Nature Inspired Computing for Data Science

Minakhi Rout, Jitendra Kumar Rout, Himansu Das (Herausgeber)

Buch | Hardcover

XII, 295 Seiten

2020
Springer International Publishing (Verlag)
978-3-030-33819-0 (ISBN)

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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.

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.

Erscheinungsdatum	26.01.2020
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	XII, 295 p. 133 illus., 98 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	629 g
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik
Schlagworte	Big Data • data analytics • Data Science • Nature Inspired Computing • optimization techniques
ISBN-10	3-030-33819-3 / 3030338193
ISBN-13	978-3-030-33819-0 / 9783030338190
Zustand	Neuware