Results 41 to 50 of about 374 (124)
TinyML in Industrial IoT: A Systematic Review of Applications, System Components, and Methodologies
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
doaj +1 more source
Advanced Intelligent Systems, Volume 8, Issue 3, March 2026.This paper presents an integrated AI‐driven cardiovascular platform unifying multimodal data, predictive analytics, and real‐time monitoring. It demonstrates how artificial intelligence—from deep learning to federated learning—enables early diagnosis, precision treatment, and personalized rehabilitation across the full disease lifecycle, promoting a ...
Mowei Kong +4 more
wiley +1 more source
Empowering voice assistants with TinyML for user-centric innovations and real-world applications
Scientific ReportsThis study explores the motivations behind integrating TinyML-based voice assistants into daily life, focusing on enhancing their user interface (UI) and functionality to improve user experience. This research discusses real-world applications like smart
Sireesha Chittepu +2 more
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TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction
ProceedingsTiny 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 +2 more
doaj +1 more source
Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.Three key parts of our review. This review examines recent research on integrating machine learning with edge computing in healthcare. It is structured around three key parts: the demographic characteristics of the selected studies; the themes, tools, motivations, and data sources; and the key limitations, challenges, and future research directions ...
Amir Mashmool +7 more
wiley +1 more source
Nanozymes Integrated Biochips Toward Smart Detection System
Advanced Science, Volume 13, Issue 11, 23 February 2026.This review systematically outlines the integration of nanozymes, biochips, and artificial intelligence (AI) for intelligent biosensing. It details how their convergence enhances signal amplification, enables portable detection, and improves data interpretation.
Dongyu Chen +10 more
wiley +1 more source
Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG
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
doaj +1 more source
IEEE AccessCuffless continuous noninvasive blood pressure (cNIBP) monitoring based on photoplethysmography (PPG) has enjoyed great success through a wealth of high-performing machine learning (ML) algorithms.
Nour Faris Ali +3 more
doaj +1 more source
Use of Automation Technologies and Data Mining in Speech Recognition for Autism
Brain and Behavior, Volume 16, Issue 2, February 2026.Pipeline analyzes clinical and naturalistic speech using LENA, wav2vec 2.0, and foundation‐model ASR (Whisper) to enable scalable ASD detection and severity estimation. Future work integrates benchmarking, privacy‐preserving collaboration (federated learning), and explainable, edge‐ready AI for clinically credible assessment and longitudinal monitoring.
Rongjie Mao, Yuncheng Zhu
wiley +1 more source
SciThe integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation.
Margarita Terziyska +3 more
doaj +1 more source