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TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion.
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December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
View Article and Find Full Text PDFA nationwide cross-sectional study in Saudi Arabia for the assessment of understanding and practices of clinicians towards personalized genetic testing.
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December 2024
Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
In order to plan and facilitate the culture of personalized / precision medicine in medical practices within any healthcare institution, it is requisite for healthcare professionals like clinicians to have a clear understanding and approach towards the practices of personalized genetic testing. This nationwide cross-sectional study aimed to measure the perceptions and knowledge of clinicians towards personalized genetic testing and assess their current practices of personalized genetic testing in clinical settings through an online self-administered questionnaire in Saudi Arabia. The results of the study revealed that almost two-fifths of participants were responsible for ordering genetic tests directly (39.
View Article and Find Full Text PDFOptimizing VGG16 deep learning model with enhanced hunger games search for logo classification.
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December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
View Article and Find Full Text PDFDynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
View Article and Find Full Text PDFA novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction.
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December 2024
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research.
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