Results 71 to 80 of about 10,214 (201)

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

Cross‐Stream and Cross‐Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand Kinematics

The International Journal of Medical Robotics and Computer Assisted Surgery, Volume 22, Issue 4, August 2026.
ABSTRACT Background Despite the extensive research on skill assessment in minimally invasive surgery, applications in open surgery (OS) remain limited. Methods Twenty trainees performed three OS tasks—knot tying (KT), continuous suturing (CS), and interrupted suturing (IS)—yielding 201 trials.
Constantinos Loukas, Konstantina Prevezanou, Ioannis Seimenis, Stylianos Kykalos, Dimitrios Schizas   +4 more
wiley   +1 more source

EEG Depression Recognition Based on Multi-domain Features Combined with CBAM Model

Journal of Harbin University of Science and Technology
At present, the electroencephalogram (EEG) identification method for depression mainly uses a single feature extraction method, which cannot cover multi-domain feature information, resulting in poor classification performance of the existing model ...
CHEN Yu, HU Xiuxiu, WANG Sheng
doaj   +1 more source

Diagnosing Cryospheric Runoff Dynamics: A Distributed Differentiable Hydrological Model With Global Transfer Learning

Water Resources Research, Volume 62, Issue 8, August 2026.
Abstract Accurate hydrological prediction in alpine regions remains challenging due to complex cryospheric processes and limited observational records. Traditional hydrological models are often subject to structural uncertainty, whereas purely data‐driven deep learning (DL) models may lack physical interpretability under non‐stationary climate ...
Jun Mei, Bin Yong, Gerald Corzo, Yang Hong, Ruochen Sun, Xin Tao, Jiahu Wang, Qingyun Duan   +7 more
wiley   +1 more source

THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE

Barekeng
Coal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing ...
Indra Rivaldi Siregar, Adhiyatma Nugraha, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah   +4 more
doaj   +1 more source

Speech and Language Markers of Bipolar Disorder: Challenges and Opportunities

Bipolar Disorders, Volume 28, Issue 5, August 2026.
ABSTRACT Background Clinicians aspire to predict the emergence of Bipolar Disorder (BD) in a timely manner. To accomplish this, markers reflecting mental states that can be gathered non‐invasively and at large scale are needed. Here, we systematically evaluate evidence relating speech‐based markers to mood states in BD.
Farida Zaher, Jessica Ahrens, Delphine Raucher‐Chéné, Alban Voppel, Lena Palaniyappan   +4 more
wiley   +1 more source

Modelling Time Series Data for Stock Prices Prediction Using Bidirectional Long Short-Term Memory

Knowbase
The dynamic nature of stock markets, characterized by intricate patterns and sudden fluctuations, poses significant challenges to accurate price prediction. Traditional analytical methods are often unable to capture this complexity. This requires the use
Yenie Syukriyah, Adi Purnama
doaj   +1 more source

TEC Prediction Based on Att-CNN-BiLSTM

IEEE Access
Prediction of Total Electron Content (TEC) in the ionosphere is vital to improve the accuracy of satellite positioning, navigation and remote sensing systems. Most existing TEC prediction methods ignored the local variation patterns between various positions within the TEC sequence, resulting in limited prediction accuracy.
Haijun Liu, Haoran Wang, Jing Yuan, Liangchao Li, Lili Zhang   +4 more
openaire   +2 more sources

Detecting Opioid Misuse on Social Media via Named Entity Recognition (NER) With Deep Learning

Expert Systems, Volume 43, Issue 8, August 2026.
ABSTRACT The opioid overdose epidemic constitutes a critical public health crisis, necessitating advanced surveillance tools to enable timely intervention. Social media platforms provide a real‐time source of information on drug‐related behaviours. However, extracting structured knowledge from their informal, slang‐heavy and fragmented text presents ...
Muhammad Ahmad, Rita Orji, Fida Ullah, Ildar Batyrshin, Grigori Sidorov   +4 more
wiley   +1 more source

Analysis of digital intelligent financial audit system based on improved BiLSTM neural network

Nonlinear Engineering
Traditional auditing methods have difficulties in detecting various financial issues hidden in massive amounts of data. With the continuous advancement of deep learning and digital technology, new audit methods have been provided for computer auditing ...
Zhu Xincai
doaj   +1 more source