EXPERT SYSTEMS WITH APPLICATIONS
EXPERT SYSTEMS WITH APPLICATIONS(英文缩写 EXPERT SYST APPL),ISSN 0957-4174,eISSN 1873-6793 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
指标来源：jcr_cas_ifqb
期刊简介
暂无简介。
历年影响因子趋势
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EXPERT SYSTEMS WITH APPLICATIONS 最新收录文献
※ 中文译文由 AI 辅助生成,仅供学术参考,请以英文原文为准。
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Unlocking 3D baby face photogrammetry: Multi-view BabyMorph reconstruction from uncalibrated photographs.
Craniofacial anomalies are important diagnostic markers in early life. Recent studies emphasize the value of 3D imaging for extracting robust facial features that are potential indicators of disease. …
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A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model.
Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the pati…
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Enhancing Text Datasets With Scaling and Targeting Data Augmentation to Improve BERT-Based Machine Learners.
Synthetic data is used to increase a dataset's size for machine learning when acquiring new data is difficult. However, this is difficult for text data due to its symbolic nature. Large language model…
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Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning.
Atrial fibrillation (AF) is associated with progressive structural remodeling of the atria, including chamber dilation, fibrosis, and variations in atrial wall thickness (AWT). Late gadolinium-enhance…
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Deep video anomaly detection in automated laboratory setting.
Laboratory automation integrates robotics, machine learning, and computer vision to enhance precision and efficiency while reducing costs. Despite its pivotal importance in fully automated experimenta…
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Corrigendum to "Identification of gene regulatory networks associated with breast cancer patient survival using an interpretable deep neural network model" [Expert Syst. Appl. 262 (2025) 125632]. “使用可解释的深度神经网络模型识别与癌症患者生存相关的基因调节网络”的更正[Expert Syst.Appl.262（2025）125632]
[This corrects the article PMC11643596.].
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Identification of Gene Regulatory Networks Associated with Breast Cancer Patient Survival Using an Interpretable Deep Neural Network Model. 使用可解释的深度神经网络模型鉴定与癌症患者生存相关的基因调控网络
Artificial neural networks have recently gained significant attention in biomedical research. However, their utility in survival analysis still faces many challenges. In addition to designing models f…
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Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence. 使用可解释的人工智能发现肝细胞癌的新预后生物标志物
Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for mor…
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Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module. 通过应用空间到时间注意力模块从面部视频访谈中检测轻度认知障碍
Early detection of Mild Cognitive Impairment (MCI) leads to early interventions to slow the progression from MCI into dementia. Deep Learning (DL) algorithms could help achieve early non-invasive and …
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MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos. MC ViViT：使用面部视频检测老年人轻度认知障碍的多分支分类器ViViT
Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. Th…