Results 51 to 60 of about 793 (174)

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

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

Multimodal AI for Real‐Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation

Food Science &Nutrition, Volume 14, Issue 2, February 2026.
The graphical abstract depicts an integrated multimodal AI pipeline for real‐time food safety and quality across the farm‐to‐fork continuum, where heterogeneous sensing modalities including vision, spectroscopy, electronic nose volatiles, biosensing, and IoT/RFID generate complementary data streams that undergo dataset engineering through ...
Zhaojie Chen, Guangyu Zhang, Fan Zhang
wiley   +1 more source

A Review of Photoplethysmography-Based Blood Pressure Monitoring: From Cloud-Based Machine Learning to TinyML Edge Deployment

IEEE Access
Cuffless 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, Hasan Al-Nashash, Ibrahim M. Elfadel, Mohamed Atef   +3 more
doaj   +1 more source

Towards scalable insect monitoring: Ultra‐lightweight CNNs as on‐device triggers for insect camera traps

Methods in Ecology and Evolution, Volume 17, Issue 2, Page 357-370, February 2026.
Abstract Camera traps, combined with AI, have emerged to achieve automated, scalable biodiversity monitoring. However, passive infrared (PIR) sensors that typically trigger camera traps are poorly suited for detecting small, fast‐moving ectotherms such as insects. Insects comprise over half of all animal species and are key components of ecosystems and
Ross J. Gardiner, Sareh Rowlands, Benno I. Simmons   +2 more
wiley   +1 more source

Microfabricated Neural Biosensors for Detection of Neurotransmitters, Biomarkers, and Small Molecules: Emerging Trends on Self‐Sustained Systems and Energy Harvesting

Advanced Sensor Research, Volume 5, Issue 1, January 2026.
This paper reviews the state of the art and recent developments in thin‐film biosensors for the detection of neurotransmitters, small molecules, and biomarkers within flexible, implantable bioelectronic systems. It covers the main sensing modalities, including electrochemical, plasmonic, acoustic, and magnetic, alongside their materials, transduction ...
Massimo Mariello
wiley   +1 more source

A Review of the Transition from Industry 4.0 to Industry 5.0: Unlocking the Potential of TinyML in Industrial IoT Systems

Sci
The 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, Iliana Ilieva, Zhelyazko Terziyski, Nikolay Komitov   +3 more
doaj   +1 more source

A Joint Survey in Decentralized Federated Learning and TinyML: A Brief Introduction to Swarm Learning

Future Internet
TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-power devices to process their own data. The lack of resources restricts the aforementioned devices to running only inference tasks (static TinyML), while training ...
Evangelia Fragkou, Dimitrios Katsaros
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

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