[HTML][HTML] Ensemble methods with feature selection and data balancing for improved code smells classification performance

PS Yadav, RS Rao, A Mishra, M Gupta - Engineering Applications of …, 2025 - Elsevier
Code smells are software flaws that make it challenging to comprehend, develop, and
maintain the software. Identifying and removing code smells is crucial for software quality …

Coal mine gas emission prediction based on multifactor time series method

H Lin, W Li, S Li, L Wang, J Ge, Y Tian… - Reliability Engineering & …, 2024 - Elsevier
The prediction of coal mine gas emission is an important indicator for ventilation systems
reliability and a data basis for mine gas extraction design. The traditional gas emission …

Sustainable financial fraud detection using garra rufa fish optimization algorithm with ensemble deep learning

M Maashi, B Alabduallah, F Kouki - Sustainability, 2023 - mdpi.com
Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical
practices in the detection of fraudulent activities in the financial sector. Credit card fraud …

[HTML][HTML] AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks

F Nawshin, D Unal, M Hammoudeh, PN Suganthan - Ad Hoc Networks, 2024 - Elsevier
The widespread usage of Android-powered devices in the Internet of Things (IoT) makes
them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks …

Advancing Model Performance With ADASYN and Recurrent Feature Elimination and Cross-Validation in Machine Learning-Assisted Credit Card Fraud Detection: A …

E Ileberi, Y Sun - IEEE Access, 2024 - ieeexplore.ieee.org
Online card transactions have become more frequent due to the growth of e-commerce and
financial technology apps. However, this also means more opportunities for credit card …

[HTML][HTML] Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models

Y Duan, R Wang, Z Huang, H Chen, M Tang… - Hellenic Journal of …, 2024 - Elsevier
Objective This study aimed to leverage real-world electronic medical record data to develop
interpretable machine learning models for diagnosis of Kawasaki disease while also …

[HTML][HTML] Joint use of population pharmacokinetics and machine learning for prediction of valproic acid plasma concentration in elderly epileptic patients

P Ma, S Shang, Y Huang, R Liu, H Yu, F Zhou… - European Journal of …, 2024 - Elsevier
Background Valproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For
elderly epileptic patients, VPA plasma concentrations have a considerable variation. We aim …

[HTML][HTML] Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning

S Seo, Y Choi, C Koo - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Since the frequency and intensity of heatwaves are expected to increase due to global
warming, it is crucial to establish a simplified approach to preventing personal heat-related …

Superpixel segmentation integrated feature subset selection for wetland classification over Yellow River Delta

L Cui, J Zhang, Z Wu, L Xun, X Wang, S Zhang… - … Science and Pollution …, 2023 - Springer
Wetlands are one of the world's most significant and vulnerable ecosystems. The wetlands
of the Yellow River Delta are subject to multiple influences of ocean tidal action and the …

An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured …

J Al Faysal, M Noor-E-Alam, GJ Young… - Computers in Biology …, 2024 - Elsevier
Objectives Buprenorphine is an effective evidence-based medication for opioid use disorder
(OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the …