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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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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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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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

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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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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

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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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Identification of Risk Group for Root Caries and Analysis of Associated Factors in Older Adults Using Unsupervised Machine Learning Clustering by Jiang L, Huang S, Reissmann DR, Schmalz G, Li J

“…The confusion matrix and alluvial diagram were used to visualize the predictive accuracy and composition of the clustering results. Binary logistic regression models further analyzed the associated factors, while ROC (receiver operating characteristic) curves and the random forest model visualized the predictive performance and the most important associated factors.Results: Two clusters were identified: cluster 1, with low root caries risk (21.5% with and 78.5% without root caries), and cluster 2, with high root caries risk (83.7% with and 16.3% without root caries). …”
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A Building-Block Favoring Method for the Topology Optimization of Internal Antenna Design by Yen-Sheng Chen

“…However, the defining length of the resultant building blocks is so large that the problem difficulty arises; furthermore, the order of the building blocks becomes extremely high, so genetic algorithms (GAs) and binary particle swarm optimization (BPSO) are not more efficient than the random search algorithm. …”
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Health Seeking Behaviour of Young Adults in Accra, Ghana by Sydney Stanley Blankson

“…However, despite generally positive self-assessments, 48.5% of respondents reported being ill in the past three months, with about 50% diagnosing themselves. …”
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Optimised autoencoder-based ensemble deep learning approaches for cyber-physical event classification utilizing synchrophasor PMU data by Dhinu Lal M, Ramesh Varadarajan

“…The proposed methodology incorporates Adaptive Synthetic Sampling (ADASYN), Random Oversampling (ROS), and Synthetic Minority Oversampling Technique (SMOTE), followed by the selection of features by three variants of the Grey Wolf Optimiser (GWO): Standard GWO, Adaptive Weight GWO (AW-GWO), and Binary GWO (B-GWO), with B-GWO providing the optimal balance among accuracy and computing efficiency. …”
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Predicting Block Delays in Bitcoin Blockchain via Bayesian and Non-Bayesian Logistic Regression by Phumzile T. Mgcima, Wilson Tsakane Mongwe, Peter O. Olukanmi

“…The PR AUC values showed that the fitted models performed better than the random classifier, with the Gibbs sampler model performing the best among the three. …”
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The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance by Taifei Zhao, Hua Wang, Qianwen Ma

“…The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.…”
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Recognition model for major depressive disorder in Arabic user-generated content by Esraa M. Rabie, Atef F. Hashem, Fahad Kamal Alsheref

“…Results In binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” …”
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Towards arbitrary QUBO optimization: analysis of classical and quantum-activated feedforward neural networks by Chia-Tso Lai, Carsten Blank, Peter Schmelcher, Rick Mukherjee

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