Computational Intelligence and Big Data Analytics: Applications in Bioinformatics


Computational Intelligence and Big Data Analytics: Applications in Bioinformatics (SpringerBriefs in Applied Sciences and Technology)
By 作者: Ch. Satyanarayana - Kunjam Nageswara Rao - Richard G. Bush
ISBN-10 书号: 9811305439
ISBN-13 书号: 9789811305436
Edition 版本: 1st ed. 2019
Release Finelybook 出版日期: 2018-09-09
Pages 页数: 137

The Book Description robot was collected from Amazon and arranged by Finelybook

This book highlights major issues related to big data analysis using computational intelligence techniques, mostly interdisciplinary in nature. It comprises chapters on computational intelligence technologies, such as neural networks and learning algorithms, evolutionary computation, fuzzy systems and other emerging techniques in data science and big data, ranging from methodologies, theory and algorithms for handling big data, to their applications in bioinformatics and related disciplines. The book describes the latest solutions, scientific results and methods in solving intriguing problems in the fields of big data analytics, intelligent agents and computational intelligence. It reflects the state of the art research in the field and novel applications of new processing techniques in computer science. This book is useful to both doctoral students and researchers from computer science and engineering fields and bioinformatics related domains.

Contents


1A Novel Level-Based DNA Security Algorithm Using DNA Codons
2 Cognitive State Classifiers for Identifying Brain Activities
3 Multiple DG Placement and Sizing in Radial Distribution System Using Genetic
Algorithm and Particle Swarm Optimization
4Neighborhood Algorithm for Product Recommendation
5 A Quantitative Analysis of Histogram Equalization-Based Methods on Fundus
Images for Diabetic Retinopathy Detection
6 Nanoinformatics:Predicting Toxicity Using Computational Modeling
7 Stock Market Prediction Based on Machine Learning Approaches
8 Performance Analysis of Denoising of ECG Signals in Time and Frequency
Domain
9 Design and Implementation of Modified Sparse K-Means Clustering Method
for Gene Selection of T2DM
10 ldentifying Driver Potential in Passenger Genes Using Chemical Properties of
Mutated and Surrounding Amino Acids
11 Data Mining Efficiency and Scalability for Smarter Internet of Things
12 FGANN:A Hybrid Approach for Medical Diagnosing
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