udendhran (37 résultats)

Hardcover. Etat : Very Good. VERY GOOD hardcover, no marks in text, very clean exterior, appears unread. Book.

Etat : new.

Etat : New.

Hardcover. Etat : new. Hardcover. Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor dataAn exploration of the benefits of neural networks in real-time environmental sensor data analysisPractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognitionAn analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : As New. Unread book in perfect condition.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New. In.

Etat : New.

Hardback. Etat : New. Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor dataAn exploration of the benefits of neural networks in real-time environmental sensor data analysisPractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognitionAn analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days.

Etat : New.

Etat : New. 2021. 1st Edition. Hardcover. . . . . .

Etat : New.

Hardcover. Etat : new. Hardcover. Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor dataAn exploration of the benefits of neural networks in real-time environmental sensor data analysisPractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognitionAn analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Etat : New. A. Suresh, PhD is an Associate Professor in the Department of Computer Science and Engineering in SRM Institute of Science & Technology, Tamil Nadu, India. With nearly two decades of experience in teaching, his areas of specializations include Data Mining, .

Hardcover. Etat : Brand New. 250 pages. 9.60x6.69x0.70 inches. In Stock.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Etat : new. Hardcover. BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformaticians important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New.

Etat : New. 2021. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.

Etat : new.

Etat : New.

Hardback. Etat : New. Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor dataAn exploration of the benefits of neural networks in real-time environmental sensor data analysisPractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognitionAn analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Buch. Etat : Neu. Neuware - Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysisSensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data.The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance.The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of:\* A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data\* An exploration of the benefits of neural networks in real-time environmental sensor data analysis\* Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition\* An analysis of boosting with XGBoost for sensor data analysisPerfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

Hardcover. Etat : new. Hardcover. Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor dataAn exploration of the benefits of neural networks in real-time environmental sensor data analysisPractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognitionAn analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Etat : New.

Hardcover. Etat : new. Hardcover. BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformaticians important tools and examines how they are used to evaluate biological data and advance disease knowledge. The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information. Audience The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.