Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fa...
The classification and diagnosis of pancreatic tumors present significant challenges due to their inherent complexity and variability. Traditional methods often struggle to capture the dynamic nature of these tumors, highlighting the need for advance...
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and early pathological diagnosis is crucial for formulating treatment plans. Despite the widespread attention to pathology in the treatment of HCC patients,...
This paper proposes a hybrid model combining Transformer and Temporal Convolutional Network (TCN). This model addresses the shortcomings of current approaches in capturing long-term and short-term dependencies in network traffic prediction tasks. The...
Neural networks : the official journal of the International Neural Network Society
Apr 22, 2025
We propose a novel quaternionic time series compression methodology where we divide a long time series into segments of data, extract the min, max, mean and standard deviation of these chunks as representative features and encapsulate them in a quate...
Neural networks : the official journal of the International Neural Network Society
Apr 22, 2025
Equivariant neural networks incorporate symmetries into their architecture, achieving higher generalization performance. However, constructing equivariant neural networks typically requires prior knowledge of data types and symmetries, which is diffi...
Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein...
OBJECTIVE: Artificial neural networks have been used in various fields to solve classification and prediction tasks. However, it is unclear if these may be adequate methods to predict psychological treatment outcomes. This study aimed to evaluate the...
OBJECTIVE: Breast cancer poses a major health concern for women globally. Effective segmentation of breast tumors for ultrasound images is crucial for early diagnosis and treatment. Conventional convolutional neural networks have shown promising resu...
Increasing medical research evidence suggests that even low PM concentrations may trigger significant health issues. Hence, an accurate prediction of PM holds immense significance in securing public health safety. However, current data-drive predicti...
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