Items related to Deep Learning Frameworks for Driver Drowsiness Prediction
Synopsis
Deep Learning Frameworks for Driver Drowsiness Prediction explores the application of deep learning, artificial intelligence, and computer vision techniques for detecting and predicting driver drowsiness. The book examines how intelligent systems can analyze driver-related visual and behavioral information to identify signs of fatigue and support the development of safety-oriented driver monitoring systems.
The book introduces fundamental concepts of deep learning, neural networks, computer vision, image processing, and predictive modeling, with particular emphasis on their application to driver drowsiness analysis. It discusses approaches for data preparation, feature extraction, model development, classification, prediction, and performance evaluation.
Particular attention is given to deep learning frameworks capable of processing visual and behavioral indicators associated with driver fatigue, including facial characteristics, eye-related patterns, and other observable cues. The book also considers challenges related to real-world driving environments, dataset quality, model performance, robustness, and the deployment of intelligent driver monitoring technologies.
By connecting deep learning methodologies with driver drowsiness prediction, this book provides a useful reference for students, researchers, data scientists, automotive engineers, computer vision professionals, and practitioners working in artificial intelligence, intelligent transportation systems, and automotive safety. It is especially relevant to readers interested in developing AI-based technologies that can contribute to safer and more intelligent transportation systems.
"synopsis" may belong to another edition of this title.
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Deep Learning Frameworks for Driver Drowsiness Prediction (Paperback)
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Paperback. Condition: new. Paperback. Deep Learning Frameworks for Driver Drowsiness Prediction explores the application of deep learning, artificial intelligence, and computer vision techniques for detecting and predicting driver drowsiness. The book examines how intelligent systems can analyze driver-related visual and behavioral information to identify signs of fatigue and support the development of safety-oriented driver monitoring systems.The book introduces fundamental concepts of deep learning, neural networks, computer vision, image processing, and predictive modeling, with particular emphasis on their application to driver drowsiness analysis. It discusses approaches for data preparation, feature extraction, model development, classification, prediction, and performance evaluation.Particular attention is given to deep learning frameworks capable of processing visual and behavioral indicators associated with driver fatigue, including facial characteristics, eye-related patterns, and other observable cues. The book also considers challenges related to real-world driving environments, dataset quality, model performance, robustness, and the deployment of intelligent driver monitoring technologies.By connecting deep learning methodologies with driver drowsiness prediction, this book provides a useful reference for students, researchers, data scientists, automotive engineers, computer vision professionals, and practitioners working in artificial intelligence, intelligent transportation systems, and automotive safety. It is especially relevant to readers interested in developing AI-based technologies that can contribute to safer and more intelligent transportation systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798240886362
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Deep Learning Frameworks for Driver Drowsiness Prediction
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Deep Learning Frameworks for Driver Drowsiness Prediction
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Deep Learning Frameworks for Driver Drowsiness Prediction
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Deep Learning Frameworks for Driver Drowsiness Prediction explores the application of deep learning, artificial intelligence, and computer vision techniques for detecting and predicting driver drowsiness. The book examines how intelligent systems can analyze driver-related visual and behavioral information to identify signs of fatigue and support the development of safety-oriented driver monitoring systems.The book introduces fundamental concepts of deep learning, neural networks, computer vision, image processing, and predictive modeling, with particular emphasis on their application to driver drowsiness analysis. It discusses approaches for data preparation, feature extraction, model development, classification, prediction, and performance evaluation.Particular attention is given to deep learning frameworks capable of processing visual and behavioral indicators associated with driver fatigue, including facial characteristics, eye-related patterns, and other observable cues. The book also considers challenges related to real-world driving environments, dataset quality, model performance, robustness, and the deployment of intelligent driver monitoring technologies.By connecting deep learning methodologies with driver drowsiness prediction, this book provides a useful reference for students, researchers, data scientists, automotive engineers, computer vision professionals, and practitioners working in artificial intelligence, intelligent transportation systems, and automotive safety. It is especially relevant to readers interested in developing AI-based technologies that can contribute to safer and more intelligent transportation systems. 152 pp. Englisch. Seller Inventory # 9798240886362
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Deep Learning Frameworks for Driver Drowsiness Prediction (Paperback)
