Bridging the Gap Between Machine Learning and Medicine: A Critical Evaluation of the Dworak Regression Grade in Rectal Cancer by Camille Raets, Chaimae El Aisati, Amir L. Rifi, Mark De Ridder, Koen Putman, Johan De Mey, Alexandra Sermeus, Kurt Barbe

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Enhanced clash detection in building information modeling: Leveraging modified extreme gradient boosting for predictive analytics by Ali Shehadeh, Odey Alshboul, Madhar M. Taamneh, Aiman Q. Jaradat, Ahmad H. Alomari

“…The result justifies the model's performance and identifies its potential to streamline predictive analytics in BIM concerning clash detection. …”
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Predictability of Lifetime Nonsuicidal Self-injury by Symptoms of Sleep Disorders Using a Neural Network Model by Shakiba Rezaei, Azita Chehri, Saeede Sadat Hosseini, Mokhtar Arefi, Hassan Amiri

“…Conclusion: The neural network algorithm can be effectively employed in predicting NSSI by several sleep disorders. …”
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Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance by Johannes C. Ayena, Johannes C. Ayena, Amina Bouayed, Amina Bouayed, Myriam Ben Arous, Myriam Ben Arous, Youssef Ouakrim, Youssef Ouakrim, Karim Loulou, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani, Neila Mezghani

“…Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.ResultsWearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. …”
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Energy Loss Reduction for Distribution Electric Power Systems with Renewable Power Sources, Reactive Power Compensators, and Electric Vehicle Charge Stations by Le Chi Kien, Tran Duc Loi, Minh Phuc Duong, Thang Trung Nguyen

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Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features by Xue Du, Xue Du, Chunbao Chen, Lu Yang, Yu Cui, Min Li

“…ObjectiveTo investigate the value of preoperative prediction of risk factors for recurrence of operable cervical cancer based on the radiomics features of biparametric magnetic resonance imaging (bp-MRI) combined with clinical features.MethodA retrospective collection of cervical cancer cases undergoing radical hysterectomy + pelvic and/or para-aortic lymph node dissection at the Affiliated Hospital of North Sichuan Medical College was conducted. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…The wrapping method was identified as the most effective feature selection algorithm. Based on field conditions, 8 optimal features were selected for prediction. …”
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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Unfortunately, all the previous research conducted without effective feature reduction suffers from high-dimensional data, leading to unsatisfactory prediction performance measures. …”
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Prediction and Verification of Stability Domain in the Milling Process of Titanium Alloy Thin walled Workpiece by YUE Caixu, XIE Na, LI Xiaochen, LI Hengshuai, LIU Zhibo

“…During milling, chatter will lead to the increase of cutting force amplitude and of cutting force fluctuation, leading to the reduction of workpiece surface quality. In order to solve this problem, firstly, according to the flutter stability analysis algorithm, a dynamic milling system for titanium alloy Ti6Al4V thinwalled parts was established to obtain the stability lobe diagram. …”
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Predicting adolescent psychopathology from early life factors: A machine learning tutorial by Faizaan Siddique, Brian K. Lee

“…The best performing elastic net models included prenatal and family history factors (Sensitivity 0.654, Specificity 0.713; AUC 0.742, F1-score 0.401) and prenatal, family, history, and sociodemographic factors (Sensitivity 0.668, Specificity 0.704; AUC 0.745, F1-score 0.402). Across all 5 ML algorithms, family history factors (e.g., either parent had nervous breakdowns, trouble holding jobs/fights/police encounters, and counseling for mental issues) and sociodemographic covariates (e.g., maternal age, child's sex, caregiver income and caregiver education) tended to be better predictors of adolescent psychopathology. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…These findings highlight the considerable potential of machine learning algorithms in predicting mixing ellipses and parameterizing eddy mixing processes within climate models.…”
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Digital intelligence and synergy of pollution reduction and carbon reduction: “Dividend” or “gap”? by Jiajia Li, Xianfeng Han, Tonglei Zhang, Jian Xiao, Longtao Chen

“…At the intersection of the “dual carbon” goal and the era of digital intelligence (DI), exploring the synergy between pollution and carbon reduction (SPCR) within the context of DI is important for promoting a comprehensive green transformation of economic and social development. …”
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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

“…Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. …”
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Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. …”
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Optimized Model Predictive Control-Based Path Planning for Multiple Wheeled Mobile Robots in Uncertain Environments by Yang She, Chao Song, Zetian Sun, Bo Li

“…Addressing the path planning problem for multiple wheeled mobile robots (WMRs) in uncertain environments, this paper proposes a multi-WMR path planning algorithm based on the fusion of artificial potential field and model predictive control. …”
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Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot by Qihong Chen, Rong Long, Shuhai Quan, Liyan Zhang

“…Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. …”
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Energy Management Strategy for Hybrid Electric Vehicles Based on Adaptive Equivalent Ratio-Model Predictive Control by Farah Mahdi Ali, Nizar Hadi Abbas

“…This paper develops an optimal energy management algorithm based on Model Predictive Control that can produce optimal control parameters for power distribution between the battery unit and generator. …”
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