Results 81 to 90 of about 93,524 (286)

Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms

Entropy, 2020
In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important.
Weronika Wegier, Pawel Ksieniewicz
doaj   +1 more source

Machine learning‐based prediction of elevated N terminal pro brain natriuretic peptide among US general population

ESC Heart Failure, Volume 12, Issue 2, Page 859-868, April 2025.
Abstract Aims Natriuretic peptide‐based pre‐heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre‐heart failure, has not been well established.
Yuichiro Mori, Shingo Fukuma, Kyohei Yamaji, Atsushi Mizuno, Naoki Kondo, Kosuke Inoue   +5 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

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG

Epileptic Disorders, EarlyView.
Abstract Objective Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post‐febrile seizure electroencephalography (EEG) recordings.
Boran Şekeroğlu, Hüseyin Öztoprak, Özlem Yayıcı Köken, Hatice Demir, Mehpare Sarı Yanartaş, Deniz Yılmaz, Burçin Şanlıdağ, Ayşegül Neşe Çıtak Kurt   +7 more
wiley   +1 more source

A translational multimodal machine‐learning prototype predicting valproate response in epilepsy treatment

Epilepsia, EarlyView.
Abstract Objective Epilepsy affects ~1% of the global population and often requires lifelong antiseizure medication (ASM) therapy. Valproic acid (VPA) is a commonly prescribed first‐line ASM, yet only approximately half of patients achieve sustained seizure freedom. Treatment selection remains largely empirical.
Simeon Platte, Afsheen Kumar, Giorgia Guerini, Massimo Pandolfo, Denise Haslinger, Colin B. Josephson, Guillermo Delgado‐García, Navprabhjot Kaur, Michaela‐Pauline Lux, Heiko Stempfle, Chantal Depondt, Reetta Kälviäinen, Felix Rosenow, Sophie von Brauchitsch, Karl Martin Klein, Andreas G. Chiocchetti   +15 more
wiley   +1 more source

Asynchronous Channel Training in Multi-Cell Massive MIMO

, 2017
Pilot contamination has been regarded as the main bottleneck in time division duplexing (TDD) multi-cell massive multiple-input multiple-output (MIMO) systems. The pilot contamination problem cannot be addressed with large-scale antenna arrays.
Jafarkhani, Hamid, Zou, Xun
core  

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

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 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 Faults in Power Transformers Based on Machine‐Learning Algorithms Compared With Other Techniques

Energy Science &Engineering, EarlyView.
General framework of ensemble learning technique for transformer fault diagnostics compared with traditional dissolved gas analysis methods. ABSTRACT This paper implemented a comprehensive variety of modern machine‐learning techniques, which were demonstrated to be effective in handling complex tabular data, generating accurate predictions, and ...
Osama E. Gouda, Nourhan Ramadan, Matti Lehtonen, Mohamed M. F. Darwish   +3 more
wiley   +1 more source