Results 51 to 60 of about 2,210 (159)

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

Next‐Generation Wearable Optical Sensors for Personalized Health and Point‐of‐Care Diagnostics—A Systematic Review

Advanced Healthcare Materials, Volume 15, Issue 10, 13 March 2026.
This review article presents a comprehensive and integrated overview of wearable optical sensors, systematically bridging advanced functional materials with fundamental optical sensing mechanisms. It further highlights their diverse applications across healthcare, fitness monitoring, environmental surveillance, and food safety.
Rituparna Duarah, Helena Torné‐Morató, Guolong Zhang, Youssef Amin, Mahdieh Pabast, Nidhi Sharma, Sailing He, Manash R. Das, Pier Paolo Pompa   +8 more
wiley   +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

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

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

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

Nanozymes Integrated Biochips Toward Smart Detection System

Advanced Science, Volume 13, Issue 11, 23 February 2026.
This review systematically outlines the integration of nanozymes, biochips, and artificial intelligence (AI) for intelligent biosensing. It details how their convergence enhances signal amplification, enables portable detection, and improves data interpretation.
Dongyu Chen, Wang Zheng, Zhihui Zhang, Shenping Yu, Xinxin Hang, Han Wu, Xiao‐Wei Xiang, Wei Mu, Yanli Jiao, Zaizai Dong, Lingqian Chang   +10 more
wiley   +1 more source

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-Based Lightweight AI Healthcare Mobile Chatbot Deployment [PDF]

Anita Christaline Johnvictor,1 M Poonkodi,1 N Prem Sankar,1 Thinesh VS2 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India; 2Arista Networks Pvt Ltd, Bangalore, IndiaCorrespondence: Anita Christaline ...
Poonkodi M, VS T, Johnvictor AC, Prem Sankar N   +3 more
core  

TinyReptile: TinyML with Federated Meta-Learning [PDF]

, 2023
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can ...
Runkler, Thomas A., Anicic, Darko, Ren, Haoyu   +2 more
core   +1 more source