Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…This study aims to identify the most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies.Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies.Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. …”
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An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer by Mohamed Abdel-Aty, Tarek Hasan, B M Tazbiul Hassan Anik

“…Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. …”
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Flood extent mapping in SAR images using semi-supervised approach by Girisha S, Savitha G, Sughosh P

“…An attention module-based tailored student model is developed to precisely identify flood-zones. …”
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Anchor Dragging Risk Estimation Strategy from Supervised Cost-Sensitive Learning by Sang-Lok Yoo, Shem Otoi Onyango, Joo-Sung Kim, Kwang-Il Kim

“…This study also demonstrated potential applications of the model, discussed its limitations, and suggested possible improvements for the ML approach. …”
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Machine Learning-Driven Optimization of Transport Layers in MAPbI₃ Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…In this research work, among those eight ML models, the XGBoost algorithm shows high accuracy for predicting the power conversion efficiency (PCE) of the cell, achieving root mean square error (RMSE) of 0.052 and a coefficient of determination (R2) of 0.999. …”
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Global and Local Interpretable Machine Learning Allow Early Prediction of Unscheduled Hospital Readmission by Rafael Ruiz de San Martín, Catalina Morales-Hernández, Carmen Barberá, Carlos Martínez-Cortés, Antonio Jesús Banegas-Luna, Francisco José Segura-Méndez, Horacio Pérez-Sánchez, Isabel Morales-Moreno, Juan José Hernández-Morante

“…Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive models using interpretable machine learning techniques to early identify individual unscheduled hospital readmissions. …”
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Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children. by Megan Palmer, James A Seddon, Marieke M van der Zalm, Anneke C Hesseling, Pierre Goussard, H Simon Schaaf, Julie Morrison, Bram van Ginneken, Jaime Melendez, Elisabetta Walters, Keelin Murphy

“…We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. …”
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SCoralDet: Efficient real-time underwater soft coral detection with YOLO by Zhaoxuan Lu, Lyuchao Liao, Xingang Xie, Hui Yuan

“…However, underwater coral detection presents unique challenges, including low image contrast, complex coral structures, and dense coral growth, which limit the effectiveness of general object detection algorithms. To address these challenges, we propose SCoralDet, a soft coral detection model based on the YOLO architecture. …”
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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. …”
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A Study on Staging Cystic Echinococcosis Using Machine Learning Methods by Tuvshinsaikhan Tegshee, Temuulen Dorjsuren, Sungju Lee, Dolgorsuren Batjargal

“…This research underscores the potential of hybrid and pre-trained models in advancing medical image classification, providing a promising approach to improving the differential diagnosis of CE disease.…”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. Explainability techniques such as SHAP, LIME, and Grad-CAM approaches are employed to present a complete comprehension of feature significance and local or global predictive behavior of the model over various malware categories. …”
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COSMIC-Based Early Software Size Estimation Using Deep Learning and Domain-Specific BERT by Yohannes Sefane Molla, Esubalew Alemneh, Samuel Temesgen Yimer

“…On average, RE-BERT-based regressor models provided 1.23% to 3.19% improvement over BASE-BERT regressor models. …”
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Study on the mechanisms associating community outdoor public spaces with elderly behavior by Lei Wang, Wenqi He, Shan Wu, Bo Zhang, Xiaorui Zhang, Hu Yin

“…A computer vision-based behavior detection algorithm is then constructed to effectively identify six typical activities of the elderly: exercising, jogging, sitting, standing, walking, and playing chess or cards. …”
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A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys by Lokesh Kumar, Ashish Goyal, Vimal Kumar Pathak, Abhijit Bhowmik

“…These innovations are supported by suitable modeling and metaheuristic techniques, including adaptive neuro-fuzzy inference systems (ANFIS), simulated annealing, teaching-learning-based optimization (TLBO), the finite element method (FEM), and the non-dominated sorting genetic algorithm II (NSGA-II). …”
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Optimizing Rural Connectivity Through Hybrid Wireless-Wireline and Satellite Network Integration by Fareha Nizam, Teong Chee Chuah, Ying Loong Lee

“…This dual-access approach not only improves system robustness but also ensures efficient resource utilization. …”
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Research on target localization and adaptive scrubbing of intelligent bathing assistance system by Ping Li, Ping Li, Shikai Feng, Hongliu Yu

“…The depth correction algorithm is designed to improve the depth accuracy of RGB-D vision sensors. …”
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Legal Regulation of Relations in the Sphere of Crowdfunding in the Republic of Belarus by A. V. Vasilevskii

“…The probable contract models of the parties’ legal relations registering were named. …”
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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

“…The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.…”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

“…Therefore, the aim of this study was to construct a machine learning model for early identification of people at high risk of postoperative TIC. …”
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Artificial Intelligence Systems for Solving Problems of Agro­Industrial Complex Digitalization and Robotization by A. L. Ronzhin, A. I. Savel'ev

“…The digital transformation of agricultural technological processes is substantiated to be relevant in the context of the exacerbating global problems of food security, the agricultural business stagnation in the regions of the northern Non-Black Earth Region, as well as the ESG transformation of the economy.Research purpose To develop control algorithms for unmanned aerial vehicles (UAVs) based on numerical methods of machine learning to ensure the monitoring of the crops state and the improving of the production process planning and operational management.Materials and methods The following methodology was used: the original methods of machine learning, knowledge engineering and computer modeling for organizational and technological processes of technical objects’ life cycle in industry and products’ life cycle in the national economy,  as well as mathematical and algorithmic models, methods and prototypes of proactive automation tools for information, physical and energy interaction of heterogeneous robotic and cyber-physical complexes.Results and discussion Artificial intelligence systems were created for the photogrammetric processing of visible spectrum images and those taken with multispectral video cameras with the construction of orthophotomaps, digital elevation models. …”
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