The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
IoT Device Onboarding, Monitoring, and Management: Approaches, Challenges, and Future
Abstract
Deep learning models can achieve more accuracy sometimes that exceed human-level performance. It is crucial for safety-critical applications such as driverless cars, aerospace, defence, medical research, and industrial automation. Most of the deep learning methods mimic the neural network. It has many hidden layers and creates patterns for decision making and it is a subset of machine learning that performs end-to-end learning and has the capability to learn unsupervised data and also provides very flexible, learnable framework for representing the visual and linguistic information. Deep learning has greatly changed the way and computing devices processes human-centric content such as speech, image recognition, and natural language processing. Deep learning plays a major role in IoT-related services. The amalgamation of deep learning to the IoT environment makes the complex sensing and recognition tasks easier. It helps to automatically identify patterns and detect anomalies that are generated by IoT devices. This chapter discusses the impact of deep learning in the IoT environment.
Related Content
Dina Darwish.
© 2024.
43 pages.
|
Kassim Kalinaki, Musau Abdullatif, Sempala Abdul-Karim Nasser, Ronald Nsubuga, Julius Kugonza.
© 2024.
23 pages.
|
Yogita Yashveer Raghav, Ramesh Kait.
© 2024.
17 pages.
|
Renuka Devi Saravanan, Shyamala Loganathan, Saraswathi Shunmuganathan.
© 2024.
21 pages.
|
Veera Talukdar, Ardhariksa Zukhruf Kurniullah, Palak Keshwani, Huma Khan, Sabyasachi Pramanik, Ankur Gupta, Digvijay Pandey.
© 2024.
30 pages.
|
Dharmesh Dhabliya, Sukhvinder Singh Dari, Nitin N. Sakhare, Anish Kumar Dhablia, Digvijay Pandey, Balakumar Muniandi, A. Shaji George, A. Shahul Hameed, Pankaj Dadheech.
© 2024.
9 pages.
|
Avtar Singh, Shobhana Kashyap.
© 2024.
11 pages.
|
|
|