Applications computational learning iot (37 risultati)
Lingua: Inglese
Editore: CRC Press, 2024
Serie: Libro 3 di 8 - Computational Intelligence in Engineering Problem Solving
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Condizione: New. pages cm First edition Includes bibliographical references and index.
Lingua: Inglese
Editore: CRC Press, 2021
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Hardcover. Condizione: Fine.
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Editore: CRC Press, 2024
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Condizione: New.
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Editore: CRC Press, 2024
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Editore: CRC Press 2021-11-25, 2021
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Lingua: Inglese
Editore: H N H International Limited, 2021
Serie: Libro 3 di 8 - Computational Intelligence in Engineering Problem Solving
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Lingua: Inglese
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Serie: Libro 3 di 8 - Computational Intelligence in Engineering Problem Solving
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220)
Amodu, Oluwatosin Ahmed; Mahmood, Raja Azlina Raja; Althumali, Huda; Bukar, Umar Ali; Abdullah, Nor Fadzilah; Jarray, Chedia
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Lingua: Inglese
Editore: CRC Press, 2021
Serie: Libro 3 di 8 - Computational Intelligence in Engineering Problem Solving
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Da: California Books, Miami, FL, U.S.A.California Books
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Applications of Computational Learning and Iot in Smart Road Transportation System
Mallik, Saurav (EDT); Qin, Hong (EDT); Nandi, Subrata (EDT); Alam, Munshi Yusuf (EDT); Roy, Arup (EDT)
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Hardcover. Condizione: new. Hardcover. 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 T…hings (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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: CRC Press, 2021
Serie: Libro 3 di 8 - Computational Intelligence in Engineering Problem Solving
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: California Books, Miami, FL, U.S.A.California Books
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Condizione: New.
Applications of Computational Learning and Iot in Smart Road Transportation System
Mallik, Saurav (EDT); Qin, Hong (EDT); Nandi, Subrata (EDT); Alam, Munshi Yusuf (EDT); Roy, Arup (EDT)
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Applications of Computational Learning and Iot in Smart Road Transportation System
Mallik, Saurav (EDT); Qin, Hong (EDT); Nandi, Subrata (EDT); Alam, Munshi Yusuf (EDT); Roy, Arup (EDT)
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Condizione: As New. Unread book in perfect condition.
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Applications of Computational Learning and IoT in Smart Road Transportation System | Saurav Mallik (u. a.) | Taschenbuch | Springer Tracts on Transportation and Traffic | viii | Englisch | 2026 | Springer | EAN 9783031876295 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 1…7, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Applications of Computational Learning and Iot in Smart Road Transportation System
Mallik, Saurav (EDT); Qin, Hong (EDT); Nandi, Subrata (EDT); Alam, Munshi Yusuf (EDT); Roy, Arup (EDT)
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Hardcover. Condizione: Brand New. 244 pages. 9.25x6.10x9.21 inches. In Stock.
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Hardcover. Condizione: new. Hardcover. 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 T…hings (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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: CRC Press, 2024
Serie: Libro 3 di 8 - Computational Intelligence in Engineering Problem Solving
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Lingua: Inglese
Editore: H N H International Limited, 2021
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220)
Amodu, Oluwatosin Ahmed; Mahmood, Raja Azlina Raja; Althumali, Huda; Bukar, Umar Ali; Abdullah, Nor Fadzilah; Jarray, Chedia
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 244 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.
Markov Decision Processes and Reinforcement Learning for Timely UAV-IoT Data Collection Applications (Studies in Computational Intelligence, 1220)
Amodu, Oluwatosin Ahmed; Mahmood, Raja Azlina Raja; Althumali, Huda; Bukar, Umar Ali; Abdullah, Nor Fadzilah; Jarray, Chedia
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Lingua: Inglese
Editore: Springer Nature Switzerland, Springer International Publishing Mai 2025, 2025
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 intelligen…ce (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. 244 pp. Englisch.
