Applications Computational Learning Iot (27 results)
Language: English
Published by CRC Press 2024
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Softcover
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
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Language: English
Published by CRC Press 2021
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Hardcover
Seller: Books From California, Simi Valley, CA, U.S.A.Books From California
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Hardcover. Condition: Fine.
Language: English
Published by CRC Press 2024
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Softcover
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Condition: New. pages cm First edition Includes bibliographical references and index.
Language: English
Published by CRC Press 2024
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Softcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Language: English
Published by CRC Press 2024
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
Language: English
Published by CRC Press 2021-11-25 2021
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Hardcover
Seller: Chiron Media, Wallingford, , United KingdomChiron Media
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Hardcover. Condition: New.
Language: English
Published by H N H International Limited 2021
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Hardcover
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
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Language: English
Published by H N H International Limited 2021
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. 1st edition NO-PA16APR2015-KAP.
- Hardcover
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Language: English
Published by CRC Press 2021
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Language: English
Published by Springer International Publishing AG, Cham 2025
- Hardcover
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Hardcover. Condition: 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 Th…ings (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.
Language: English
Published by Springer International Publishing AG, Cham 2025
- Hardcover
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Hardcover. Condition: 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 Th…ings (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 UK warehouse or from our Australian or US warehouses, depending on stock availability.
Language: English
Published by Springer Nature Switzerland, Springer International Publishing Jun 2025 2025
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: 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.
- Hardcover
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- Hardcover
Seller: Revaluation Books, Exeter, , United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 244 pages. 9.25x6.10x9.21 inches. In Stock.
Language: English
Published by Springer International Publishing AG, Cham 2025
- Hardcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Hardcover. Condition: 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 Th…ings (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.
Language: English
Published by H N H International Limited 2021
Series: Computational Intelligence in Engineering Problem Solving, Book 3 of 8. Book 3 of 8 - Computational Intelligence in Engineering Problem Solving
- Hardcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Seller: Revaluation Books, Exeter, , United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 244 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Language: English
Published by Springer Nature Switzerland, Springer International Publishing Mai 2025 2025
- Hardcover
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: 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 intelligenc…e (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.
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Buch. Condition: Neu. Applications of Computational Learning and IoT in Smart Road Transportation System | Saurav Mallik (u. a.) | Buch | Springer Tracts on Transportation and Traffic | viii | Englisch | 2025 | Springer | EAN 9783031876264 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidel…berg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 intelligence (A…I), 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 244 pp. Englisch.
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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