Results 61 to 70 of about 2,597 (210)

XiNet: Efficient Neural Networks for tinyML

2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
The recent interest in the edge-to-cloud continuum paradigm has emphasized the need for simple and scalable architectures to deliver optimal performance on computationally constrained devices. However, resource-efficient neural networks usually optimize for parameter count and thus use operators such as depthwise convolutions, which do not maximally ...
Alberto Ancilotto, Francesco Paissan, Elisabetta Farella   +2 more
openaire   +2 more sources

An Overview of Deep Learning Techniques for Big Data IoT Applications

International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.
Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur, Vipin Balyan, Sindhu Hak Gupta   +2 more
wiley   +1 more source

CMOS-MEMS Gas Sensor Dubbed GMOS for SelectiveAnalysis of Gases with Tiny Edge Machine Learning

Engineering Proceedings, 2022
Embedded machine learning, TinyML, is a relatively new and fast-growing field of ML, enabling on-device sensor data analytics at low power requirements. This paper presents possible improvements to GMOS, a gas sensor, using TinyML technology.
Adir Krayden, Maayan Schohet, Oz Shmueli, Dima Shlenkevitch, Tanya Blank, Sara Stolyarova, Yael Nemirovsky   +6 more
doaj   +1 more source

Datasheets for machine learning sensors

AI Magazine, Volume 47, Issue 1, Spring 2026.
Abstract Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These “ML sensors” enable context‐sensitive, real‐time data collection and decision‐making across diverse applications ranging from anomaly detection in industrial settings to wildlife tracking for conservation efforts.
Matthew Stewart, Yuke Zhang, Pete Warden, Yasmine Omri, Shvetank Prakash, Jacob Huckelberry, Joao Henrique Santos, Shawn Hymel, Benjamin Yeager Brown, Jim MacArthur, Nat Jeffries, Emanuel Moss, Mona Sloane, Brian Plancher, Vijay Janapa Reddi   +14 more
wiley   +1 more source

OBJECT DETECTION ALGORITHMS IMPLEMENTATION ON EMBEDDED DEVICES: CHALLENGES AND SUGGESTED SOLUTIONS

Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ
Object detection and image classification are among the most important areas to which scientific research is directed, which are commonly used in various applications based on computer vision.
Ruqaya Alaa, Ehab Hussein, Hilal Al-libawy   +2 more
doaj   +1 more source

Leveraging Lightweight AI for Anomaly Detection in Mechanical Power Transmission At The Edge [PDF]

EPJ Web of Conferences
The integration of IoT and TinyML platforms has revolutionized fault detection and condition monitoring in electromechanical systems, enabling real-time data acquisition and analysis in resource-constrained environments.
Ayadi Walid, Amouri Amar, Ali Saeed, Alzahmi Nada, Khalid Mohammad Nadeem   +4 more
doaj   +1 more source

Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)

IEEE Access, 2022
Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and ...
Syed Ali Raza Zaidi, Ali M. Hayajneh, Maryam Hafeez, Q. Z. Ahmed   +3 more
doaj   +1 more source

Building an Intelligent Cardiovascular System Platform: Embedding Artificial Intelligence across All Facets of Cardiovascular Medicine

Advanced Intelligent Systems, Volume 8, Issue 3, March 2026.
This paper presents an integrated AI‐driven cardiovascular platform unifying multimodal data, predictive analytics, and real‐time monitoring. It demonstrates how artificial intelligence—from deep learning to federated learning—enables early diagnosis, precision treatment, and personalized rehabilitation across the full disease lifecycle, promoting a ...
Mowei Kong, Xingwei Zhao, Qiuting Li, Yang Yu, Chunxiang Zhang   +4 more
wiley   +1 more source

Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.
Three key parts of our review. This review examines recent research on integrating machine learning with edge computing in healthcare. It is structured around three key parts: the demographic characteristics of the selected studies; the themes, tools, motivations, and data sources; and the key limitations, challenges, and future research directions ...
Amir Mashmool, Giorgio Delzanno, Hamid Saadatfar, Aakash Ahmad, Rainer Koschke, Roohallah Alizadehsani, U. Rajendra Acharya, Daniele D'Agostino   +7 more
wiley   +1 more source

The Case for Hierarchical Deep Learning Inference at the Network Edge [PDF]

, 2023
Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, developing tinyML models is an area of active
Al Atat, Ghina, Behera, Adarsh Prasad, Champati, Jaya Prakash, Fresa, Andrea, Gross, James, Moothedath, Vishnu Narayanan   +5 more
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