dempster-Shafer Theory -- Sentiment Analysis: A Recent Survey with Applications and a Proposed Ensemble Algorithm -- An Automated System for Facial Mask Detection and Social Distancing During Covid - 19 Pandemic -- Detection of Insider Threats Using Deep Learning: A Review -- An Incisive analysis of Advanced Persistent Threat detection using Machine learning Techniques -- Intelligent Computing Systems For Diagnosing Plant Diseases -- Multimodal MRI Analysis for Segmentation of Intra-tumoral Regions of High-Grade Glioma using VNet and WNet based deep models -- Early Onset Alzheimer Disease Classification using Convolution Neural Network.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9789811694462
    Additional Edition: Printed edition: ISBN 9789811694486
    Additional Edition: Printed edition: ISBN 9789811694493
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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