Results 81 to 90 of about 92,198 (268)

Sleep enhances gamma oscillations in the seizure onset zone and broadband activity in the irritative zone of focal cortical dysplasia

Epilepsia Open, EarlyView.
Abstract Objective Focal cortical dysplasia (FCD) is a leading cause of drug‐resistant epilepsy and is associated with sleep‐related seizures, yet the underlying electrophysiological mechanisms during different brain states remain poorly understood. We investigated whether fast oscillations (FOs) within the seizure onset zone (SOZ) and irritative zone (
Mohammad F. Khazali, Nina Schwoon, Armin Brandt, Matthias Dümpelmann, Yiwen Li Hegner, Nicolas Roehri, Dirk‐Matthias Altenmüller, Victoria San Antonio‐Arce, Peter C. Reinacher, Julia M. Nakagawa, Soroush Doostkam, Theo Demerath, Horst Urbach, Andreas Schulze‐Bonhage, Marcel Heers   +14 more
wiley   +1 more source

Decision Support Model for Time Series Data Augmentation Method Selection

IEEE Access
Data augmentation (DA) plays a crucial role in machine learning by improving model generalization and tackling data scarcity issues, particularly prevalent in domains with limited access to sensitive information or rare events.
Dorian Joubaud, Sylvain Kubler, Raoni Lourenco, Maxime Cordy, Yves Le Traon   +4 more
doaj   +1 more source

Using Variational Auto Encoding in Credit Card Fraud Detection

IEEE Access, 2020
Machine learning approaches are widely used to analyze and detect the increasingly serious problem of credit card fraud. However, typical credit card datasets present imbalanced classification situations because of severely skewed class distributions ...
Huang Tingfei, Cheng Guangquan, Huang Kuihua   +2 more
doaj   +1 more source

Prediction of Pipeline Defect Depth and Classification Based on CatBoost

Energy Science &Engineering, EarlyView.
Obtaining detection data using in‐line pipeline inspection, the synthetic minority oversampling technique (SMOTE) is applied to expand the sample set, thereby increasing the number of minority‐class samples. This approach effectively improves minority‐class detection and enhances pipeline safety assessment. ABSTRACT Magnetic flux leakage detection is a
Cong Chen, Rui Li, Guanwei Jia, Dongcun Qin   +3 more
wiley   +1 more source

Interpretable Tree‐Based Models for Predicting Short‐Term Rockburst Risk Considering Multiple Factors

Energy Science &Engineering, EarlyView.
Interpretable tree‐based models integrate microseismic, geological, and mining indicators to predict short‐term rockburst risk. SHAP analysis reveals the dominant role of energy‐related features and clarifies nonlinear factor interactions, enabling transparent and reliable early‐warning in deep coal mines.
Shuai Chen, Linming Dou, Atao Li, Wu Cai, Lei Zhang   +4 more
wiley   +1 more source

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods

Head &Neck, EarlyView.
ABSTRACT Background Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. Methods Clinical data and postoperative flap photos of head and neck cancer patients (2018–2024) at our center were retrospectively collected, categorized into normal and venous ...
Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang, Qi Zhong   +10 more
wiley   +1 more source

GMO-AC: Gaussian-Based Minority Oversampling With Adaptive Outlier Filtering and Class Overlap Weighting

IEEE Access
Imbalanced data significantly affects the performance of standard classification models. Data-level approaches primarily use oversampling methods, such as the synthetic minority oversampling technique (SMOTE), to address this problem.
Seung Jee Yang, Kyungjoon Cha
doaj   +1 more source

Interpretable machine learning enables early and accurate detection of drug‐induced liver injury: A multicenter study with real‐world clinical translation

Interdisciplinary Medicine, EarlyView.
This study develops an interpretable gradient‐boosting model that accurately identifies drug‐induced liver injury (DILI) using routine laboratory data. The model explains key clinical features through SHapley Additive exPlanations analysis and detects DILI earlier than expert evaluation, offering a transparent and practical tool for precision ...
Jingyi Ling, Shian Zhang, Jun Lin, Zhengju Xu, Runcheng Xu, Qian Lv, Kaixiang Zhang, Sheng Liu, Jie Guo, Cheng Hua, Yin Jia, Xiaoyu Xu, Kun Qian, Shanrong Liu   +13 more
wiley   +1 more source

Differences in patterns of attention deficit/hyperactivity disorder medication use in US children

JCPP Advances, EarlyView.
Abstract Background Understanding attention deficit/hyperactivity disorder (ADHD) medication patterns is crucial for optimizing treatment outcomes. There are limited data on racial, ethnic, gender and socioeconomic treatment differences across longitudinal national samples.
Jennie E. Ryan, Alexander Weigard, Sean Esteban McCabe, Timothy E. Wilens, Philip T. Veliz   +4 more
wiley   +1 more source

Decision‐making and risk‐taking as predictors of health risk behaviors in the Millennium Cohort Study

JCPP Advances, EarlyView.
Abstract Background Facets of decision‐making and risk‐taking are implicated in adolescent health risk behaviors; however, whether they may lead to adolescent engagement in substance use, gambling, and self‐harm is unknown. Methods We used the Millennium Cohort Study to test whether a task‐based measure of decision‐making and risk‐taking predicts ...
Nicole G. Hammond, Georgia Condran, Elise E. DeVito, Marie‐Claude Geoffroy, Ian Colman   +4 more
wiley   +1 more source