Internet of Things and Artificial Intelligence for Smart Environments
Herausgegeben:Yew, Hoe Tung; Mamat, Mazlina; Dargham, Jamal Ahmad; Seng Kheau, Chung; Moung, Ervin Gubin
Internet of Things and Artificial Intelligence for Smart Environments
Herausgegeben:Yew, Hoe Tung; Mamat, Mazlina; Dargham, Jamal Ahmad; Seng Kheau, Chung; Moung, Ervin Gubin
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This book highlights the application of smart technologies that utilize the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance efficiency and productivity across various industries. It provides a comprehensive review of the latest IoT and AI developments, including their applications, opportunities, and challenges in smart environments. This book is a valuable resource for researchers, engineers, academics, and students working on implementing smart technologies.
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This book highlights the application of smart technologies that utilize the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance efficiency and productivity across various industries. It provides a comprehensive review of the latest IoT and AI developments, including their applications, opportunities, and challenges in smart environments. This book is a valuable resource for researchers, engineers, academics, and students working on implementing smart technologies.
Produktdetails
- Produktdetails
- Internet of Things
- Verlag: Springer / Springer Nature Singapore / Springer, Berlin
- Artikelnr. des Verlages: 978-981-97-1431-5
- 2024
- Seitenzahl: 236
- Erscheinungstermin: 5. Juni 2024
- Englisch
- Abmessung: 241mm x 160mm x 18mm
- Gewicht: 510g
- ISBN-13: 9789819714315
- ISBN-10: 9819714311
- Artikelnr.: 69900158
- Internet of Things
- Verlag: Springer / Springer Nature Singapore / Springer, Berlin
- Artikelnr. des Verlages: 978-981-97-1431-5
- 2024
- Seitenzahl: 236
- Erscheinungstermin: 5. Juni 2024
- Englisch
- Abmessung: 241mm x 160mm x 18mm
- Gewicht: 510g
- ISBN-13: 9789819714315
- ISBN-10: 9819714311
- Artikelnr.: 69900158
Yew Hoe Tung obtained his Ph.D. degree from Universiti Teknologi Malaysia in Biomedical Engineering. Currently, he is a Senior Lecturer at the Faculty of Engineering, Universiti Malaysia Sabah. He is a Chartered Engineer (CEng) with the Institute of Engineering and Technology, U.K., and a Professional Engineer with the Malaysia Board of Engineers. He is also a Professional Technologist with the Malaysia Board of Technologists (MBOT). His research interests include the general area of wireless communications and, in particular, the area of vertical handover, wireless sensor networks, and the Internet of Things. Mazlina Mamat received the B.Eng. and M.Sc. degrees in electrical and electronic engineering from Universiti Sains Malaysia, in 2000 and 2003, respectively. She received her Ph.D. degree in microengineering and nanoelectronics from Universiti Kebangsaan Malaysia, in 2013. Currently, she is a senior lecturer at the Electronic Engineering (Computer), Universiti Malaysia Sabah. She is also a professional engineer registered with the Board of Engineers Malaysia (BEM). Her research interests include sensors, signal processing, machine learning, and artificial intelligence. Jamal Ahmad Dargham Jamal Dargham received his B.Sc. in Control Systems Engineering from University of Technology, Iraq, in 1984 and his M.Sc. in Control Systems Engineering University of Manchester, UK, in 1987 and his Ph.D. in Image Processing from Universiti Malaysia Sabah in 2008. From 1989 till 1996, he was a lecturer at the Advanced Management College, Sabah. Since 1996, he has been with the School of Engineering and Information Technology, Universiti Malaysia Sabah as a senior lecturer. He was the Program head of the Computer Engineering Program from 2006 till 2011. His main research interests are in Image Processing and Biometrics as well as Engineering Education. He has published more than 70 papers in refereedjournals, conferences, book chapters, and research reports. Chung Seng Kheau received the B.Sc. and M.Sc. degrees in computer science from Universiti Teknologi Malaysia, Johor, Malaysia, in 1997 and 2000, respectively, and the Ph.D. degree in computer science from Universiti Malaysia Sabah, Kota Kinabalu, Malaysia, in 2015. Currently, he is a senior lecturer at the Electronic Engineering (Computer), Universiti Malaysia Sabah. His main research interests are in e-commerce and Internet of Things. Ervin Gubin Moung is a senior lecturer in the Faculty of Computing and Informatics, Universiti Malaysia Sabah. He received his Bachelor of Computer Engineering, Master of (Computer) Engineering, and Ph.D. in Computer Engineering from Universiti Malaysia Sabah (UMS) in 2008, 2013, and 2018, respectively. His research interest generally falls under Computer Vision and Pattern Recognition, such as image processing, image segmentation, imageclassification, object detection, vision-based learning, and big data analytics. His domain of interest includes public health, smart health, agriculture, food security, biodiversity, and environmental sustainability.
