How Predictable Is Electric Vehicle Adoption? Exploring the Broader Role of Renewables in Transportation Using a Data-Driven Approach by Simona-Vasilica Oprea, Adela Bara

“…Equally important is the capacity to predict the likelihood of EV adoption. A classification model is proposed, embedding several cutting-edge classifiers. …”
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Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data by Denglin Li, Luxin Zhang, Lifei Xu, Renhe Zhai, Hanyu Gao, Junlan Gao, Minghai Wei, Ningwei Che, Yeting He

“…Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patients with glioblastoma and establish predictive models for the OS and CSS of Asian patients with glioblastoma based on the machine learning algorithms. …”
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PSO-NMPC control strategy based path tracking control of mining LHD (scraper) by Ya Liu, Ping-an Peng, Li-guan Wang, Jia-xi Wu, Ming-yu Lei, Chao-wei Zhang, Ru Lei

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Artificial Intellect Systems in Today’s Economic Environment by N. V. Barinova, V. R. Barinov

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Crude Oil and Hot-Rolled Coil Futures Price Prediction Based on Multi-Dimensional Fusion Feature Enhancement by Yongli Tang, Zhenlun Gao, Ya Li, Zhongqi Cai, Jinxia Yu, Panke Qin

“…In addition, the significance and stability of the model performance were verified by statistical methods such as a paired t-test and ANOVA analysis of variance. This MDFFE algorithm offers a robust and practical approach for predicting commodity futures prices. …”
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A Semi-Supervised-Learning-Aided Explainable Belief Rule-Based Approach to Predict the Energy Consumption of Buildings by Sami Kabir, Mohammad Shahadat Hossain, Karl Andersson

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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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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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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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Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions by Mohamed Rebbouj, Said Lotfi

“…Background and purpose The aim of this study is to incorporte machine learning techniques in physical education activities assessment so we can plan a training session and learning cycle based on predictive analyses using machine learning algorithms. …”
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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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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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Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer by Kohei Isemoto, Yuma Waseda, Motohiro Fujiwara, Koichiro Kimura, Daisuke Hirahara, Tatsunori Saho, Eichi Takaya, Yuki Arita, Thomas C. Kwee, Shohei Fukuda, Hajime Tanaka, Soichiro Yoshida, Yasuhisa Fujii

“…The subtraction of radiological features between CE- and NE-T1WI yielded 112 delta-radiomics features, which were utilized in multiple machine-learning algorithms to construct optimal predictive models for CRT responsiveness. …”
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