Results 201 to 210 of about 15,702 (241)

The Fifth Edition of the Automated Assessment of Pain (AAP 2025). [PDF]

Proc ACM Int Conf Multimodal Interact
Hammal Z, Walter S, Bianchi-Berthouze N.
europepmc   +1 more source

Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights. [PDF]

Front Microbiol
Okafor UC, Alghamdi SM, Anguilano L, Yang Y.   +3 more
europepmc   +1 more source

Research on Alzheimer's disease MRI image classification based on spatial attention mechanism. [PDF]

Front Aging Neurosci
Zhao S, Shi H, Guan C, Zeng Q, Zhang C, Wan Y.   +5 more
europepmc   +1 more source

Modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms. [PDF]

Elife
Bredenberg C, Normandin F, Richards B, Lajoie G.   +3 more
europepmc   +1 more source

Deep learning-based detection of retinal detachment with vitreous hemorrhage in ocular ultrasound images. [PDF]

Sci Rep
Toyama N, Hidaka T, Tamura H, Ikeda Y.
europepmc   +1 more source

Tiny Machine Learning for Hyperspectral Skin Cancer Image Classification

Knowledge-Based Systems
openaire   +1 more source

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Tiny Machine Learning (Tiny-ML) for Efficient Channel Estimation and Signal Detection

IEEE Transactions on Vehicular Technology, 2022
Bin Li, Hongfu Liu, Wei Ziping
exaly   +2 more sources

Incremental On-Device Tiny Machine Learning

Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, 2020
Tiny Machine Learning (TML) is a novel research area aiming at designing and developing Machine Learning (ML) techniques meant to be executed on Embedded Systems and Internet-of-Things (IoT) units. Such techniques, which take into account the constraints on computation, memory, and energy characterizing the hardware platform they operate on, exploit ...
Disabato S., Roveri M.
openaire   +2 more sources

Machine Learning on Commodity Tiny Devices

2022
This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration.
Guo, Song, Zhou, Qihua
openaire   +2 more sources