Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial by Thanh G. Phan, Velandai K. Srikanth, Dominique A. Cadilhac, Mark Nelson, Joosup Kim, Muideen T. Olaiya, Sharyn M. Fitzgerald, Christopher Bladin, Richard Gerraty, Henry Ma, Amanda G. Thrift

“…We determine the optimal machine learning and associated tuning parameters from the following: random forest, extreme gradient boosting, category boosting, support vector regression, multilayer perceptron neural network, and K‐nearest neighbor. …”
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AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness by Anantaya Timtong, Atthaphon Ariyarit, Wanwanut Boongsood, Prasert Aengchuan, Attasit Wiangkham

“…This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…The proposed models can provide educational institutions with a roadmap and insight in evaluating online examination practices and ensuring academic integrity. …”
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A Neutrosophic Approach for Opponent Analysis and Game Strategy Formulation in College Volleyball by Bogang Huang

“…This study introduces a QNeutrosophic Soft Set (QNSS) approach to opponent analysis, enabling multi-context decision evaluation based on a granular assessment of opponent characteristics across different game conditions. …”
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Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach by Mariam Kili Bechir, Ferhat Atasoy

“…We evaluated seven distinct algorithms: Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbors (k-NN), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), Random Forests (RF), and a basic "nonlinear mapping technique". …”
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Quantification of MODIS Land Surface Temperature Downscaled by Machine Learning Algorithms by Qi Su, Xiangchen Meng, Lin Sun, Zhongqiang Guo

“…This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. …”
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Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence by Ahmet Cifci

“…Ten ML models, including Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), Gradient Boosting (GBoost), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were compared for their performance in predicting the grid stability. …”
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ENHANCING NETWORK INTRUSION DETECTION USING MACHINE LEARNING AND META-MODELLING FOR IMPROVED CYBER SECURITY PERFORMANCE by Sunita, Pankaj Verma, Nitika, Jaspreet Kaur, Vijay Rana

“…Data enhancements that were used in the models include data normalization and feature selection in a bid to enhance the accuracy of the model’s predictions. Common parameters such as accuracy, precision, recall, and F1-score were computed on each model to allow for a comparative evaluation. …”
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Construction of a NOx Emission Prediction Model for Hybrid Electric Buses Based on Two-Layer Stacking Ensemble Learning by Jiangyan Qi, Xionghui Zou, Ren He

“…To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine operating parameters, and emission after-treatment device operating parameters are selected as input features for the model. …”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…Evaluations conducted on the VisDrone dataset reveal that the proposed method improves Precision, Recall, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mrow><mn>0.5</mn></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mrow><mn>0.5</mn><mo>:</mo><mn>0.95</mn></mrow></msub></mrow></semantics></math></inline-formula> by 4.4%, 5.6%, 6.4%, and 4%, respectively, compared to YOLOv8s, with a 28.3% reduction in parameters. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Logistic regression (LR), random forest (RF), support vector machine (SVM), and gradient boosting decision tree (GBDT) were constructed, and model performances were evaluated using accuracy, precision, recall, area under curve(AUC) of the receiver operating characteristic curve, and F1-score.Results A total of 856 perimenopausal women were included in the study, of which 557 were in the normal or mild PMS group and 299 were in the moderate to severe PMS group; 599 were in the training set and 257 were in the testing set. 9 features (employment status, exercise, age, menstrual condition, medical history, obesity, residence area, history of health education, household register) were selected as predictors for the final model using the Boruta algorithm and SHAP analysis. …”
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Development and validation of an interpretable risk prediction model for the early classification of thalassemia by Jin-Xin Lai, Jia-Wei Tang, Shan-Shan Gong, Ming-Xiong Qin, Yu-Lu Zhang, Quan-Fa Liang, Li-Yan Li, Zhen Cai, Liang Wang

“…The results demonstrated that the categorical boosting (CatBoost) model exhibited the best discriminative ability in both the training and external validation cohorts. …”
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Design and Development of Gorilla Optimized Deep Resilient Architecture for Prediction of Agro-Climatic Changes to Increase the Crop–Yield Production by Deepa Devarashetti, S. S. Aravinth

“…This research article proposes the ensemble Residual Long Short-Term Memory (R-LSTM) along with Artificial Gorilla Troops Optimized Deep Learning Networks (AGTO-DLN) as a solution for climatic condition prediction to boost crop–yield production. Performance metrics for the proposed model examining precision, F1 score, accuracy, specificity, and recall operate through evaluation using 5,04,647 climatic parameters with various advanced learning techniques. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining by Suhua Zhou, Wenjie Han, Minghua Huang, Zhiwen Xu, Jinfeng Li, Jiuchang Zhang

“…A dataset of 242 slope cases from existing literature is compiled for training and evaluation. The ILB model’s performance is assessed using accuracy, area under the ROC curve (AUC), generalization ability, and computation time and compared to four common batch learning models: Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…Drawing from the test data, a structured database was assembled, and a new learning framework was developed by combining the Logical Development Algorithm (LDA), Adaptive Boosting (BA), and Artificial Neural Networks (ANN). …”
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Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers by Eros Fani, Raffaello Camoriano, Barbara Caputo, Marco Ciccone

“…Prior work in the literature shows that client drift particularly affects the parameters of the classification layer, hindering both convergence and accuracy. …”
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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

“…This study provides a reliable computational tool for seismic slope stability evaluation, contributing to improved risk assessment in geotechnical engineering.…”
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AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy by Divisha Garg, Harpreet Singh, Yosi Shacham-Diamand

“…The key task addressed was the prediction of stress-related parameters using machine learning. A novel boosting-based ensemble method, AdapTree, combining AdaBoost and decision trees, was proposed to improve predictive accuracy and model interpretability. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…To mitigate latency issues, we deploy the model at the edge of the SDN-IoT network, enforcing mitigation rules through the SDN controller. We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. …”
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