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Paperback. Condition: new. Paperback. Deep Learning Frameworks for Driver Drowsiness Prediction explores the application of deep learning, artificial intelligence, and computer vision techniques for detecting and predicting driver drowsiness. The book examines how intelligent systems can analyze driver-related visual and behavioral information to identify signs of fatigue and support the development of safety-oriented driver monitoring systems.The book introduces fundamental concepts of deep learning, neural networks, computer vision, image processing, and predictive modeling, with particular emphasis on their application to driver drowsiness analysis. It discusses approaches for data preparation, feature extraction, model development, classification, prediction, and performance evaluation.Particular attention is given to deep learning frameworks capable of processing visual and behavioral indicators associated with driver fatigue, including facial characteristics, eye-related patterns, and other observable cues. The book also considers challenges related to real-world driving environments, dataset quality, model performance, robustness, and the deployment of intelligent driver monitoring technologies.By connecting deep learning methodologies with driver drowsiness prediction, this book provides a useful reference for students, researchers, data scientists, automotive engineers, computer vision professionals, and practitioners working in artificial intelligence, intelligent transportation systems, and automotive safety. It is especially relevant to readers interested in developing AI-based technologies that can contribute to safer and more intelligent transportation systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798240886362
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Deep Learning Frameworks for Driver Drowsiness Prediction (Paperback)
Print on DemandSeller: AussieBookSeller, Truganina, VIC, Australia
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Paperback. Condition: new. Paperback. Deep Learning Frameworks for Driver Drowsiness Prediction explores the application of deep learning, artificial intelligence, and computer vision techniques for detecting and predicting driver drowsiness. The book examines how intelligent systems can analyze driver-related visual and behavioral information to identify signs of fatigue and support the development of safety-oriented driver monitoring systems.The book introduces fundamental concepts of deep learning, neural networks, computer vision, image processing, and predictive modeling, with particular emphasis on their application to driver drowsiness analysis. It discusses approaches for data preparation, feature extraction, model development, classification, prediction, and performance evaluation.Particular attention is given to deep learning frameworks capable of processing visual and behavioral indicators associated with driver fatigue, including facial characteristics, eye-related patterns, and other observable cues. The book also considers challenges related to real-world driving environments, dataset quality, model performance, robustness, and the deployment of intelligent driver monitoring technologies.By connecting deep learning methodologies with driver drowsiness prediction, this book provides a useful reference for students, researchers, data scientists, automotive engineers, computer vision professionals, and practitioners working in artificial intelligence, intelligent transportation systems, and automotive safety. It is especially relevant to readers interested in developing AI-based technologies that can contribute to safer and more intelligent transportation systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798240886362
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Deep Learning Frameworks for Driver Drowsiness Prediction
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Deep Learning Frameworks for Driver Drowsiness Prediction explores the application of deep learning, artificial intelligence, and computer vision techniques for detecting and predicting driver drowsiness. The book examines how intelligent systems can analyze driver-related visual and behavioral information to identify signs of fatigue and support the development of safety-oriented driver monitoring systems.The book introduces fundamental concepts of deep learning, neural networks, computer vision, image processing, and predictive modeling, with particular emphasis on their application to driver drowsiness analysis. It discusses approaches for data preparation, feature extraction, model development, classification, prediction, and performance evaluation.Particular attention is given to deep learning frameworks capable of processing visual and behavioral indicators associated with driver fatigue, including facial characteristics, eye-related patterns, and other observable cues. The book also considers challenges related to real-world driving environments, dataset quality, model performance, robustness, and the deployment of intelligent driver monitoring technologies.By connecting deep learning methodologies with driver drowsiness prediction, this book provides a useful reference for students, researchers, data scientists, automotive engineers, computer vision professionals, and practitioners working in artificial intelligence, intelligent transportation systems, and automotive safety. It is especially relevant to readers interested in developing AI-based technologies that can contribute to safer and more intelligent transportation systems. Seller Inventory # 9798240886362
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Deep Learning Frameworks for Driver Drowsiness Prediction
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Taschenbuch. Condition: Neu. Deep Learning Frameworks for Driver Drowsiness Prediction | Affan Kinu | Taschenbuch | Englisch | 2026 | Nala Kala | EAN 9798240886362 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 136390355