Anemia Classification System Using Machine Learning by Jorge Gómez Gómez, Camilo Parra Urueta, Daniel Salas Álvarez, Velssy Hernández Riaño, Gustavo Ramirez-Gonzalez

“…We built a supervised learning approach and trained three models (Linear Discriminant Analysis, Decision Trees, and Random Forest) using an anemia dataset from a previous study by Sabatini in 2022. …”
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Machine learning predictive performance in road accident severity: A case study from Thailand by Ittirit Mohamad, Sajjakaj JomnonKwao, Vatanavongs Ratanavaraha

“…Among the models tested, Random Forest demonstrated superior performance in the binary classification task, achieving an average class AUC of 0.768, classification accuracy of 0.777, precision of 0.752, and recall of 0.777. …”
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Сorrective phase in the approximation of space-time analysis with accounting interference in collisions of heavy ions by S. O. Omelchenko, V. S. Olkhovsky

“…The aim of the work is to expand the approximation of the space-time analysis, which was previously used to describe binary elastic nucleon scattering reactions on nuclei and light ion collisions, to consider coherent effects in heavy ion collisions with three particles in the final reaction channel, two of which are detected. …”
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DAugSindhi: a data augmentation approach for enhancing Sindhi language text classification by Raja Vavekanand, Bhagwan Das, Teerath Kumar

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A Unified Deep Learning Ensemble Framework for Voice-Based Parkinson’s Disease Detection and Motor Severity Prediction by Madjda Khedimi, Tao Zhang, Chaima Dehmani, Xin Zhao, Yanzhang Geng

“…To enhance prediction performance, ensemble learning strategies were applied by stacking outputs from the fusion model with tree-based regressors (Random Forest, Gradient Boosting, and XGBoost), using diverse meta-learners including XGBoost, Ridge Regression, and a deep neural network. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Hybrid Machine Learning Model for Hurricane Power Outage Estimation from Satellite Night Light Data by Laiyin Zhu, Steven M. Quiring

“…In general, the classification and regression tree-based machine learning models (XGBoost and random forest) demonstrated better performance than the logistic and CNN models in both binary classification and regression models. …”
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Introducing Primality Testing Algorithm with an Implementation on 64 bits RSA Encryption Using Verilog by Rehan Shams, Fozia Hanif Khan, Umair Jillani, M. Umair

“…The RSA algorithm has three parts i.e. key generation, encryption and decryption. …”
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Bias and Linking Error in Fixed Item Parameter Calibration by Alexander Robitzsch

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Data augmentation via diffusion model to enhance AI fairness by Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon, Xishuang Dong

“…Five traditional machine learning models—Decision Tree (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)—were used to validate the proposed approach.Results and discussionExperimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification.…”
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Mental Workload Detection and Assessment Through Statistical Features Extraction and Optimization Using GEL-RF Method for EEG Signals Using N-Back Dataset by Waleed Manzoor, Noman Naseer, Imran Fareed Nizami, Syed Hammad Nazeer, Husam A. Neamah

“…We propose a novel model, which integrates genetic, evolutionary and linear (GEL) feature selection techniques with a Random Forest (RF) classifier as GEL-RF model. Experimental results demonstrate that the GEL-RF model achieves classification accuracies of 97% for binary tasks and 96.3% for multiclass tasks MW assessment, outperforming existing methods. …”
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Trade-offs between machine learning and deep learning for mental illness detection on social media by Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang, Yuchen Cao, Yunchong Liu, Xiaorui Shen, Yexin Tian, Jianglai Dai

“…This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside DL architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The extra trees classifier (ET) performed exceptionally, achieving 97.23% accuracy on the multi‐class dataset and 97.45% on the binary dataset.…”
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Completeness of reporting of simulation studies on responder analysis methods and simulation performance: a methodological survey by Gordon H Guyatt, Kehu Yang, Derek K Chu, Romina Brignardello-Petersen, Xiajing Chu, Junjie Ren, Thabane Lehana

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Sex/gender in the association between ambient air pollution and cardiovascular mortality: Systematic review and meta-analysis by Ute Kraus, Sophie Horstmann, Lisa Dandolo, Gabriele Bolte, Annette Peters, Alexandra Schneider

“…We further evaluated whether sex/gender was a source of heterogeneity within these through a moderator analysis using random effects models. We examined sex/gender differences through random effects pooling of the female-to-male-ratio (FMR) of each study. …”
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Mechanical and optical effects of elastomer interaction in polypropylene modification: Ethylene-propylene rubber, poly-(ethylene-co-octene) and styrene-butadiene elastomers by M. Gahleitner, C. Grein, K. Bernreitner

“…The interaction between binary combinations of three different elastomer classes commonly applied in impact modification of isotactic polypropylene (iPP) was studied. …”
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Image Classification Models as a Balancer Between Product Typicality and Novelty by Hung-Hsiang Wang, Hsueh-Kuan Chen

“…The accuracy of the model of rear views trained with BinaryPatternsPyramid-Filter and random forest classifier was 80.5%, and the test accuracy was 90%. …”
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Association between functional disability and depressive symptoms among older adults in rural China: a cross-sectional study by Hong Ding, Jian Rong, Xueqin Wang, Yanhong Ge, Guimei Chen

“…Data were analysed using SPSS statistics V.25.0 program with χ2 test, Mann-Whitney U test, binary logistic regression analysis and classification and regression tree (CART) model.Results The prevalence of depressive symptoms in 3336 interviewed older people was 52.94%. …”
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Weed Types and Dynamics Associations with Catena Landscape Positions: Smallholder Farmers’ Knowledge and Perception in Zimbabwe by Justin Chipomho, Simbarashe Tatsvarei, Cosmas Parwada, Arnold Bray Mashingaidze, Joyful T. Rugare, Stanford Mabasa, Regis Chikowo

“…Factors that predicted the spatial distribution of weeds were determined using a binary logistic model. From the survey, 52.8% and 42.3% of farms are on the upper catena and lower catena, respectively, and only 4.8% are on the middle catena. …”
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Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement by Joko Siswantoro, Anton Satria Prabuwono, Azizi Abdullah, Bahari Idrus

“…Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. …”
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