Results 71 to 80 of about 1,376 (159)

Research Progress on Memristors for Compute‐In‐Memory Architectures

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
The era of artificial intelligence and big data has witnessed an explosive growth in computational demands. The von Neumann architecture faces constraints from the “memory wall” and “power wall” due to its separation of storage and computation. Compute‐in‐memory (CIM) technology has emerged as a key pathway to overcome the von Neumann bottleneck.
Yida Shang, Xiang Lin, Xiangyang Che, Yusen Wang, Haotian Ma, Jingkai Zhao, Changcai Zuo, Shixu Yang, Haozhe Gao, Yi Li, Yuliang Liu, Gaoqiang Deng, Guoguang Wu, Xin Dong, Baolin Zhang, Guoxing Li, Yuantao Zhang, Kallol Mohanta   +17 more
wiley   +1 more source

An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network

Complexity, 2020
The shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface ...
Zhijian Huang, Bowen Sui, Jiayi Wen, Guohe Jiang   +3 more
doaj   +1 more source

Mainstream Artificial Intelligence Technologies in Contemporary Ophthalmology

Advanced Intelligent Systems, Volume 7, Issue 12, December 2025.
This review explores the latest artificial intelligence (AI) technologies in ophthalmology, focusing on four key data types: medical imaging, electronic health records, robotic‐assisted surgery, and genomics. It examines the structural features, use cases, clinical goals, and evaluation metrics of various AI algorithms, while also introducing emerging ...
Shiqi Yin, Lei Wang, Shanjun Wu, Jiewei Jiang, Wei Qiang, Yangyang Wang, Shihong Wang, Yi Shao, Wei Chen, Zhongwen Li   +9 more
wiley   +1 more source

Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny

, 2020
The paper aims to classify the defects in a fabric material using deep learning and neural network methodologies. For this paper, 6 classes of defects are considered, namely, Rust, Grease, Hole, Slough, Oil Stain, and, Broken Filament. This paper has implemented both the YOLOv2 model and the YOLOv3 Tiny model separately using the same fabric data set ...
R. Sujee, D. Shanthosh, L. Sudharsun
openaire   +1 more source

AI‐ and Image‐Based Analysis of Emulsification Processes: Opportunities and Challenges

Chemie Ingenieur Technik, Volume 97, Issue 11-12, Page 1070-1084, November 2025.
Smart sensor system and its usage for analysis show great potential for process industry. The development and application of an image‐based sensor using the single‐stage detector YOLO for real‐time analysis of liquid–liquid processes is presented. Opportunities and challenges using real‐time object detection for analysis in laboratory and industrial ...
Inga Burke‐Oeing (née Burke), Sebastian Derkum, Tom Olatomiwa Olakunle Olusanya, Norbert Kockmann   +3 more
wiley   +1 more source

Explainable Attention‐Enhanced Approach for Multimodal Breast Cancer Diagnosis Across Diverse Imaging Modalities

International Journal of Imaging Systems and Technology, Volume 35, Issue 6, November 2025.
ABSTRACT Early and accurate detection of breast cancer is critical for improving survival rates. This study presents a robust deep learning framework that integrates convolutional and attention‐based modules to enhance feature extraction across various imaging modalities.
Uzma Nawaz, Zubair Saeed, Hafiz Muhammad UbaidUllah, Farheen Mirza, Mirza Muzzamil   +4 more
wiley   +1 more source

Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments

IEEE Access, 2018
Object detection in streaming images is a major step in different detection-based applications, such as object tracking, action recognition, robot navigation, and visual surveillance applications.
Dong Kyun Shin, Minhaz Uddin Ahmed, Phill Kyu Rhee   +2 more
doaj   +1 more source

Computer vision‐based real‐time cable safety assessment under vehicle‐induced bridge fires

Computer-Aided Civil and Infrastructure Engineering, Volume 40, Issue 28, Page 5269-5287, 28 November 2025.
Abstract Vehicle‐induced fires present a critical risk to cable‐supported bridges, where the integrity of cable components is especially vulnerable. However, conventional monitoring solutions face significant limitations: infrared cameras are often economically prohibitive, and standard smoke detectors are ineffective in open bridge environments.
Jinglun Li, Binyang Wang, Xiaoyi Zhou, Raffaele Cucuzza, Kang Gao, Xiang Yun   +5 more
wiley   +1 more source

An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network.

PLoS ONE, 2019
Artificial intelligence (AI) tools have been applied to diagnose or predict disease risk from medical images with recent data disclosure actions, but few of them are designed for mobile terminals due to the limited computational power and storage ...
Shanchen Pang, Shuo Wang, Alfonso Rodríguez-Patón, Pibao Li, Xun Wang   +4 more
doaj   +1 more source

Robust real-time detection and tracking of tennis via YOLOBR and FTOC in complex video streams

Discover Artificial Intelligence
Detecting tiny, fast-moving objects such as tennis shuttlecocks remains challenging due to their small size, high velocity, and complex backgrounds. This paper demonstrates a hybrid visual system that incorporates classical machine learning and deep ...
Haiyan Wang
doaj   +1 more source