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  • Advances in Data-Driven Computing and Intelligent Systems: Selected Papers from ADCIS 2024, Volume 4

    Advances in Data-Driven Computing and Intelligent Systems by Bansal, Jagdish Chand; Saha, Snehanshu; Coello, Carlos A. Coello; Rathore, Hemant;

    Selected Papers from ADCIS 2024, Volume 4

    Series: Lecture Notes in Networks and Systems; 1445;

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      • Publisher's listprice EUR 267.49
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        110 941 Ft (105 658 Ft + 5% VAT)
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      • Discounted price 97 628 Ft (92 979 Ft + 5% VAT)

    110 941 Ft

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    Product details:

    • Publisher Springer Nature Singapore
    • Date of Publication 3 January 2026
    • Number of Volumes 1 pieces, Book

    • ISBN 9789819671397
    • Binding Paperback
    • No. of pages634 pages
    • Size 235x155 mm
    • Language English
    • Illustrations XXIV, 634 p. 284 illus., 218 illus. in color. Illustrations, black & white
    • 700

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    Long description:

    This book is a collection of best-selected research papers presented at the International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2024) held at BITS Pilani, K K Birla Goa Campus, Goa, India, during September 20–21, 2024. It includes state-of-the-art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book is useful for academicians, research scholars, and industry persons.

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    Table of Contents:

    Optimizing Traffic Signal Timings in Urban Intersections A Methodology Incorporating PCU and Lane Width.- Texture Feature Analysis and Clustering of Remotely Sensed Data using Gray Level Co-occurrence Matrix and Haralick Features.- Comparative Analysis of Deep Learning Models for VM Load Prediction.- Optimizing Task Allocation in Cloud Computing Environments using Hybrid Meta-heuristic Algorithms.- Contrastive Scenarios Enhancing ML Model Interpretability.

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