Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. This experimental research focuses on evaluating existing robust cross-validation variants to address the issues of validating credit default models. …”
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An exploratory study of high-educated poverty through machine learning approach: a case study of East Java, Indonesia by Dias Satria, Risti Permani, Kodrad Winarno, David Kaluge, Citra Rahayu Indraswari, Radityo Putro Handrito

“…Research methodology – using data from the Indonesian National Survey, this study employs Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN) algorithms as classification methods in machine learning.  …”
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PaySwitch: Smart Contract-Based Payment Switch for Off-Chain Payment Channel Networks by Anan Jin, Yuhang Ye, Brian Lee, Xiang Li, Yuansong Qiao

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The prognostic and therapeutic significance of polyunsaturated fatty acid‐derived oxylipins in ST‐segment elevation myocardial infarction by Zhiyong Du, Yingyuan Lu, Ying Ma, Yunxiao Yang, Wei Luo, Sheng Liu, Ming Zhang, Yong Wang, Lei Li, Chun Li, Wei Wang, Hai Gao

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Fostering non-intrusive load monitoring for smart energy management in industrial applications: an active machine learning approach by Lukas Fabri, Daniel Leuthe, Lars-Manuel Schneider, Simon Wenninger

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Performance Estimation of Low Power and Area-Efficient Parallel Pipelined FFT by Surya P, Arunachalaperumal C, Dhilipkumar S

“…The proposed FFT processor utilizes only 8 % of the available look-up tables (LUTs) with a 1:3 ratio in resource utilization and a 56 % reduction in delays compared to previous research. This makes this architecture best suited for high-speed wireless communications and 5G applications. …”
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YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

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Credit card default prediction using ML and DL techniques by Fazal Wahab, Imran Khan, Sneha Sabada

“…This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. …”
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Among the DLTs: Holochain for the Security of IoT Distributed Networks—A Review and Conceptual Framework by Shereen Ismail, Raouf Mehannaoui, Eden Teshome Hunde, Hassan Reza

“…Holochain, an emerging distributed ledger technology (DLT), offers the benefits of blockchain while overcoming its limitations, such as the reliance on consensus algorithms and a globally synchronized ledger. As a result, Holochain has garnered attention in the research community as a promising solution for distributed IoT applications. …”
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An automated approach to identify sarcasm in low-resource language. by Shumaila Khan, Iqbal Qasim, Wahab Khan, Aurangzeb Khan, Javed Ali Khan, Ayman Qahmash, Yazeed Yasin Ghadi

“…While fewer studies identifying sarcasm have focused on low-resource languages, most of the work is in English. This research addresses the gap by exploring the efficacy of diverse machine learning (ML) algorithms in identifying sarcasm in Urdu. …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

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Image Enhancement Techniques for Images at Blur Motion and Different Noises by Sahlah Ali

“…So it is one of the research titles of interest to the researchers. The images get degraded due to environmental conditions and atmospheric difference, it is therefore important to retrieve original images using different algorithms of image processing. …”
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Multiobjective Optimization of Turbine Coolant Collection/Distribution Plenum Based on the Surrogate Model by Junsheng Chai, Zhenyu Wang, Xuanling Zhao, Chunhua Wang

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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…The performance of the proposed model is compared with the Support Vector Machine and Random Decision algorithms and evaluated by four significant performance metrics, namely, sensitivity, specificity, accuracy, and the F-measure. …”
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Wind speed and power forecasting using Bayesian optimized machine learning models in Gabal Al-Zayt, Egypt by Nehal Elshaboury, Haytham Elmousalami

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Fuzzy logic-controlled three-phase dynamic voltage restorer for enhancing voltage stabilization and power quality by Abu Shufian, Sowrov Komar Shib, Durjoy Roy Dipto, Md Tanvir Rahman, Nasif Hannan, Shaikh Anowarul Fattah

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Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional–Integral Controllers by Alejandra Marmol, Elyas Zamiri, Marziye Purraji, Duberney Murillo, Jairo Tuñón Díaz, Aitor Vazquez, Angel de Castro

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Epidemiological and pharmacoeconomic aspects of HIV infection in military by Yu. I. Bulankov, M. A. Bulygin, A. V. Bespalov, K. V. Zhdanov, A. A. Murachev, K. S. Ivanov, Yu. I. Lyashenko

“…The study uses the following methods for the analysis: Markov chains are employed to calculate QALYs (Quality Adjusted Life Years), “cost-effectiveness”, and “willingness to pay”. The research compares two competitive models: “Current Situation” - without the introduction of algorithms for early detection and treatment of HIV infection, and “Prognosis” - with the introduction of these algorithms.The following results are obtained: implementing a mandatory triennial screening for HIV-antibodies among military personnel allows to raise the detection of HIV-positive military personnel in the early stages of the disease by 55%. …”
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Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance by Eslam Abdelhakim Seyam

“…The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. …”
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A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) by Emilda Indrisari, Hidayat Febiansyah, Bambang Adiwinoto

“…This study employed a Systematic Literature Review (SLR) to examine 20 scientific articles published between 2020 and 2024, focusing on the application of ML algorithms in stunting research. Literature was sourced from Scopus and Google Scholar, with inclusion criteria covering studies relevant to the Indonesian context or comparable global settings. …”
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