Biomedical physics & engineering express
Oct 3, 2025
This paper proposes a novel gesture prediction method for accurately predicting hand gesture types from raw sEMG signals in real time. First, we utilize a linear combination of the mean and standard deviation of sEMG signals within a sliding window t...
Biomedical physics & engineering express
Oct 3, 2025
Histopathological imaging is of paramount importance for the initial detection, diagnosis, and classification of tumors. Recurrent neural networks and convolutional neural networks have led to substantial advancements in digital pathology, thereby en...
BACKGROUND: Lateral malleolar avulsion fractures (LMAFs) and subfibular ossicles (SFOs) are distinct entities that both present as small bone fragments near the lateral malleolus in imaging but require different treatment strategies. Clinical and rad...
Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. Howev...
Accurate prediction of chiller energy consumption is crucial for reducing building energy consumption. In this study, an innovative dual-branch network architecture DNTB (A Dual-Branch Network Model Based on Transformer and Bi-LSTM for Energy Consump...
Journal of chemical information and modeling
Oct 1, 2025
The expansion of large-scale materials databases has facilitated the development of graph-based representations, encoding structural and functional similarities as edges in data-driven networks. These enable machine learning models to leverage both l...
MOS gas sensors offer significant potential for real-time dissolved gas analysis (DGA) in power transformer monitoring. However, their performance is often degraded in high-hydrogen (H) environments due to cross-interference, which impairs detection ...
LC-MS has become an essential tool for the analysis of complex samples. However, conventional MS data processing often involves cumbersome workflows and is prone to loss of information, particularly in the context of chemically diverse natural produc...
BACKGROUND: The progression of cancer is driven by the accumulation of mutations in driver genes. Many researches promote to identify cancer driver genes. However, most of them ignore the high-order features in the network.
To increase the accuracy as well as effectiveness of predicting the level of CO in mushroom cultivating greenhouses, two optimized prediction models of long and short term memory neural networks (VMD-SSA-LSTM and VMD-DBO-LSTM) are proposed. To start ...
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