Results 111 to 120 of about 7,124,551 (244)

GADMM : Fast and Communication Efficient Framework for Distributed Machine Learning [PDF]

, 2020
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper. In particular, we propose a fast,
Bedi, Amrit S., Aggarwal, Vaneet, Bennis, Mehdi, Elgabli, Anis, Park, Jihong   +4 more
core  

Monitoring and assessment for obstructive sleep apnea

FlexMat, EarlyView.
This review systematically classifies Obstructive Sleep Apnea (OSA) monitoring indicators into three categories: physical, biochemical, and electrophysiological indicators, and lists several methods for each category. Abstract Obstructive sleep apnea (OSA) is a common chronic sleep‐disordered breathing disease characterized by recurrent upper airway ...
Yaowen Xu, Feifei Lin, Shujiao Chen, Zhen You, Shujing Zhu, Weiwei Zhao, Qiang Zhao   +6 more
wiley   +1 more source

FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV Environments

Sensors
The 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, Abir El Azzaoui, Jong Hyuk Park   +2 more
doaj   +1 more source

Artificial structures for human‐machine interfaces with closed‐loop interaction

FlexMat, EarlyView.
Artificial structures bridge sensing and feedback in closed‐loop human‐machine interfaces by providing geometry‐enabled mechanical and functional programmability. This review maps the role of artificial structures in diverse sensing modalities and feedback strategies, and illustrates their integration into closed‐loop interaction systems, highlighting ...
Taiqi Hu, Taisong Pan, Siqi Chen, Yiting Zhang, Yuan Lin   +4 more
wiley   +1 more source

TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction

Proceedings
Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. It is possible to improve tinyML performance by using a meta-learning approach.
I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner   +2 more
doaj   +1 more source

Engineered molecular diagnostic strategies for hepatocellular carcinoma: Integrating flexible photonic barcodes, liquid biopsies, and AI‐enhanced sensing platforms

FlexMat, EarlyView.
Abstract Early diagnosis of hepatocellular carcinoma (HCC) remains challenging because conventional serum biomarkers, especially alpha‐fetoprotein (AFP), show limited sensitivity for early‐stage disease. Recent progress in engineered molecular diagnostics provides new opportunities to improve HCC detection through integrated material, molecular, and ...
Jingzhi Xue, Wanqi Yang, Xiaoyu Liu, Xuehui Chu, Wei Li, Jinglin Wang   +5 more
wiley   +1 more source

Heterophily-informed Message Passing

Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Graph neural networks (GNNs) are known to be vulnerable to oversmoothing due to their implicit homophily assumption.
Solin, Arno, Garg, Vikas, Wang, Haishan
core  

Research progress on the depth of anesthesia monitoring based on the electroencephalogram

Ibrain, Volume 11, Issue 1, Page 32-43, Spring 2025.
Electroencephalogram (EEG) can noninvasive, continuous, and real‐time monitor the state of brain electrical activity, and the monitoring of EEG can reflect changes in the depth of anesthesia (DOA). The development of artificial intelligence can enable anesthesiologists to extract, analyze, and quantify DOA from complex EEG data.
Xiaolan He, Tingting Li, Xiao Wang
wiley   +1 more source

I-MCM: IoT Malware Counter Measures for Cross-Architecture IoT Malware Detection

IEEE Access
The recent attacks initiated by malware-infected IoT devices illustrate that these attacks have tremendous impacts not only on the targeted systems but also on the entire internet infrastructure.
Ibrahim Gulatas, H. Hakan Kilinc, A. Halim Zaim, M. Ali Aydin   +3 more
doaj   +1 more source

Tiny Machine Learning: Progress and Futures [PDF]

Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence.
Han, Song, Zhu, Ligeng, Wang, Wei-Chen, Chen, Wei-Ming, Lin, Ji   +4 more
core   +1 more source