Results 21 to 30 of about 2,210 (159)

TinyML: A Systematic Review and Synthesis of Existing Research

, 2022
269275Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links embedded systems (hardware and software) and machine learning, with the purpose of realizing ultra-low-power and low-cost and efficiency and privacy, brings ...
Han, Hui, Julien Siebert, Siebert, Julien, Hui Han   +3 more
core   +1 more source

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

IoT Makers: A Collaborative Learning Experience with TinyML

Shodh Sari
Traditional teaching methods often fail to fully engage students in the field of IoT, particularly when it comes to applying machine learning at the edge. This paper presents an innovative pedagogical approach titled “IoT Makers,” aimed at MSc Artificial
Dr. Helen K. Joy
doaj   +1 more source

Edge AI for Climate-Aware ET0 Forecasting and Autonomous Precision Irrigation: A Hardware-Software Co-Design for Autonomous Precision Irrigation [PDF]

E3S Web of Conferences
Precision agriculture is hindered by its dependence on centralized cloud infrastructures, which prevents deployment of advanced deep learning (DL) in disconnected, resource‑constrained rural environments.
Hodouto Horatio Harley Koffivi, Cherouat Abel, Dzagli Milohum Mikesokpo, Bassir David   +3 more
doaj   +1 more source

Mapping the Convergence of Artificial Intelligence, IoT, and Embedded Systems: A Comprehensive Bibliometric Analysis (2015-2025) [PDF]

E3S Web of Conferences
Background: The rapid convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Embedded Systems has birthed the era of "Edge Intelligence.
Nouayti Mohamed, Aynaou Anass, Nouayti Nordine, Adnani Younes   +3 more
doaj   +1 more source

Cross‐Sectoral AI Integration Is Essential to Tackling Food Waste and Food Insecurity: A Roadmap for Developing Resilient Food Systems

Advanced Intelligent Systems, EarlyView.
As food insecurity and global food demands surge, artificial intelligence (AI)‐based technologies offer promising opportunities to reduce food loss and waste. In this perspective, current AI adoption across the food supply chain is assessed using various academic, industry, and policy sources.
Akansha Prasad, Hannah Mann, Lubna Najm, Shadman Khan, Carlos D. M. Filipe, Tohid F. Didar   +5 more
wiley   +1 more source

A Federated Learning Framework for Predictive Maintenance in IoT-Enabled Smart Environments [PDF]

Computer and Knowledge Engineering
Predictive maintenance is a critical component in the management of smart environments, aiming to reduce unplanned downtimes and enhance operational efficiency.
alireza esmaili, Hakan Burak Emekli
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

Future of edge AI in biodiversity monitoring

Methods in Ecology and Evolution, EarlyView.
Abstract Many ecological decisions are slowed by the gap between collecting and analysing biodiversity information. Edge computing moves data processing closer to the sensor, with edge artificial intelligence (AI) using AI models for data processing and selective data transfer.
Aude Vuilliomenet, Kate E. Jones, Duncan Wilson   +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