The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved belug...
Data training algorithms based on Artificial Intelligence (AI) often encounter overfitting, underfitting, or bias issues. This article presents the design of a hybrid self-learning algorithm to address the above challenges. The proposed approach is d...
This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the nec...
Chest X-ray (CXR) represents one of the most widely utilized clinical diagnostic tools for thoracic diseases. Nevertheless, computer-aided diagnosis based on chest radiographs still faces considerable challenges in anomaly detection. Certain lesions ...
Early identification of students' mental health issues has become an urgent priority in education and public health. However, existing studies often rely on questionnaire-based assessments or traditional machine learning models, which are limited by ...
PURPOSE: To propose a multi-parametric ultrasound imaging-based deep learning method for accurately classifying metastatic and non-metastatic axillary lymph nodes in breast cancer patients.
BACKGROUND: Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence f...
Kidney stone disease is a common syndrome and a recurring one, where it bears a 50% chance of being manifested again within ten years and may lead to serious complications like ureteral obstruction and unbearable pain. If timely intervention is consi...
U-Net has gained traction in biomedical signal processing, particularly for segmenting 1D waveforms. Building on this success, we propose a U-Net-inspired architecture that integrates both 2D and 1D CNNs to effectively learn and segment gastroesophag...
Effective prediction of Aedes mosquito abundance and dengue risk indicators such as the Aedes Index (AI) and Dengue Positive Trap Index (DPTI) is essential for early intervention and targeted vector control. However, current models often rely on coar...
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