Retracted: Improving Students’ Learning Motivation and Attitude in Physical Education by Using Algorithm Thinking Innovation Teaching Method by International Transactions on Electrical Energy Systems

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Building a Computer Vocational Guidance System for Graduates of Secondary Educational Institutions Based on a Genetic Algorithm by A. P. Sergushicheva, E. N. Davydova

“…The purpose of the article is to present the results of a study on the development of a genetic algorithm to solve the problems of career guidance for graduates of secondary educational institutions and to verify the possibility of its implementation in a computer system. …”
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Retracted: Obstacles and Regulation Deconstruction of Athletes’ Psychological Training Based on Machine Learning Algorithm in Physical Education Teaching by International Transactions on Electrical Energy Systems

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Critical dimensions in strengthening education and instructor training using fuzzy based decision algorithm and CRITIC WASPAS method by Hui Zhou, RenGuo Yang

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Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application by Eko Abdul Goffar, Rosa Eliviani, Lili Ayu Wulandhari

“…The literature review section discusses the factors that influence stunting, and these factors are used as features to build a stunting prediction model. Then the features were used to build a model with three machine learning algorithms Extreme Gradient Boosting (XGBoost), Random Forest, and K-Nearest Neighbor (KNN) to build and evaluate models that predict stunting. …”
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A Fused Multi-Channel Prediction Model of Pressure Injury for Adult Hospitalized Patients—The “EADB” Model by Eba’a Dasan Barghouthi, Amani Yousef Owda, Majdi Owda, Mohammad Asia

“…This study aims to construct a novel fused multi-channel prediction model of PIs in adult hospitalized patients using machine learning algorithms (MLAs). …”
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Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems by Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Satyesh Shanker Awasthi, Michael Khayyat, Stefano Arrigoni, Francesco Braghin

“…The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). …”
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Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches by Farveh Vahidpour, Hossein Sabouri, Fakhtak Taliei, Sayed Javad Sajadi, Saeed Yarahmadi, Hossein Hosseini Moghaddam

“…Abstract By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). …”
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A Systematic Model of an Adaptive Teaching, Learning and Assessment Environment Designed Using Genetic Algorithms by Doru Anastasiu Popescu, Nicolae Bold, Michail Stefanidakis

Subjects: “…educational assessment…”
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Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery by Catherine Chiu, Matthias R. Braehler, Anne L. Donovan, Atul J. Butte, Romain Pirracchio, Andrew M. Bishara

“…A UIA was defined as any post-operative patient recovering in the post-anesthesia care unit (PACU) requiring direct transfer to the intensive care unit (ICU) for higher level of care. We developed our prediction model with a gradient-boosting decision tree algorithm (XGBoost). …”
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A Novel Method for a Pursuit–Evasion Game Based on Fuzzy Q-Learning and Model-Predictive Control by Penglin Hu, Chunhui Zhao, Quan Pan

“…This paper explores a pursuit–evasion game (PEG) based on quadrotors by combining fuzzy Q-learning (FQL) and model-predictive control (MPC) algorithms. Initially, the FQL algorithm is employed to perceive, make decisions, and predict the trajectory of the evader. …”
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Predicting the outcome of psychological treatments for borderline personality disorder and posttraumatic stress disorder: a machine learning approach to predict long-term outcome o... by Jakob Blaß, Benjamin Iffland, Philipp Herzog, Tim Kaiser, Thomas Elbert, Carolin Steuwe

“…A cross-validated genetic algorithm was used to detect baseline predictors of change in PTSD symptoms.Results: In the NET group higher education, more baseline PTSD symptoms, more traumatic experiences, fewer baseline BPD symptoms, and not taking antipsychotic medication predicted better treatment outcome. …”
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Predictive Analytics in Finance Using the Arima Model. Application for Bucharest Stock Exchange Financial Companies Closing Prices by Spulbar Cristi, Ene Cezar Cătălin

“…Moreover, the selected model is part of the time series analysis under prediction algorithms, the purpose of the research being to predict the prices of the selected shares. …”
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Cooperative Graph-Based Predictive Collision Avoidance (CGPCA): A Decentralized Framework for Safe Drone Traffic Management by Fariborz Rasoulie

“…Unlike traditional methods that rely on fixed geometric rules or purely reactive algorithms, CGPCA leverages predictive learning. A GNN, trained on a wide range of simulated traffic scenarios, processes the traffic graph to forecast near-future positions and assign collision risk scores. …”
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Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis by Negin Sanaei, Mohamad Zamani-Ahmadmahmudi, Seyed Mahdi Nassiri

“…In this study, we sought to develop models to predict clinical outcome and recovery time in dogs with CPV infection using 10 and 4 machine learning algorithms, respectively. …”
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Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…The model dynamically allocates weights to top-performing algorithms based on their 10-fold cross-validated RMSE, ensuring robust and adaptive prediction performance.Five models—Linear Regression, Ridge Regression, Decision Tree, Random Forest, and Gradient Boosting—were evaluated over ten folds. …”
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Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications by Xiaoying Liao, Chunhua Li, Qunyan Liu, Wang Xia, Zhenglin Liu, Jiamao Zhu, Wei Hu, Qionghua Hong

“…BackgroundEnteral Nutrition-Associated Diarrhea (ENAD) is a common complication in critically ill patients, significantly impacting clinical outcomes. Accurately predicting the risk of ENAD is crucial for early intervention and improving patient care.ObjectiveThis study aims to develop and validate a machine learning (ML)-based risk prediction model for Enteral Nutrition-Associated Diarrhea (ENAD) in ICU patients, and explore its application in nursing practice.MethodThis study was conducted from January 2023 to October 2024 in the Comprehensive Intensive Care Unit (ICU) of a tertiary hospital in China, retrospectively analyzing data from ICU patients receiving enteral nutrition. …”
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Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights by Sina Sarfarazi, Rabee Shamass, Federico Guarracino, Ida Mascolo, Mariano Modano

“…The Extra Trees Regression algorithm demonstrated the highest predictive performance, achieving R² = 0.987, mean absolute error (MAE) = 3.575 kN, and root mean square error (RMSE) = 6.464 kN for the entire dataset. …”
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Development of a Predictive Model for Estimating Stocks of Medicinal Plants Using GIS Tools on the Example of the Middle Urals by A. Yu. Turyshev, V. D. Belonogova, V. G. Luzhanin

“…A geospatial analysis of the distribution of medicinal plant populations by soil types within the regions of the Middle Urals was carried out. An algorithm for constructing predictive models of the distribution of populations of wild medicinal plants of the Middle Urals has been worked out. …”
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Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study by Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li

“…ML models were constructed to evaluate the predictive value of maternal parameter changes on preeclampsia combined with FGR. …”
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