Results 21 to 30 of about 793 (174)

A SYSTEMATIC REVIEW OF THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN THE DEVELOPMENT OF SMART WEARABLE DEVICE APPLICATIONS

Tạp chí Khoa học
The convergence of artificial intelligence (AI) and wearables is ushering in a new paradigm of software development - AI- powered app creation - where AI is not just a feature but a software creation tool.
Nguyen Thi Dung*, Nguyen Thu Phuong, Doan Ngoc Phuong
doaj   +1 more source

TinyML for Speech Recognition

2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)
We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples.
Andrew Barovic, Armin Moin
openaire   +2 more sources

A Holistic Review of the TinyML Stack for Predictive Maintenance

IEEE Access
Downtime caused by failing equipment can be extremely costly for organizations. Predictive Maintenance (PdM), which uses data to predict when maintenance should be conducted, is an essential tool for increasing safety, maximizing uptime and minimizing ...
Emil Njor, Mohammad Amin Hasanpour, Jan Madsen, Xenofon Fafoutis   +3 more
doaj   +1 more source

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

IEEE Access
As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats.
Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami   +3 more
doaj   +1 more source

A Machine Learning-Oriented Survey on Tiny Machine Learning

IEEE Access
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Luigi Capogrosso, Federico Cunico, Dong Seon Cheng, Franco Fummi, Marco Cristani   +4 more
doaj   +1 more source

Co‐Design of Stretchable Fabric Sensors and Tiny Machine Learning for Human Interface Device‐Based Edge‐Intelligent Wearable Gloves

Advanced Intelligent Systems, EarlyView.
A flexible smart glove integrated with textile sensors enables real‐time hand gesture recognition for human–machine interaction. Using edge AI inference and Bluetooth communication, the system translates finger movements into human interface device commands to control endpoint devices efficiently.
Chi Cuong Vu, Tuan Nghia Nguyen, Minh‐Thanh Le   +2 more
wiley   +1 more source

A Systematic Review of State-of-the-Art TinyML Applications in Healthcare, Education, and Transportation

IEEE Access
Tiny Machine Learning (TinyML) has emerged as a transformative paradigm enabling machine learning inference directly on ultra-low-power microcontrollers and edge devices.
Chaymae Yahyati, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi, May Almousa, Ahmed A. Abd El-Latif   +6 more
doaj   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

Tiny Machine Learning (TinyML): Research trends and future application opportunities

Array
Tiny Machine Learning (TinyML) enables artificial intelligence on low-power edge devices, yet a quantitative understanding of TinyML research remains limited.
Hui Han, Silvana Trimi, Sang M. Lee
doaj   +1 more source

An autonomous network of acoustic detectors to map tiger risk by eavesdropping on prey alarm calls

Remote Sensing in Ecology and Conservation, EarlyView.
Tiger population recovery brings with it increased fatalities from human‐tiger conflict. We describe a network of autonomous intelligent passive acoustic sensors that monitor the forest for deer alarm calls as a proxy for tiger risk and provide a risk map to local communities in real‐time.
Arik Kershenbaum, Andrew Markham, Holly Root‐Gutteridge, Bethany Smith, Casey Anderson, Riley McClaughry, Ramjan Chaudhary, Amogh Vishwakarma, Stephen Cummins, Angela Dassow   +9 more
wiley   +1 more source