Cost-effectiveness of different tuberculosis diagnostic approaches in Nigeria based on decision analytical modelling by Muktar H Aliyu, Hafsat U Ibrahim, Rayyan M Garba, Jameel I Ahmad, Kabiru Abdulsalam, Yasir N Jibril, Faisal S Dankishiya, Abdullahi Ibrahim, Ibrahim U Garzali, Aisha A Galadanci, Abdullahi K Suleiman, Abdulgafar L Olawumi, Umar F Abdullahi, Fatima M Mahmud, Amina A Umar, Olive Asafu- Adjaye, Lukman F Owolabi, Baba M Musa

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Performance Evaluating of Honeyword Generation Methods: Traditional versus AI by Shahad Abdulkhalik Alkazzaz, Saja Jasim Mohammed

“…The results illustrate a contrast in the performance of different techniques and intelligence algorithms based on the Hamming distance value.…”
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Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images by R. J. Hemalatha, V. Vijaybaskar, T. R. Thamizhvani

“…Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. …”
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The Evaluation of Music Teaching in Colleges and Universities Based on Machine Learning by Xia Xiongjun, Danmeng Lv

“…This article uses decision tree algorithms, support vector machines, Bayesian theory, and random forest four different classification techniques to evaluate the student curriculum evaluation dataset. …”
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Nature–Inspired Metaheuristic Optimization for Control Tuning of Complex Systems by Jesús Garicano-Mena, Matilde Santos

“…Two bio-inspired meta-heuristic optimization algorithms –the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)– have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine–Generator–Rectifier, as an example of a complex non-linear dynamic system. …”
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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Unlike prior studies relying on statistical models or single-algorithm methods, this research assesses multiple ML models to predict discharge readiness, comparing them against staff evaluations and the Aldrete checklist. …”
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Methodology of evaluation and correction of geometric data topology in QGIS software by Anita Kukulska, Tomasz Salata, Katarzyna Cegielska, Marta Szylar

“…Elaborated algorithms are aimed at acceleration of data processing to allow their usage during that process. …”
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Protocol for detecting neuronal subnetworks from in vivo calcium imaging data using multiple clustering algorithms in MATLAB by Jui-Yen Huang, Gautam Chauhan, Pei-Ying Chen, Esen Tuna, Hui-Chen Lu

“…We provide a step-by-step workflow to detect subnetworks from in vivo calcium imaging data using multiple clustering algorithms in MATLAB. We apply statistical modeling in R to evaluate group-level differences, followed by model selection and bootstrapping for robust estimation. …”
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Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

“…In this study, we examine how the accuracy of college student success predictions differs between racialized groups, signaling algorithmic bias. …”
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Multi-factor evaluation of clustering methods for e-commerce application by Adam Wasilewski, Krzysztof Juszczyszyn, Vera Suryani

“…The results showed consistent outcomes from both analyzed Multi-Criteria Decision Methods, with some notable differences. The methods obtained the same ranking of the top three clustering algorithms (K-median - BIRCH - K-means). …”
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A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection by N. K. Sreeja

“…PMC+ is evaluated on 44 binary imbalanced datasets and 15 multiclass imbalanced datasets. …”
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Comparative performance of PROMIS Sleep Disturbance computerized adaptive testing algorithms and static short form in postmenopausal women by Andrew Trigg, Claudia Haberland, Huda Shalhoub, Christoph Gerlinger, Christian Seitz

“…A graded response model (GRM) was fitted for the item bank, then simulations evaluated the performance of CAT algorithms and the short form, in terms of root mean square error (RMSE) versus the latent trait estimate derived from the full bank. …”
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EVALUATION OF ELECTROMYOGRAPHIC NOISE STATISTICAL CHARACTERISTICS IN MULTICHANNEL ECG RECORDINGS by Evgene B. Grigoriev, Alexander S. Krasichkov, Evgeny M. Nifontov

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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations by Yong Li, Xin Sun, Yongtao Qu, Shuling Yang, Yueyi Zhai, Yan Qu

“…Chi-square and Mann-Whitney U tests examined variables, and multivariate logistic regression determined ONIHL risk factors. Machine learning algorithms like Logistic Regression and Random Forest were developed, optimized, and evaluated. …”
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Evaluation of functional disorders in tuberculosis patients with the severe course of the disease by T. V. Pyanzova, I. A. Vasilyeva, Yu. T. Dzhangildin

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COMPARATIVE EVALUATION OF INDICATORS OF STRUCTURAL AND MECHANICAL PROPERTIES OF WHEAT GRAIN by P. V. Medvedev, V. A. Fedotov, E. S. Lukyanova

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DYNAMICS OF URBAN LANDSCAPE AND ITS THERMAL INTERACTIONS WITH SELECTED LAND COVER TYPES: A CASE OF BENIN CITY, NIGERIA by Oseyomon John AIGBOKHAN, Oludare Hakeem ADEDEJI, Abiodun Olusegun OLADOYE, John Adebayo OYEDEPO

“…The maximum likelihood algorithm for land use and land cover (LULC) analysis was used to categorise Landsat images. …”
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Artificial Bee Colony Algorithm Based on the Division Between Exploration and Exploitation and Its Application in Esophageal Cancer Prediction by WANG Yingcong, YAN Jun, SUN Junwei, WANG Yanfeng

“…The Wilcoxon test results indicated that there was no significant difference between ABC_DEE and GABC, but ABC_DEE significantly outperformed the other algorithms. …”
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Analysis of Smart Grid Optimal Scheduling considering the Demand Response of Different EV Owners by Xiaohua Zhang, Chongyang Liao, Xingrui Chen, Kuiheng Deng, Bolin Chen, Yibo Gong

“…This paper proposes a classified scheduling method for different types of EV owners. According to charging and vehicle-to-grid (V2G) data of EV owners, the K-means clustering algorithm (K-means) is used to classify EV owners, and the demand response (DR) model is established based on the classification results. …”
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