Results 71 to 80 of about 2,210 (159)

High Accuracy of Epileptic Seizure Detection Using Tiny Machine Learning Technology for Implantable Closed-Loop Neurostimulation Systems

BioMedInformatics
Background: Epilepsy is one of the most common and devastating neurological disorders, manifesting with seizures and affecting approximately 1–2% of the world’s population.
Evangelia Tsakanika, Vasileios Tsoukas, Athanasios Kakarountas, Vasileios Kokkinos   +3 more
doaj   +1 more source

TinyML for Smart Libraries

This study is about Tiny Machine Learning (TinyML) as an affordable device, but it transforms the library systems across the developing nations. Nowadays artificial intelligence (AI) very much influences the library systems, and it has a very high cost ...
Payel Saha, Volina Podder, Pradipta Dutta   +2 more
core   +1 more source

Lightweight confidentiality framework for TinyML-enabled IoT edge communication

Frontiers in Computer Science
Lightweight security mechanisms are essential for Internet of Things (IoT) edge environments, where devices operate under strict constraints in computation, memory, and energy.
Kurunandan Jain, Prabhakar Krishnan, Sethu Subramanian N., Prabu P.   +3 more
doaj   +1 more source

EVALUATION OF TINYML LIBRARIES FOR COMPUTER VISION ON RIOT OS

openNegli ultimi anni l’Intelligenza Artificiale (IA) e il Machine Learning (ML) hanno suscitato un interesse sempre maggiore, sia in ambito accademico che industriale.
DI PAOLO, DAVIDE
core  

TinyML: Progress and applications of machine learning on embedded platforms

openL’apprendimento automatico sta guadagnando sempre più interesse anche nell’ambito dell’elettronica. I motivi principali risiedono nel fatto che le tecniche di Machine Learning richiedono significative quantità di energia e potenza di calcolo per ...
RASERA, NICOLÒ
core  

On-device Online Learning and Semantic Management of TinyML Systems [PDF]

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges.
Li, Xue, Runkler, Thomas A., Anicic, Darko, Ren, Haoyu   +3 more
core   +1 more source

Enhancing TinyML Security: Study of Adversarial Attack Transferability [PDF]

The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers real-time data analysis and swift
Shah, Parin, Kulkarni, Pavan, Govindarajulu, Yuvaraj, Parmar, Manojkumar   +3 more
core   +1 more source

Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications

Sensors
The 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, Leila Bousbia, Yassmine Ben Dhiab, Mohamed Ould-Elhassen Aoueileyine, Ridha Bouallegue   +4 more
doaj   +1 more source

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

Voice-activated home automation system for IoT edge devices using TinyML

Discover Internet of Things
Home automation systems are popular because they enhance the quality of life and the way users interact with the environment. Deploying complex machine learning models on Internet of Things (IoT) devices with limited resources is still difficult.
Timothy Malche, Sandeep Budhani, Pramod Kumar Soni, Govind Murari Upadhyay   +3 more
doaj   +1 more source