AI and data-driven methods, particularly machine and deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks such as atmospheric condition monitoring. These models enable highly accurate predictions from large, labelled datasets. However, one of the key challenges with current AI systems is their opacity. While they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence techniques are increasingly employed, yet their internal workings remain largely opaque. In this edited book, the contributors explore the potential of machine intelligence, deep learning and sensing technologies, with a focus on their integration and applications. They present new methods, cutting-edge technologies and practical solutions to real-world challenges. The goal is to help readers better grasp the concepts necessary for designing and deploying the next generation of smarter and more sustainable systems and environments. The Convergence of AI, IoT, Smart Sensors and Remote Sensing for Sustainable Smart Environment Applications is a useful reference for researchers, engineers and scientists in academia and industry, as well as advanced students in the fields of data science and security, AI and networking, communication engineering, smart sensing, remote sensing and automation. It will also be of interest to system designers and developers of AI and sensing technologies for smart environment applications.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Chaker Abdelaziz Kerrache is a full professor in the department of computer science at the University of Amar Telidji Laghouat, Algeria. His research areas cover the fields of security, trust and risk management, secure multi-hop wireless communications, unmanned aerial vehicles (UAVs), named data networking (NDN), and the mobile internet. He is an associate editor of Computer Communications and Frontiers in Space Technologies, and is also a TPC member for several international conferences, including Infocom, Globecom, ICC, and CCNC. He holds a PhD degree in computer science from the University of Laghouat, Algeria.
Mohamed Lahby is an associate professor and Director of the Laboratory of Applied Mathematics and Computer Sciences at the Ecole Normale Supérieure (ENS), Hassan II University of Casablanca, Morocco. His research interests include wireless communication and networks, mobility management, QoS/QoE, Internet of Things, smart cities, optimization, machine learning, and generative AI. He has published 60+ papers and edited 10+ books. He regularly serves on executive and technical program committees for international conferences. He is also a referee for research journals including Ad Hoc Networks, Applied Computing and Informatics, and International Journal of Disaster Risk Reduction. He has organized and participated in 20+ conferences and workshops. He is a senior member of IEEE. He received his PhD degree in computer science from the University Hassan II of Casablanca, Morocco.
Al-Sakib Khan Pathan is a professor in the CSE department at the United International University (UIU), Bangladesh. His research focuses on computer networks and security, wireless network routing, e-services technologies, Internet and web applications, and multidisciplinary issues. He serves as a chair and board member for top-ranked international IEEE and ACM conferences, He is editor-in-chief of the International Journal of Computers and Applications and the Journal of Cyber Security Technology. He has edited 27 books and authored 6 books. He is a senior member of the IEEE. He received his PhD degree in computer engineering from Kyung Hee University, South Korea.
Yassine Maleh is an associate professor of cybersecurity and IT governance at Sultan Moulay Slimane University, Morocco. He is the founding chair of the IEEE Consultant Network Morocco and founding president of the African Research Center of Information Technology and Cybersecurity. He is a former CISO at the National Port Agency. He has published 200+ papers in international journals and at conferences and workshops), edited 40 books, and authored 5 books. He is the editor-in-chief of the International Journal of Information Security and Privacy and series editor of Advances in Cybersecurity Management. He serves on the executive and technical program committees, and as general chair and publication chair and reviewer for international conferences and journals. He is a senior member of IEEE. He holds a PhD degree in information system management from the National School of Business and Management (ENCG-SETTAT) and a PhD degree in computer sciences and engineering from the Facult des Sciences et Techniques (FST) in Settat, Morocco.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. AI and data-driven methods, particularly machine and deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks such as atmospheric condition monitoring. These models enable highly accurate predictions from large, labelled datasets. However, one of the key challenges with current AI systems is their opacity. While they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence techniques are increasingly employed, yet their internal workings remain largely opaque.In this edited book, the contributors explore the potential of machine intelligence, deep learning and sensing technologies, with a focus on their integration and applications. They present new methods, cutting-edge technologies and practical solutions to real-world challenges. The goal is to help readers better grasp the concepts necessary for designing and deploying the next generation of smarter and more sustainable systems and environments.The Convergence of AI, IoT, Smart Sensors and Remote Sensing for Sustainable Smart Environment Applications is a useful reference for researchers, engineers and scientists in academia and industry, as well as advanced students in the fields of data science and security, AI and networking, communication engineering, smart sensing, remote sensing and automation. It will also be of interest to system designers and developers of AI and sensing technologies for smart environment applications. With a focus on integration and application, this book covers new methods, cutting-edge technologies and practical solutions to real world challenges at the intersection of machine learning, deep learning, and sensing technologies to design more sustainable and smarter systems and environments. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781837242436
