Results 121 to 130 of about 793 (174)

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

Sensors (Basel)
Ha J, El Azzaoui A, Park JH.
europepmc   +1 more source

Low-Power Embedded Sensor Node for Real-Time Environmental Monitoring with On-Board Machine-Learning Inference. [PDF]

Sensors (Basel)
Reis MJCS.
europepmc   +1 more source

A hand sign recognition based signal system for mute people using machine learning. [PDF]

MethodsX
Dagde R, Thakre S, Chopade S, Rokde L, Kakani V.   +4 more
europepmc   +1 more source

From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting. [PDF]

Sensors (Basel)
Islam MB, Guerrieri A, Gravina R, Delaney DT, Fortino G.   +4 more
europepmc   +1 more source

TinyML-Based In-Pipe Feature Detection for Miniature Robots. [PDF]

Sensors (Basel)
Yang M, Blight A, Bhardwaj H, Shaukat N, Han L, Richardson R, Pickering A, Jackson-Mills G, Barber A.   +8 more
europepmc   +1 more source

A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications. [PDF]

Sci Rep
Holsamudrkar N, Sikdar S, Kalgutkar AP, Banerjee S, Mishra R.   +4 more
europepmc   +1 more source

DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning

Sensors, 2023
Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices, which pose a challenge to fulfilling DL models that demand large storage and computation.
Norah Alajlan, Dina M Ibrahim, Ibrahim Dina M   +2 more
exaly   +4 more sources

Some of the next articles are maybe not open access.

Related searches:

Is TinyML Sustainable?

Communications of the ACM, 2023
Assessing the environmental impacts of machine learning on microcontrollers.
Shvetank Prakash, Matthew Stewart, Colby R. Banbury, Mark Mazumder, Pete Warden, Brian Plancher, Vijay Janapa Reddi   +6 more
openaire   +1 more source

TinyML Meets IoT: A Comprehensive Survey

Internet of Things (Netherlands), 2021
Abstract The rapid growth in miniaturization of low-power embedded devices and advancement in the optimization of machine learning (ML) algorithms have opened up a new prospect of the Internet of Things (IoT), tiny machine learning (TinyML), which calls for implementing the ML algorithm within the IoT device .
Lachit Dutta
exaly   +2 more sources

A Review on the emerging technology of TinyML

ACM Computing Surveys
Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of ...
Athanasios Kakarountas, Vasileios Tsoukas, Eleni Boumpa   +2 more
exaly   +2 more sources