Results 1 to 10 of about 374 (124)
A review on TinyML: State-of-the-art and prospects
Journal of King Saud University - Computer and Information Sciences, 2022 Machine learning has become an indispensable part of the existing technological domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to imply machine learning techniques at the resource constrained embedded devices at ...
Partha Pratim Ray
exaly +4 more sources
DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning
Sensors, 2023 Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices,
Norah N Alajlan, Dina M Ibrahim
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A Comprehensive Survey on TinyML
IEEE Access, 2023 Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and ...
Youssef Abadade +5 more
doaj +2 more sources
TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
Future Internet, 2022 The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within ...
Nikolaos Schizas +2 more
exaly +3 more sources
Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
Applied Sciences (Switzerland), 2021 On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing.
Jisu Kwon, Daejin Park, Park Daejin
exaly +3 more sources
Micromachines, 2022 Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed in various fields. Many of these devices are based on machine learning (ML) models, which render them intelligent and able to make decisions.
Norah N. Alajlan, Dina M. Ibrahim
doaj +1 more source
Tạp chí Khoa họcThe convergence of artificial intelligence (AI) and wearables is ushering in a new paradigm of software development - AI- powered app creation - where AI is not just a feature but a software creation tool.
Nguyen Thi Dung*, Nguyen Thu Phuong, Doan Ngoc Phuong
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On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
IEEE AccessAs technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats.
Fatemeh Dehrouyeh +3 more
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A Machine Learning-Oriented Survey on Tiny Machine Learning
IEEE AccessThe emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Luigi Capogrosso +4 more
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A Holistic Review of the TinyML Stack for Predictive Maintenance
IEEE AccessDowntime caused by failing equipment can be extremely costly for organizations. Predictive Maintenance (PdM), which uses data to predict when maintenance should be conducted, is an essential tool for increasing safety, maximizing uptime and minimizing ...
Emil Njor +3 more
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