Results 61 to 70 of about 6,008 (208)

MIF‐MAPMS: Enhancing identification of myelin autoantigenic peptides in multiple sclerosis through multimodal information fusion

Protein Science, Volume 35, Issue 8, August 2026.
Abstract Multiple sclerosis (MS) arises from an autoimmune response in which the immune system erroneously targets myelin autoantigens within the central nervous system, leading to myelin degradation and subsequent neurological dysfunction. Identifying myelin autoantigenic peptides (MAPs) is therefore critical for understanding MS pathogenesis and ...
Watshara Shoombuatong, Nalini Schaduangrat, Pramote Chumnanpuen, Lawankorn Mookdarsanit, Pakpoom Mookdarsanit   +4 more
wiley   +1 more source

Unidirectional-bidirectional recurrent networks for cardiac disorders classification

, 2021
The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study.
Firdaus, Firdaus; Universitas Sriwijaya, Rachmatullah, Muhammad Naufal; Universitas Sriwijaya, Nurmaini, Siti; Universitas Sriwijaya, Darmawahyuni, Annisa; Universitas Sriwijaya, Tutuko, Bambang; Universitas Sriwijaya   +4 more
core   +2 more sources

Heterogeneous Deep‐Ensemble Framework for Sentiment Analysis of Movie Reviews Based on Stacking and Voting

Concurrency and Computation: Practice and Experience, Volume 38, Issue 11, June 2026.
ABSTRACT Sentiment analysis of movie reviews reflects users' behavioral preferences and emotional responses to films, aiding in understanding audience needs and enhancing user experience. In recent years, deep ensemble learning has significantly improved sentiment polarity prediction by combining multiple deep models.
Hanzhe Cai, Ze Wu, Seena Joseph
wiley   +1 more source

Defense Algorithm Based on Reinforcement Learning and Digital Twin for Distribution Protection Devices Adversarial Samples

Engineering Reports, Volume 8, Issue 6, June 2026.
Power system protection devices are vulnerable to adversarial samples. We propose a digital twin and reinforcement learning framework. It trains a virtual model within safe operational boundaries, reducing the false operation rate to below 3.5%. The model's inference delay is under 10 ms, meeting real‐time protection requirements.
Wei Zhang, Shuai Zhang, Song Wang, Zheran Zheng   +3 more
wiley   +1 more source

Optimizing the Collaborative Supply Scheduling of Renewable Energy in the Tourism Industry Chain Using Graph Neural Networks

Engineering Reports, Volume 8, Issue 6, June 2026.
Typical configuration of Huangshan Scenic Area. ABSTRACT To cope with the supply and demand imbalance challenge caused by the surge in energy demand in tourism‐intensive areas and the high volatility of renewable energy, this paper aims to construct a collaborative scheduling optimization model based on graph neural networks (GNNs). The model abstracts
Lixia Wang
wiley   +1 more source

Comparative performance analysis of LSTM, GRU, and bidirectional neural networks for political ideology classification

Political ideology classification is crucial for understanding social polarization, monitoring democratic processes, and identifying bias on online platforms. This study compares the performance of long short-term memory (LSTM), gated recurrent unit (GRU)
Permadi, Ipung, Iqbal, Iqbal, Afuan, Lasmedi, Hidayat, Nurul, Bintang Pradana Yosua, Panky, Suprihanto, Didit, Alfarez Marchelian, Reyno   +6 more
core   +2 more sources

Semantic Evolution and Consistency Learning for Robust Malicious Network Traffic Detection

Engineering Reports, Volume 8, Issue 6, June 2026.
This paper proposes a semantic evolution and consistency network (SECN) for malicious traffic detection, modeling attack behaviors as temporally evolving semantics. By integrating dual‐level temporal representation and semantic consistency constraints, SECN achieves robust detection and strong generalization under encrypted, cross‐dataset, and unknown ...
Jing Yang, Wei Tan
wiley   +1 more source

RUL Prediction of Rolling Bearings via Adaptive Multi‐Scale Feature Alignment and Semantic Fusion

Engineering Reports, Volume 8, Issue 6, June 2026.
An adaptive multi‐scale framework integrates denoising, feature fusion, and degradation‐stage‐driven alignment to suppress noise, capture local–global degradation patterns, and enable robust and accurate remaining useful life prediction. ABSTRACT Remaining useful life (RUL) prediction of rolling bearings is essential for intelligent maintenance and ...
Yuanyuan Wu, Ning Li
wiley   +1 more source

Seismic phase recognition model with low SNR based on U-net

Geomatics, Natural Hazards & Risk
Aiming at the problem of low recognition accuracy and high missed detection rate of seismic phase recognition of low signal-to-noise ratio seismic signals, a new seismic phase recognition model UBAN (U-net-Bidirectional Gated Recurrent Unit-Attention ...
Jianxian Cai, Zhongjie Sun, Mengying Zhang, Fenfen Yan, Li Wang, Ling Li   +5 more
doaj   +1 more source

Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

IEEE Access, 2020
Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) have been widely used in the field of text sentiment analysis and have achieved good results.
Yan Cheng, Leibo Yao, Guoxiong Xiang, Guanghe Zhang, Tianwei Tang, Linhui Zhong   +5 more
doaj   +1 more source