Genetic association between celiac disease and chronic kidney disease: a two-sample Mendelian randomization study by Zhimin Chen, Zigui Zheng, Bingjing Jiang, Yanfang Xu

“…We employed four widely recognized causal inference algorithms: MR-Egger, inverse variance weighted (IVW), weighted median, and weighted mode. …”
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Modeling the variables that influence substance consumption of people who experience homelessness in Colombia by Leandro González Támara, Sandra Patricia Barragán Moreno

“…A two-stage quantitative methodology was applied: (1) descriptive analysis of the demographic and socioeconomic characteristics of the homeless population, and (2) predictive modeling using random forest algorithms to identify key variables associated with substance use. …”
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Architectural Exploration and Performance Enhancement of the CVA6 RISC-V Core Using the Gem5 Simulation Framework by Umer Shahid, Muhammad Tahir, Bilal Zafar, Ayesha Ahmad, Shanzay Wasim, Bisal Saeed

“…To further enhance the architecture, we integrated machine learning algorithms to optimize performance and resource efficiency. …”
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A Web-Based Interface That Leverages Machine Learning to Assess an Individual’s Vulnerability to Brain Stroke by Divyansh Bhandari, Arnav Agarwal, R. Reena Roy, Rajaram Priyatharshini, Rodriguez Rivero Cristian

“…We compare a range of algorithms-including traditional classifiers and deep learning models-and report comprehensive performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) for each. …”
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The growth of computer-assisted (robotic) surgery in urology 2000–2014: The role of Asian surgeons by Deepansh Dalela, Rajesh Ahlawat, Akshay Sood, Wooju Jeong, Mahendra Bhandari, Mani Menon

“…Methods: PubMed/Medline and Scopus databases were searched for publications from 2000 through June 2014, using algorithms based on keywords “robotic surgery”, ”prostate”, “kidney”, “adrenal”, “bladder”, “reconstruction”, and “kidney transplant”. …”
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Deep‐HH: A deep learning‐based high school student hidden hunger risk prediction system by Yang Yang, Zheng Zhang, Huake Cao, Yuchen Zhang, Minao Wang, Ning Zhang

“…After quality control, we designated 632 students from Xuancheng City as the external test cohort and used the remaining 6477 students as the training cohort to develop predictive models. We used six ML algorithms (i.e., deep‐learning neural network [DNN], random forest, support vector machine, extreme gradient boosting, gradient boosting decision tree, and k‐nearest neighbor) to fit the training set using five‐fold cross‐validation, with hyperparameter tuning performed via Bayesian optimization. …”
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Vision-Based Navigation and Perception for Autonomous Robots: Sensors, SLAM, Control Strategies, and Cross-Domain Applications—A Review by Eder A. Rodríguez-Martínez, Wendy Flores-Fuentes, Farouk Achakir, Oleg Sergiyenko, Fabian N. Murrieta-Rico

“…Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. …”
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Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints by Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris

“…We introduce a general mathematical framework for the vectorisation of structural fingerprints via a formal operation called substructure pooling that encompasses hash-based folding, algorithmic substructure selection, and a wide variety of other potential techniques. …”
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Key gene screening and diagnostic model establishment for acute type a aortic dissection by Yue Pan, Zhiming Yu, Xiaoyu Qian, Xuesong Zhang, Qun Xue, Weizhang Xiao

“…Recent studies highlight the role of immune dysregulation, vascular smooth muscle cell (VSMC) apoptosis, and metabolic-epigenetic interactions in AD pathogenesis, underscoring the need for novel biomarkers and therapeutic targets.ObjectiveThis study aims to identify critical genes and molecular pathways associated with ATAAD, develop a multi-omics diagnostic model, and evaluate potential therapeutic interventions to improve clinical outcomes.MethodsTranscriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”
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Prediction model for the selection of patients with glioma to proton therapy by Jesper Folsted Kallehauge, Siri Grondahl, Camilla Skinnerup Byskov, Morten Høyer, Slavka Lukacova

“…Prediction models were built using logistic regression algorithms and support vector machines (SVMs) and evaluated using the area under the precision-recall curve (AUC-PR). …”
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Model Development for Estimating Sub-Daily Urban Air Temperature Patterns in China Using Land Surface Temperature and Auxiliary Data from 2013 to 2023 by Yuchen Guo, János Unger, Tamás Gál

“…In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. …”
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Real-Time Polarimetric Imaging and Enhanced Deep Learning Model for Automated Defect Detection of Specular Additive Manufacturing Surfaces by Dingkang Li, Xing Peng, Hongbing Cao, Yuanpeng Xie, Shiqing Li, Xiang Sun, Xinjie Zhao

“…While machine learning and neural network algorithms have recently achieved significant advancements in innovative applications for AM defect detection, practical implementations still face challenges, including insufficient detection accuracy and poor system robustness. …”
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Methods of evaluation the quality of teaching in higher education institutions by Irina V. Barannikova, Elena N. Shaforostova

“…A method of assessing the quality of education at universities, based on the criteria of quality of education, the method of expert evaluation, formalization and algorithmization of the process is proposed. The generalized assessment of the group of experts is formed on the basis of average values. …”
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Using radiomics model for predicting extraprostatic extension with PSMA PET/CT studies: a comparative study with the Mehralivand grading system by Linjie Bian, Fanxuan Liu, Yige Peng, Xinyu Liu, Panli Li, Qiufang Liu, Lei Bi, Shaoli Song

“…Radiomics features were extracted from PSMA PET/CT images to construct predictive models using Support Vector Machine (SVM) and Random Forest algorithms. In addition, among the 63 patients who underwent both PSMA PET/CT and multiparametric MRI (mpMRI), the performance of the radiomics model was compared with that of the Mehralivand Grading System. …”
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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review by Zia‐Ur‐Rehman, Mohd Khalid Awang, Ghulam Ali, Muhammad Faheem

“…This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging for AD diagnosis, where popular imaging types, reviews well‐known online accessible data sets, and describes different algorithms used in DL for the correct initial evaluation of AD are presented. …”
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Evaluation of the Finis Swimsense® and the Garmin Swim™ activity monitors for swimming performance and stroke kinematics analysis. by Robert Mooney, Leo R Quinlan, Gavin Corley, Alan Godfrey, Conor Osborough, Gearóid ÓLaighin

“…Further development to improve accuracy of feature detection algorithms, specifically for lap time and stroke count, would also increase their suitability within competitive settings.…”
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Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression by Zhonglin Zhang, Huan Liu, Xiling Gu, Yang Qiu, Jiangqing Ma, Guangyong Ai, Xiaojing He

“…This included the Akaike Information Criterion (AIC), Maximum Relevance Minimum Redundancy (mRMR), and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, validated through 10-fold cross-validation. …”
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A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. by Eric Nyarko, Edmund Fosu Agyemang, Ebenezer Kwesi Ameho, Louis Agyekum, José María Gutiérrez, Eduardo Alberto Fernandez

“…<h4>Results</h4>Considering all the responses, none of the six machine learning algorithms proved superior, but the Generalized Regression Model (Ridge) performed consistently better among the candidate models. …”
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Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang, Zhilong Huang

“…Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. …”
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A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. …”
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