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Hardback. Etat : New. AI and data-driven methods, particularly machine and deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks such as atmospheric condition monitoring. These models enable highly accurate predictions from large, labelled datasets. However, one of the key challenges with current AI systems is their opacity. While they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence techniques are increasingly employed, yet their internal workings remain largely opaque. In this edited book, the contributors explore the potential of machine intelligence, deep learning and sensing technologies, with a focus on their integration and applications. They present new methods, cutting-edge technologies and practical solutions to real-world challenges. The goal is to help readers better grasp the concepts necessary for designing and deploying the next generation of smarter and more sustainable systems and environments. The Convergence of AI, IoT, Smart Sensors and Remote Sensing for Sustainable Smart Environment Applications is a useful reference for researchers, engineers and scientists in academia and industry, as well as advanced students in the fields of data science and security, AI and networking, communication engineering, smart sensing, remote sensing and automation. It will also be of interest to system designers and developers of AI and sensing technologies for smart environment applications. N° de réf. du vendeur LU-9781837242436
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Hardcover. Etat : new. Hardcover. AI and data-driven methods, particularly machine and deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks such as atmospheric condition monitoring. These models enable highly accurate predictions from large, labelled datasets. However, one of the key challenges with current AI systems is their opacity. While they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence techniques are increasingly employed, yet their internal workings remain largely opaque.In this edited book, the contributors explore the potential of machine intelligence, deep learning and sensing technologies, with a focus on their integration and applications. They present new methods, cutting-edge technologies and practical solutions to real-world challenges. The goal is to help readers better grasp the concepts necessary for designing and deploying the next generation of smarter and more sustainable systems and environments.The Convergence of AI, IoT, Smart Sensors and Remote Sensing for Sustainable Smart Environment Applications is a useful reference for researchers, engineers and scientists in academia and industry, as well as advanced students in the fields of data science and security, AI and networking, communication engineering, smart sensing, remote sensing and automation. It will also be of interest to system designers and developers of AI and sensing technologies for smart environment applications. With a focus on integration and application, this book covers new methods, cutting-edge technologies and practical solutions to real world challenges at the intersection of machine learning, deep learning, and sensing technologies to design more sustainable and smarter systems and environments. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781837242436
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Hardback. Etat : New. AI and data-driven methods, particularly machine and deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks such as atmospheric condition monitoring. These models enable highly accurate predictions from large, labelled datasets. However, one of the key challenges with current AI systems is their opacity. While they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence techniques are increasingly employed, yet their internal workings remain largely opaque. In this edited book, the contributors explore the potential of machine intelligence, deep learning and sensing technologies, with a focus on their integration and applications. They present new methods, cutting-edge technologies and practical solutions to real-world challenges. The goal is to help readers better grasp the concepts necessary for designing and deploying the next generation of smarter and more sustainable systems and environments. The Convergence of AI, IoT, Smart Sensors and Remote Sensing for Sustainable Smart Environment Applications is a useful reference for researchers, engineers and scientists in academia and industry, as well as advanced students in the fields of data science and security, AI and networking, communication engineering, smart sensing, remote sensing and automation. It will also be of interest to system designers and developers of AI and sensing technologies for smart environment applications. N° de réf. du vendeur LU-9781837242436
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