Mitigating Algorithmic Bias Through Probability Calibration: A Case Study on Lead Generation Data by Miroslav Nikolić, Danilo Nikolić, Miroslav Stefanović, Sara Koprivica, Darko Stefanović

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Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

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Employee Turnover Prediction Model Based on Feature Selection and Imbalanced Data Handling by Yuan Fang, Zhongqiu Zhang

“…In this study, we utilized the Kaggle HR dataset to predict employee turnover by employing a comprehensive machine learning pipeline. The dataset underwent rigorous preprocessing and exploratory data analysis (EDA) to identify key patterns and relationships. …”
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Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques by Oryza Wisesa, Andi Andriansyah, Osamah Khalaf

“…Sales prediction analysis requires intelligent data mining techniques with accurate prediction models and high reliability. …”
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Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances by V. Indragandhi, R. Senthil Kumar, R. Saranya

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Development of Motion Learning Media and Energy Conservation Law Through Coaster Tracks Based on Logger Pro Analysis by A. Setiyani, W. H. Kristiyanto, F. S. Rondonuwu

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DI4SHE: Deep Learning via Incremental Capacity Analysis for Sodium Battery State-of-Health Estimation by Xikang Wang, Bangyu Zhou, Huan Xu, Song Xu, Tao Wan, Wenjie Sun, Yuanjun Guo, Zuobin Ying, Wenjiao Yao, Zhile Yang

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Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding by Joe Benganga, Tshepo Kukuni, Ben Kotze, Lepekola Lenkoe

“…Furthermore, the results demonstrate higher data losses in the data transfer learning due to data recycling, suggesting that the model is prone to image feature losses when the model threshold is set at 75%. …”
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Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Approach with Integration of Reservoir Mineralogy and Porosity by Papa Owusu, Abdelmoneam Raef, Essam Sharaf

“…Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. …”
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Extraction and Analysis of Influencing Factors of Scientific and Technological Ability Improvement of University Teachers Based on Deep Learning Model by Aiqin Wang, Wei Yan

“…In order to improve the development and construction level of colleges and universities and help teachers improve their scientific and technological abilities, a model based on deep learning model for the extraction and analysis of factors affecting the improvement of college teachers’ scientific and technological abilities was proposed. …”
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Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models by Eden T. Wasehun, Leila Hashemi Beni, Courtney A. Di Vittorio, Christopher M. Zarzar, Kyana R.L. Young

“…Consequently, spatiotemporal maps of Chl-a concentration across the reservoir were generated using the best-performing machine learning models: XGBoost for S2 data and SVR for PS data. …”
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Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

“…IntroductionDiabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.MethodsA novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. …”
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A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature by Trilok Nath Pandey, Vishvajeet Ravalekar, Sidharth D. Nair, Sunil Kumar Pradhan

“…Abstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. …”
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Deep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers by Gonçalo Jesus, Zahra Mardani, Elsa Alves, Anabela Oliveira

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Leveraging federated learning for privacy-preserving analysis of multi-institutional electronic health records in rare disease research by Karthik Meduri, Geeta Sandeep Nadella, Akhila Reddy Yadulla, Vinay Kumar Kasula, Mohan Harish Maturi, Steven Brown, Snehal Satish, Hari Gonaygunta

“…This research announces that the fresh federated learning structure is designed to enhance the privacy-preserving analysis of electronic health records (EHRs), and multiple institutions in this framework permit secure collaboration among institutions, allowing them to train machine-learning replicas without directly sharing patient data. …”
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AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change by Hasib Khan, Wafa F. Alfwzan, Rabia Latif, Jehad Alzabut, Rajermani Thinakaran

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Nonlinear Resilient Learning Method Based on Joint Time-Frequency Image Analysis in Underwater Visible Light Communication by Hui Chen, Yiheng Zhao, Fangchen Hu, Nan Chi

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Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

“…Using biological context for interpreting the data resulted in improved model performance and made tailored analysis possible by reducing the dimensionality and increasing signal-to-noise ratio as well as only preserving biologically relevant features in each category. …”
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Integrating robust feature selection with deep learning for ultra-high-dimensional survival analysis in renal cell carcinoma by Shaymaa Mohammed Ahmed, Majid Khan Majahar Ali, Raja Aqib Shamim

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Comparative analysis of daily global solar radiation prediction using deep learning models inputted with stochastic variables by Amit Kumar Yadav, Raj Kumar, Meizi Wang, Gusztáv Fekete, Tej Singh

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