Big Data and Data Science Engineering

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Geliefert zwischen Do., 26.02.2026 und Fr., 27.02.2026

Details

This book reports state-of-the-art results in Big Data and Data Science Engineering in both printed and electronic form. Studies in Computation Intelligence (SCI) has grown into the most comprehensive computational intelligence research forum available in the world.

This book publishes original papers on both theory and practice that address foundations, state-of-the-art problems and solutions, and crucial challenges.


Covers recent research in Big Data and Data Science Engineering Presents theory and applications Written by experts in the field

Inhalt

Using Modified SA With Adaptive Clustering for Solving The Electric Vehicle Routing Problem.-Utilizing Machine Learning Methods For Predicting Stock Market Movement A Case StudyWith The S&P 500 Index.- Career awareness of foreigners working in Hokkaido as a result of studying abroad.- Predicting Power Plant Start Up Time Leveraging Random Forest Algorithm and Univariate Selection for Enhanced Generator Performance.- Explainable Sentiment Analysis Utilizing Deep Learning Methods and LIME.- Quantum safe Hash Time locked Cross chain Transaction Mechanism.- Positive Computing through Images and Scents.- Edge Enhanced ConvNeXt Unet for Foveal Avascular Zone Segmentation from Optical Coherence Tomography Angiography.- Edge Enhanced ConvNeXt Unet for Foveal Avascular Zone Segmentation from Optical Coherence Tomography Angiography.- Wavelet Enhanced GAN for Image Outpainting.- Wood Strength Prediction via Log Section Images using Filtered Swin Transformer Model.- Lifelog Tagging System Using Physiological and Environmental Indicators.- Governance of Cyber Threat Information Sharing for Public Private Partnerships Comparative Analysis of National Cases.- Extracting Unique Keywords Related to Customer Engagement from Review Text Using UncorrelatedWeights Estimation in Neural Networks.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783031783722
    • Genre Technology Encyclopedias
    • Editor Roger Lee
    • Lesemotiv Verstehen
    • Anzahl Seiten 196
    • Herausgeber Springer
    • Größe H241mm x B160mm x T16mm
    • Jahr 2025
    • EAN 9783031783722
    • Format Fester Einband
    • ISBN 3031783727
    • Veröffentlichung 01.05.2025
    • Titel Big Data and Data Science Engineering
    • Untertitel Volume 7
    • Gewicht 499g
    • Sprache Englisch

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