Nature Inspired Computing for Data Science

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This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.

Focuses on the advances in nature-inspired computing and its value in data science Presents contributions from various fields of computational intelligence, machine learning, deep learning, and nature-inspired computing to build intelligent systems for real-time data analytics Includes fundamentals, applications, algorithms and case studies of the advances and research in the fields of nature-inspired computing, data science and engineering

Inhalt
An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO based Optimized Neural Network.- Mid-term Home Health Care Planning Problem with Flexible Departing Way for Caregivers.- Performance Analysis of NASNet on Unconstrained Ear Recognition.- Optimization of performance parameter for Vehicular Ad-hoc NETwork (VANET) using Swarm Intelligence.- Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data.- Application of Genetic Algorithms for Unit Commitment and Economic Dispatch Problems in microgrids.- Application of Genetic Algorithms for Designing Micro-Hydro Power Plants in Rural Isolated Areas - a case study in San Miguelito, Honduras.- Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making.- Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-Objective Particle Swarm Optimization.- Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study.- An Evolutionary Algorithm based Hybrid Parallel Framework for Asia Foreign Exchange Rate prediction.<p

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783030338190
    • Auflage 1st edition 2020
    • Editor Minakhi Rout, Himansu Das, Jitendra Kumar Rout
    • Sprache Englisch
    • Genre Allgemeines & Lexika
    • Lesemotiv Verstehen
    • Größe H241mm x B160mm x T23mm
    • Jahr 2020
    • EAN 9783030338190
    • Format Fester Einband
    • ISBN 3030338193
    • Veröffentlichung 24.01.2020
    • Titel Nature Inspired Computing for Data Science
    • Untertitel Studies in Computational Intelligence 871
    • Gewicht 635g
    • Herausgeber Springer International Publishing
    • Anzahl Seiten 312

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