Computer Science Advances : Research and Applications
Book Details
Format
Hardback or Cased Book
ISBN-10
153614844X
ISBN-13
9781536148442
Publisher
Nova Science Publishers Inc
Imprint
Nova Science Publishers Inc
Country of Manufacture
US
Country of Publication
GB
Publication Date
Feb 28th, 2019
Print length
228 Pages
Weight
468 grams
Dimensions
39.20 x 23.10 x 1.40 cms
Product Classification:
Computer science
Ksh 25,550.00
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This book is comprised of eleven chapters about computer application and research areas to discuss the latest issues and technologies. Interesting and important topics like fuzzy and cognitive applications, fuzzy classifiers, neural network, data analytics, clustering and classification models, artificial intelligence, controller area network, digital technology industry 4.0, etc. are included in this book. The first chapter presents neuro-fuzzy and cognitive researches for the development of objects on the basis of location and territory. The second chapter is about the method of parametric identification using a neuro-fuzzy classifier. The third chapter describes the research on the internet of things enabled smart campus for effectual data transit from one entity to another for classroom notes. Chapters Four and Five are dedicated to the use of advanced computer science of the medical applications. Chapter Six presents the detection and estimation of obstacle position for imminent crash prediction to enhance the driver and vehicle interface using the controller area network. Chapter Seven presents the development of accurate models for estimation of pure CO2-oil minimum miscibility pressure based on artificial intelligence methods. Chapter Eight addresses dynamic, massive data handling with swarm intelligence based algorithms for finding the solution of a difficult problem. The next chapter proposes an encryption model which focuses on bit-level confusion and bit-level diffusion followed by block-level diffusion by using 1-D chaotic maps. Chapter Ten is dedicated to digital technologies of the industry 4.0. The last chapter compares the pattern sequence based forecasting method with ARIMA in univariate time series forecasting.
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