Nature Inspired Computing for Data Science

Przednia okładka
Minakhi Rout, Jitendra Kumar Rout, Himansu Das
Springer Nature, 26 lis 2019 - 295
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.

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Spis treści

An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO Based Optimized Neural Network
MidTerm 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 Adhoc NETwork VANET Using Swarm Intelligence
Development of Fast and Reliable NatureInspired Computing for Supervised Learning in HighDimensional Data
Application of Genetic Algorithms for Unit Commitment and Economic Dispatch Problems in Microgrids
Application of Genetic Algorithms for Designing MicroHydro Power Plants in Rural Isolated AreasA Case Study in San Miguelito Honduras
Performance Evaluation of Different Machine Learning Methods and DeepLearning Based Convolutional Neural Network for Health Decision Making
Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multiobjective Particle Swarm Optimization
A Computational Study
An Evolutionary Algorithm Based Hybrid Parallel Framework for Asia Foreign Exchange Rate Prediction
Nature Inspired Computing for Data Science
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