Advances in machine learning for big data analysis / / Satchidananda Dehuri, Yen-Wei Chen, editors

• Pubblicazione: Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (xix, 239 pages) : illustrations (some color), charts
• Disciplina: 780
• Soggetto topico: Computational intelligence; Machine learning; Artificial intelligence - Data processing; Big data
• Persona (resp. second.): DehuriSatchidananda; ChenYen-Wei
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 1. Multi-objective ant colony optimization : an updated review of approaches and applications -- 2. Cost-effective detection of cyber physical system attacks -- 3. A prognostic approach to crime analysis -- 4. A counter-based profiling scheme for improving locality through data and reducer placement -- 5. Hybridization of the higher order neural networks with the evolutionary optimization algorithms--an application to financial time series forecasting -- 6. Supply chain management (SCM) : employing various big data and metaheuristic strategies -- 7. Value of random vector functional link neural networks in software development effort estimation -- 8. Hybrid approach to prevent accidents at railway : an assimilation of big data, IoT and cloud -- 9. Hybrid decision tree for machine learning : a big data perspective.
• Sommario/riassunto: This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems.
• Titolo autorizzato: Advances in machine learning for big data analysis
• ISBN: 981-16-8929-6; 981-16-8930-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910743352903321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent systems reference library ; ; v. 218.