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Full Description
The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows:
• Part I: Data Science: Theory, Concepts, and Algorithms
This part comprises five chapters on data Science theory, concepts, techniques and algorithms.
• Part II: Data Design and Analysis
This part comprises five chapters on data design and analysis.
• Part III: Applications and New Trends in Data Science
This part comprises four chapters on applications and new trends in data science.
Contents
Preface. Editors. Contributors. Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark. A Study on Meta-heuristic based Neural Networks for Image Segmentation Purposes. Study and Analysis of a Feature Subset Selection Technique using Penguin Search Optimization Algorithm. A Physical Design Strategy on a NoSQL DBMS. Large-Scale Distributed Stream Data Collection Schemes. Big Data Analysis and Management in Healthcare. Healthcare Analytics: A Case Study Approach using the Framingham Heart Study. Bioinformatics Analysis of Dysfunctional (mutated) Proteins of Cardiac Ion Channels Underlying the Brugada Syndrome. Discrimination of Healthy Skin, Superficial Epidermal Burns and Full-thickness Burns from 2D-Coloured Images Using Machine Learning. A Study and Analysis of an Emotion Classification and State Transition System in Brain Computer Interfacing. Comparison of Gradient and Textural Features for Writer Retrieval in Handwritten Documents. A Supervised Guest Satisfaction Classification with Reviews Text and Ratings. Sentiment Analysis for Decision Making Using Machine Learning Algorithms. Deep Learning Model: Emotion Recognition from Continuous Action Video. Index
