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16 produkter
16 produkter
E-bok
PDF, Engelska, 2024970 kr
Läs direkt efter köp
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.
E-bok
Engelska, 2024970 kr
Läs direkt efter köp
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.
Inbunden, Engelska, 2023
2 065 kr
Skickas inom 10-15 vardagar
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.
Häftad, Engelska, 2025
820 kr
Skickas inom 10-15 vardagar
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.
Inbunden, Engelska, 2024
2 431 kr
Skickas inom 10-15 vardagar
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.
- Nyhet
Häftad, Engelska, 2026
895 kr
Skickas inom 10-15 vardagar
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.
Inbunden, Engelska, 2025
2 504 kr
Skickas inom 10-15 vardagar
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.
E-bok
PDF, Engelska, 2024942 kr
Läs direkt efter köp
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.
E-bok
Engelska, 2024942 kr
Läs direkt efter köp
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.
E-bok
PDF, Engelska, 20251 022 kr
Läs direkt efter köp
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 classification Provides real-world case studies, practical examples, and hands-on exercises for effective implementation of AI-driven solutions Bridges disciplines across computer science, data engineering, and biomedical sciences to foster cross-domain collaboration Highlights innovative research methodologies and their applications in AI-powered healthcare systems Discusses the role of AI in improving healthcare access, delivery, and outcomes across diverse populations This 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.
936 kr
Läs direkt efter köp
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 classification Provides real-world case studies, practical examples, and hands-on exercises for effective implementation of AI-driven solutions Bridges disciplines across computer science, data engineering, and biomedical sciences to foster cross-domain collaboration Highlights innovative research methodologies and their applications in AI-powered healthcare systems Discusses the role of AI in improving healthcare access, delivery, and outcomes across diverse populations This 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.
Del 22 - Springer Tracts on Transportation and Traffic
Applications of Computational Learning and IoT in Smart Road Transportation System
Inbunden, Engelska, 2025
2 158 kr
Skickas inom 5-8 vardagar
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
E-bok
Engelska, 20252 840 kr
Läs direkt efter köp
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
Del 22 - Springer Tracts on Transportation and Traffic
Applications of Computational Learning and IoT in Smart Road Transportation System
Häftad, Engelska, 2026
2 158 kr
Skickas inom 10-15 vardagar
This book discusses machine learning and AI in real-time image processing for road transportation and traffic management. There is a growing need for affordable solutions that make use of cutting-edge technology like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The efficiency, sustainability, and safety of transport networks can be greatly increased by implementing an Internet of Things (IoT) and machine learning (ML)-based smart road transport system. Install sensors on roadways and intersections to gather data on traffic conditions in real time, such as vehicle density, speed, and flow. Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. This can lessen traffic, enhance overall traffic management, and optimize traffic signal timings. Vehicles equipped with Internet of Things devices can have their health monitored in real time. Parameters including lane changes, brake condition, tire pressure, and engine performance can all be monitored by sensors. Based on the gathered data, ML models are used to forecast probable maintenance problems. By scheduling preventive maintenance, failures can be avoided and overall road safety can be increased. Create a smartphone app that would enable drivers to locate parking spots in their area. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms. Integrate Internet of Things (IoT) sensors into fleet vehicles to monitor their performance, location, and fuel consumption. To maximize fleet efficiency, reduce fuel consumption, and plan routes more effectively, apply machine learning algorithms. Train ML models to forecast the quickest and most efficient routes with the help of historical data analysis. Route recommendations for drivers or fleet management systems can be constantly adjusted with real-time updates, which contain real-time data on road conditions, accidents, and construction. To guarantee smooth integration and efficient implementation, government organizations, transportation providers, and technology firms must work together.
Inbunden, Engelska, 2026
1 836 kr
Skickas inom 10-15 vardagar
This book explores the foundational principles and emerging advancements shaping modern computing, bridging theory and practice across technologies, transforming today’s world. It begins with an introduction to modern computer science before covering key topics such as generative AI, creative content generation, quantum computing, data analytics, and machine learning. Ethical aspects of AI are highlighted to encourage responsible innovation. Chapters on big data and data science show how large datasets drive insights. The book also examines blockchain, edge computing, and IoT, with applications in finance, health care, and logistics. Additional sections include robotics, cybersecurity, human–computer interaction, and cloud computing, emphasizing their roles in building secure, connected systems. Blending core and emerging concepts of computer science with case studies, the book equips students, researchers, professionals, and enthusiasts to navigate an evolving technological landscape.
E-bok
Engelska, 20262 366 kr
Läs direkt efter köp
This book explores the foundational principles and emerging advancements shaping modern computing, bridging theory and practice across technologies, transforming today’s world. It begins with an introduction to modern computer science before covering key topics such as generative AI, creative content generation, quantum computing, data analytics, and machine learning. Ethical aspects of AI are highlighted to encourage responsible innovation. Chapters on big data and data science show how large datasets drive insights. The book also examines blockchain, edge computing, and IoT, with applications in finance, health care, and logistics. Additional sections include robotics, cybersecurity, human–computer interaction, and cloud computing, emphasizing their roles in building secure, connected systems. Blending core and emerging concepts of computer science with case studies, the book equips students, researchers, professionals, and enthusiasts to navigate an evolving technological landscape.