Tanvir Habib Sardar – författare

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.Features:Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.Explains computing models using real-world examples and dataset-based experiments.Includes case studies, quality diagrams, and demonstrations in each chapter.Describes modifications and optimization of existing technologies along with the novel big data computing models.Explores references to machine learning, deep learning, and graph processing.This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others.Features:Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies.Explains computing models using real-world examples and dataset-based experiments.Includes case studies, quality diagrams, and demonstrations in each chapter.Describes modifications and optimization of existing technologies along with the novel big data computing models.Explores references to machine learning, deep learning, and graph processing.This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

This book examines the transformative role of artificial intelligence (AI) and data engineering in revolutionizing the healthcare landscape. It presents cutting-edge developments ranging from predictive algorithms for disease diagnosis to large-scale data systems that enhance patient outcomes. By emphasizing the synergy between AI and data engineering, the book showcases practical applications in medical imaging, clinical diagnostics, and personalized treatment strategies.It also thoughtfully examines ethical considerations, data privacy, and healthcare equity, particularly in underserved and rural populations.Key Features:Explores state-of-the-art technologies in healthcare, including image segmentation, feature extraction, feature selection, and classificationProvides real-world case studies, practical examples, and hands-on exercises for effective implementation of AI-driven solutionsBridges disciplines across computer science, data engineering, and biomedical sciences to foster cross-domain collaborationHighlights innovative research methodologies and their applications in AI-powered healthcare systemsDiscusses the role of AI in improving healthcare access, delivery, and outcomes across diverse populationsThis book is ideal for professionals, researchers, and policymakers seeking to understand and shape the future of healthcare through the lens of AI and data-driven innovation.

Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms.