An integrative approach for the analysis of risk and health across the life course: challenges, innovations, and opportunities for life course research
Life course epidemiology seeks to understand the intricate relationships between risk factors
and health outcomes across different stages of life to inform prevention and intervention …
and health outcomes across different stages of life to inform prevention and intervention …
Machine Learning Investigation of Downburst Prone Environments in Canada
Thunderstorms are recognized as one of the most disastrous weather threats in Canada
because of their power to cause substantial damage to human-made structures and even …
because of their power to cause substantial damage to human-made structures and even …
[PDF][PDF] Method for Neural Network Detecting Propaganda Techniques by Markers With Visual Analytic
The paper is devoted to the creation and approbation of the method for neural network
detecting propaganda techniques by markers with visual analytic, which allows converting …
detecting propaganda techniques by markers with visual analytic, which allows converting …
[HTML][HTML] Oversampling techniques for imbalanced data in regression
Our study addresses the challenge of imbalanced regression data in Machine Learning (ML)
by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor …
by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor …
Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk
S Gunonu, G Altun, M Cavus - arXiv preprint arXiv:2407.11089, 2024 - arxiv.org
The accuracy and understandability of bank failure prediction models are crucial. While
interpretable models like logistic regression are favored for their explainability, complex …
interpretable models like logistic regression are favored for their explainability, complex …
On the interpretability of the SVM model for predicting infant mortality in Bangladesh
Background Although machine learning (ML) models are well-liked for their outperformance
in prediction, greatly avoided due to the lack of intuition and explanation of their predictions …
in prediction, greatly avoided due to the lack of intuition and explanation of their predictions …
On the usefulness of synthetic tabular data generation
D Manousakas, S Aydöre - arXiv preprint arXiv:2306.15636, 2023 - arxiv.org
Despite recent advances in synthetic data generation, the scientific community still lacks a
unified consensus on its usefulness. It is commonly believed that synthetic data can be used …
unified consensus on its usefulness. It is commonly believed that synthetic data can be used …
Skew Probabilistic Neural Networks for Learning from Imbalanced Data
Real-world datasets often exhibit imbalanced data distribution, where certain class levels
are severely underrepresented. In such cases, traditional pattern classifiers have shown a …
are severely underrepresented. In such cases, traditional pattern classifiers have shown a …
BERT2D: Two Dimensional Positional Embeddings for Efficient Turkish NLP
This study addresses the challenge of improving the downstream performance of pretrained
language models for morphologically rich languages, with a focus on Turkish. Traditional …
language models for morphologically rich languages, with a focus on Turkish. Traditional …
Ship Engine Model Selection by Applying Machine Learning Classification Techniques Using Imputation and Dimensionality Reduction
The maritime is facing a gradual proliferation of data, which is frequently coupled with the
presence of subpar information that contains missing and duplicate data, erroneous records …
presence of subpar information that contains missing and duplicate data, erroneous records …