Internet-Of-Things: Applications, Challenges, And Future Trends.- IoT-Based Patient Monitoring System.- Enhancing Lettuce Production Quality Through Iot-Enabled Hydroponic Farming: An Optimized Fertilizer System Approach.- Iot Based Vending Machine.- Artificial Intelligence (AI) and the Future of Mankind.- Artificial Intelligence Offline, Online and Reinforcement Learning Approaches in Time Series Air Pollutant Index Prediction.- Artificial Intelligence in Time Series Prediction, Classification, and Sequence-to-Sequence Problems.- Fuzzy-Based Handover Scheme for 5G Heterogeneous Networks.- Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning A Feasibility Study.- Deep Learning Architectures and Their Efficacy in Surveillance Face Recognition A Study with the ChokePoint Dataset.
Internet-Of-Things: Applications, Challenges, And Future Trends.- IoT-Based Patient Monitoring System.- Enhancing Lettuce Production Quality Through Iot-Enabled Hydroponic Farming: An Optimized Fertilizer System Approach.- Iot Based Vending Machine.- Artificial Intelligence (AI) and the Future of Mankind.- Artificial Intelligence Offline, Online and Reinforcement Learning Approaches in Time Series Air Pollutant Index Prediction.- Artificial Intelligence in Time Series Prediction, Classification, and Sequence-to-Sequence Problems.- Fuzzy-Based Handover Scheme for 5G Heterogeneous Networks.- Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning A Feasibility Study.- Deep Learning Architectures and Their Efficacy in Surveillance Face Recognition A Study with the ChokePoint Dataset.
Internet-Of-Things: Applications, Challenges, And Future Trends.- IoT-Based Patient Monitoring System.- Enhancing Lettuce Production Quality Through Iot-Enabled Hydroponic Farming: An Optimized Fertilizer System Approach.- Iot Based Vending Machine.- Artificial Intelligence (AI) and the Future of Mankind.- Artificial Intelligence Offline, Online and Reinforcement Learning Approaches in Time Series Air Pollutant Index Prediction.- Artificial Intelligence in Time Series Prediction, Classification, and Sequence-to-Sequence Problems.- Fuzzy-Based Handover Scheme for 5G Heterogeneous Networks.- Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning A Feasibility Study.- Deep Learning Architectures and Their Efficacy in Surveillance Face Recognition A Study with the ChokePoint Dataset.
Internet-Of-Things: Applications, Challenges, And Future Trends.- IoT-Based Patient Monitoring System.- Enhancing Lettuce Production Quality Through Iot-Enabled Hydroponic Farming: An Optimized Fertilizer System Approach.- Iot Based Vending Machine.- Artificial Intelligence (AI) and the Future of Mankind.- Artificial Intelligence Offline, Online and Reinforcement Learning Approaches in Time Series Air Pollutant Index Prediction.- Artificial Intelligence in Time Series Prediction, Classification, and Sequence-to-Sequence Problems.- Fuzzy-Based Handover Scheme for 5G Heterogeneous Networks.- Estimating Shelf Life of Packed Fresh Milk Using Odor and Machine Learning A Feasibility Study.- Deep Learning Architectures and Their Efficacy in Surveillance Face Recognition A Study with the ChokePoint Dataset.