Nature Inspired Computing for Data Science / / edited by Minakhi Rout, Jitendra Kumar Rout, Himansu Das

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (xii, 295 pages) : illustrations
• Disciplina: 006.38
• Soggetto topico: Engineering—Data processing; Computational intelligence; Big data; Artificial intelligence; Data Engineering; Computational Intelligence; Big Data; Artificial Intelligence
• Persona (resp. second.): RoutMinakhi; RoutJitendra Kumar; DasHimansu
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Nature Inspired Computing for Data Science
• ISBN: 3-030-33820-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910484337403321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Computational Intelligence, . 1860-949X ; ; 871