Advanced Intelligent Systems for Sustainable Development (Ai2sd'2019): Volume 4 - Advanced Intelligent Systems for Applied Computing Sciences

ISBN-13: 9783030366735 / Angielski / Miękka / 2020 / 521 str.

Mostafa Ezziyyani

Spis treści

A comparative evaluation of classification algorithms for sentiment analysis using word embeddings.- A context Ontology for Semantic IOT Representation.- A Deep Neural Network Classification Approach for Alzheimer's Disease diagnosis.- A Lightweight Cellular Automata-based Cryptosystem evaluated with NIST statistical tests.- A literature review of steering angle prediction algorithms for self-driving cars.- A Multi-Criteria Analysis and Advanced comparative study Between IT Governance references.- A New Approach of a List of Items for Search Retrieval Systems.- A Rigid Visual Servoing Approach for Non-Rigid Objects Using Weighted Primitives.- A Supervised Method for Extractive Single Document Summarization based on Sentence Embeddings and Neural Networks.- An Adaptive Control Approach for Performance of Big Data Storage Systems.- An Advanced Intelligent Support System for Multimodal Transportation Network Based on Multi-Agent Architecture.- An online framework for earlier cancer diagnosis: Association rules and decision tree based approach.- An optimized Iterative Partitioning Model For Predicting Computer System Failures based on Deep Learning.- Analyzing social media opinions using hybrid Machine Learning model based on Artificial Neural Network optimized by Particle Swarm Optimization.- Anomaly Detection in Credit Card Transactions.- Application of a discrete to continuous approach based -alignment algorithm for Capillary Electrophoresis DNA sequencing correction.- Automatic Evaluation of UML Class Diagrams Using the XML Schema Matching and the Machine Learning Algorithm.

Blurb

This book gathers papers presented at the second installment of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD-2019), which was held on July 08–11, 2019 in Marrakech, Morocco. It offers comprehensive coverage of recent advances in big data, data analytics and related paradigms. The book consists of fifty-two chapters, each of which shares the latest research in the fields of big data and data science, and describes use cases and applications of big data technologies in various domains, such as social networks and health care.

All parts of the book discuss open research problems and potential opportunities that have arisen from the rapid advances in big data technologies. In addition, the book surveys the state of the art in data science, and provides practical guidance on big data analytics and data science. Expert perspectives are provided by authoritative researchers and practitioners from around the world, who discuss research developments and emerging trends, present case studies on helpful frameworks and innovative methodologies, and suggest best practices for efficient and effective data analytics.

Chiefly intended for researchers, IT professionals and graduate students, the book represents a timely contribution to the growing field of big data, which has been recognized as one of the leading emerging technologies that will have a major impact on various fields of science and various aspects of human society over the next several decades. Therefore, the content in this book is an essential tool to help readers understand current developments, and provides them with an extensive overview of the field of big data analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use big data, such as management and finance, medicine and health care, networks, the Internet of Things, big data standards, benchmarking of systems, and others.

In addition to a diverse range of applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modeling of high-dimensional data are also covered. The varied collection of topics addressed introduces readers to the richness of the emerging field of big data analytics.