Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML) [PDF]
IEEE Access, 2022 Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and ...
Syed Ali Raza Zaidi +3 more
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A Machine Learning-Oriented Survey on Tiny Machine Learning [PDF]
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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TinyML in Industrial IoT: A Systematic Review of Applications, System Components, and Methodologies [PDF]
SensorsTiny Machine Learning (TinyML) enables Machine Learning (ML) models to run on resource-constrained devices, which is critical for Industrial Internet of Things (IIoT) systems requiring low latency, energy efficiency, and local decision-making ...
Shahad Alharthi +2 more
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Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG [PDF]
Biomedical Engineering and Computational BiologyTiny Artificial Intelligence (Tiny AI) is transforming resource-constrained embedded systems, particularly in e-health applications, by introducing a shift in Tiny Machine Learning (TinyML) and its integration with the Internet of Things (IoT).
Moez Hizem +4 more
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Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions [PDF]
SensorsThe growth in artificial intelligence and its applications has led to increased data processing and inference requirements. Traditional cloud-based inference solutions are often used but may prove inadequate for applications requiring near-instantaneous ...
Soroush Heydari, Qusay H. Mahmoud
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Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications [PDF]
SensorsThe advent of Tiny Machine Learning (TinyML) has unlocked the potential to deploy machine learning models on resource-constrained edge devices, revolutionizing real-time monitoring in Internet of Medical Things (IoMT) applications.
Moez Hizem +4 more
doaj +2 more sources
FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV Environments [PDF]
SensorsThe widespread deployment of CCTV systems has significantly enhanced surveillance and public safety across various environments. However, the emergence of deepfake technology poses serious challenges by enabling malicious manipulation of video footage ...
Jimin Ha +2 more
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TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing [PDF]
Scientific ReportsAdvanced techniques for detecting and classifying road anomalies are crucial due to road networks’ rapid expansion and increasing complexity. This study introduces a novel integration of Tiny Machine Learning (TinyML), remote sensing, and fuzzy logic ...
Amna Khatoon +4 more
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IET Smart CitiesEmerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices.
Ali M. Hayajneh +5 more
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An optimized stacking-based TinyML model for attack detection in IoT networks. [PDF]
PLoS ONEWith the expansion of Internet of Things (IoT) devices, security is an important issue as attacks are constantly gaining more complex. Traditional attack detection methods in IoT systems have difficulty being able to process real-time and access ...
Anshika Sharma +2 more
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