BiLSTM, an ICD-11 automatic coding model using MC-BERT and label attention. Experiments on clinical records show 83.86% ...
Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...
Abstract: Fine-grained flower image classification (FGFIC) is challenging due to high similarities among species and variations within species, especially with limited training data. Existing genetic ...
Abstract: Skin cancer ranks among ubiquitous malignancies, its prevalence escalating due to ecological shifts and protracted ultraviolet (UV)exposure. This study aims to address the pressing need for ...
Abstract: Plant and leaf diseases have a significant impact on agricultural production, leading to a decrease in crop yield and quality. Effective crop management demands early and precise detection ...
Abstract: Non-intrusive load monitoring (NILM) is a solution for manage energy consumption because cheaper and easier to implement than other methods. NILM can be applied to classify the type of ...
This project implements a state-of-the-art CNN architecture for CIFAR-10 image classification, achieving 88.82% accuracy through systematic hyperparameter optimization. The implementation includes GPU ...
Abstract: Hyperspectral Imaging (HSI) has undeniably transformed various real-world applications by capturing intricate spectral information at every pixel. Nevertheless, the nonlinear relationships ...
Abstract: Arrhythmia refers to abnormal deviations in the normal heart rhythm, possibly signifying severe cardiovascular conditions. The manual identification of arrhythmias by ECG analysis is ...