An integrative approach for the analysis of risk and health across the life course: challenges, innovations, and opportunities for life course research

S Zuber, L Bechtiger, JS Bodelet, M Golin… - Discover social science …, 2023 - Springer
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 …

Machine Learning Investigation of Downburst Prone Environments in Canada

M Hadavi, D Romanic - Journal of Applied Meteorology and …, 2024 - journals.ametsoc.org
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 …

[PDF][PDF] Method for Neural Network Detecting Propaganda Techniques by Markers With Visual Analytic

I Krak, O Zalutska, M Molchanova, O Mazurets… - CEUR Workshop …, 2024 - ceur-ws.org
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 …

[HTML][HTML] Oversampling techniques for imbalanced data in regression

SB Belhaouari, A Islam, K Kassoul, A Al-Fuqaha… - Expert Systems with …, 2024 - Elsevier
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 …

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 …

On the interpretability of the SVM model for predicting infant mortality in Bangladesh

MA Sayeed, A Rahman, A Rahman, R Rois - Journal of Health, Population …, 2024 - Springer
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 …

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 …

Skew Probabilistic Neural Networks for Learning from Imbalanced Data

SM Naik, T Chakraborty, A Hadid… - arXiv preprint arXiv …, 2023 - arxiv.org
Real-world datasets often exhibit imbalanced data distribution, where certain class levels
are severely underrepresented. In such cases, traditional pattern classifiers have shown a …

BERT2D: Two Dimensional Positional Embeddings for Efficient Turkish NLP

YB Kaya, AC Tantuğ - IEEE Access, 2024 - ieeexplore.ieee.org
This study addresses the challenge of improving the downstream performance of pretrained
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

K Skarlatos, G Papageorgiou, P Biris… - Journal of Marine …, 2024 - mdpi.com
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 …