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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges de Aboul Ella Hassanien

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges: Studies in Big Data, cartea 77

Editat de Aboul Ella Hassanien, Ashraf Darwish
en Limba Engleză Paperback – 16 dec 2021
This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including  artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

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Specificații

ISBN-13: 9783030593407
ISBN-10: 3030593401
Ilustrații: XI, 648 p. 267 illus., 182 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.91 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Big Data

Locul publicării:Cham, Switzerland

Cuprins

Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains.- Big Data Analytics and Preprocessing.- Artificial Intelligence-based Plant Diseases Classification.- Artificial Intelligence in Potato Leaf Disease Classification: A Deep Learning Approach.- Granules-Based Rough Set Theory for Circuit Breaker Fault Diagnosis.- SQL Injection Attacks Detection and Prevention based on Neuro–Fuzzy Technique.- Convolutional Neural Network with Batch Normalization for Classification of Endoscopic Gastrointestinal Diseases.- A Chaotic Search-Enhanced Genetic Algorithm for Bilevel Programming Problems.- Bio-Inspired Machine Learning Mechanism for Detecting Malicious URL through Passive DNS in Big Data Platform.- Target Analytical: A Text Analytics Framework for Ranking Therapeutic Molecules in the Bibliome.- Earthquakes and Thermal Anomalies in a RemoteSensing Perspective.- Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques and their Application in Movie Ratings.- Predicting Student Retention Among a Homogeneous Population using Data Mining.- An Approach for Textual Based Clustering Using Word Embedding.- A Survey on Speckle Noise Reduction in SAR Images.- Comparative Analysis of Different Approaches to Human Activity Recognition based on Accelerometer Signals.- Soil Morphology based on Deep Learning, Polynomial Learning and Gabor Teager-Kaiser Energy Operators.-  Deep Layer Convolutional Neural Network (CNN) Architecture for Breast Cancer Classification Using Histopathological Images.- A survey on Deep Learning for Time-Series Forecasting.- Deep Learning for Taxonomic Classification of Biological Bacterial Sequences.- Particle Swarm Optimization and Grey Wolf Optimizer to Solve Continuous p-Median Location Problems.- Gene Ontology Analysis of Gene Expression Data Using Hybridized PSO Triclustering (HSPO-TriC) Model.- Experimental Studies of Variations Reduction in Chemometric Model Transfer for FT-NIR Miniaturized Sensors.- Smart Environments  Concepts, Applications, and Challenges.- Synonym Multi-Keyword Search over Encrypted Data Using Hierarchical Bloom Filters Index.- Assessing the Performance of E-government Services through Multi-Criteria Analysis: The Case of Egypt.- IoTIwC: IoT Industrial Wireless Controller.- Applying Software Defined Network Concepts for Securing the Client data signals over the Optical Transport Network of Egypt.- Watermarking 3D printing Data Based on Coyote Optimization Algorithm.- A 3D Geolocation Analysis of an RF Emitter Source with Two RF Sensors Based on Time and Angle of Arrival.

Textul de pe ultima copertă

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including  artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Caracteristici

Presents recent research in Machine Learning and Big Data Analytics Provides an Analysis, Applications, and Challenges of Big Data and Machine Learning Exhibits various technologies to create systems that can learn from the data in their environment and then make predictions and take actions when confronted with a new situation