Results 61 to 70 of about 7,108,814 (163)

Physics-Enhanced TinyML for Real- Time Detection of Ground Magnetic Anomalies

IEEE Access
Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure.
Talha Siddique, Md. Shaad Mahmud
doaj   +1 more source

A Comprehensive and Comparative Analysis of Tiny Machine Learning (TinyML): Current Trends, Technological Integrations, and Future Opportunities

 Introduction and BackgroundIn the past few years the rapid boom of AI led to the implementation of ML on several researchand business applications. However, the implementation and operation of large Machine Learningmodels on high computational power machines not only cause considerable costs but alsocontribute to an environmental impact.
Ramandeep Kaur, Jasveer Kaur
openaire   +2 more sources

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

International Journal of Computational Intelligence Systems
The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring.
R. Arthi, S. Krishnaveni
doaj   +1 more source

A Review of TinyML for Human Activity Recognition on Edge Devices

Machine Learning and Knowledge Extraction
The integration of Tiny Machine Learning (TinyML) into human activity recognition (HAR) represents a paradigm shift in artificial intelligence, enabling real-time, efficient, and privacy-preserving analysis on resource-constrained edge devices.
Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi   +4 more
doaj   +1 more source

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Sensors
Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic ...
Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Raquel Ochoa-Ornelas, Jorge Ivan Cuevas-Chávez, Daniel Alejandro Sánchez-Arias   +4 more
doaj   +1 more source

A Review on Resource-Constrained Embedded Vision Systems-Based Tiny Machine Learning for Robotic Applications

Algorithms
The evolution of low-cost embedded systems is growing exponentially; likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art
Miguel Beltrán-Escobar, Teresa E. Alarcón, Jesse Y. Rumbo-Morales, Sonia López, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vázquez   +5 more
doaj   +1 more source

TinyMetaFed: Efficient Federated Meta-Learning for TinyML

, 2023
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating their
Li, Xue, Runkler, Thomas A., Anicic, Darko, Ren, Haoyu   +3 more
core   +1 more source

TinyML-Based Swine Vocalization Pattern Recognition for Enhancing Animal Welfare in Embedded Systems

Inventions
The automatic recognition of animal vocalizations is a valuable tool for monitoring pigs’ behavior, health, and welfare. This study investigates the feasibility of implementing a convolutional neural network (CNN) model for classifying pig vocalizations ...
Tung Chiun Wen, Caroline Ferreira Freire, Luana Maria Benicio, Giselle Borges de Moura, Magno do Nascimento Amorim, Késia Oliveira da Silva-Miranda   +5 more
doaj   +1 more source

Machine Learning on Commodity Tiny Devices: Theory and Practice

, 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 ...
Zhou, Qihua, Guo, Song
core   +1 more source

Embedded Intelligence for Smart Home Using TinyML Approach to Keyword Spotting

Engineering Proceedings
Current research in home automation focuses on integrating emerging technologies like Internet of Things (IoT) and machine learning to create smart home solutions that offer enhanced convenience, efficiency, and security.
Jyoti Mishra, Timothy Malche, Amit Hirawat   +2 more
doaj   +1 more source