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Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking.
Sci Rep
January 2025
Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, Jilin, China.
In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans.
View Article and Find Full Text PDF[To comprehensively improve the diagnosis and treatment of critical care medicine by focusing on cognition, recognition and workflow management].
Zhonghua Nei Ke Za Zhi
February 2025
Department of Critical Care Medicine, West China Tianfu Hospital, Sichuan University, Chengdu610213, China.
An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.
Nutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFTime-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period.
Sensors (Basel)
January 2025
Department of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Yongin-si 16890, Republic of Korea.
In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques.
View Article and Find Full Text PDFUsing Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed.
Sensors (Basel)
January 2025
Curtin School of Allied Health, Curtin University, Perth 6102, Australia.
In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary.
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