Avian Influenza (AI), caused by highly pathogenic strains of influenza viruses, poses a significant threat to poultry populations and public health worldwide. This study offers a comprehensive evaluation of the spatial and temporal dynamics of HPAI o...
As widely spread social media platform, Twitter (Now named as X) is seamlessly used by many to share their thoughts and opinions. Twitter Avatar which is the profile image of the user, is initially uploaded when the user takes his account and never c...
This study proposes an environment- and signer-invariant sign language recognition model. The model first extracts skeletal key-points from the signer via MediaPipe, which is Google's cross-platform pipeline framework that helps to detect and track h...
Epidemiological data is often analyzed without fully accounting for the uncertainties that are key to understanding the nuances of the dataset. While traditional approaches like the SIR mathematical model provide valuable insights, our study aims to ...
Surface electromyography (EMG) provides a non-invasive human-machine interaction interface that can promote the coherence of human-machine interaction operations. Decomposing surface electromyographic signals into hand joint angles in real time can b...
We evaluated the effectiveness of magnetic resonance imaging (MRI)-based subregional texture analysis (TA) models for classifying knee osteoarthritis (OA) severity grades by compartment. We identified 122 MR images of 121 patients with knee OA (mild-...
Prostate cancer is characterized by an immunosuppressive tumour environment. This work combines Raman spectroscopy with group-and-bases-restricted non-negative matrix factorization (GBR-NMF) and machine learning to assemble models of immune cell dens...
Energy expenditure (EE) assessment is crucial in both sports science and health management. However, current EE prediction models often overlook individual differences and lack dynamic correlation analysis between multi-modal data and EE. Building up...
This study aims to identify risk factors associated with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetesmellitus (T2DM) and to develop a predictive model to support clinical decision-making. A total of 1,001 patients with T2DM w...
This study presents an automated system using Convolutional Neural Networks (CNNs) for segmenting FLAIR Magnetic Resonance Imaging (MRI) images to aid in the diagnosis of Multiple Sclerosis (MS). The dataset included 103 patients from Imam Khomeini H...
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