Software module for automatic correction of drawings obtained as a result of graphic data exchange between CAD systems, using the furniture production example by A. V. Kalinichenko, A. Yu. Alikov, T. A. Yurosheva

“…As part of the study, methods of geometric modeling, analytical geometry and graphical presentation of results were used. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. …”
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In Vitro Oral Cavity Permeability Assessment to Enable Simulation of Drug Absorption by Pankaj Dwivedi, Priyata Kalra, Haiying Zhou, Khondoker Alam, Eleftheria Tsakalozou, Manar Al-Ghabeish, Megan Kelchen, Giovanni M. Pauletti

“…The tissue-associated dose fraction recovered at the end of the transport experiment was significantly increased in the buccal EpiOral™ tissue model, reaching up to 8.5% for sufentanil. <b>Conclusions</b>: Experimental permeation data collected for selected APIs in FDA-approved oral cavity products will serve as a training set to aid the development of predictive computational models for improving algorithms that describe drug absorption from the oral cavity. …”
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Recursive GNNs for Learning Precoding Policies With Size-Generalizability by Jia Guo, Chenyang Yang

“…Nonetheless, it has been noticed in literature that only satisfying the PE property of a precoding policy in multi-antenna systems cannot ensure a GNN for learning precoding to be generalizable to the unseen problem scales. Incorporating models with GNNs helps improve size generalizability, which however is only applicable to specific problems, settings, and algorithms. …”
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MFFCI&#x2013;YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information by Sheng Xu, Lin Song, Junru Yin, Qiqiang Chen, Tianming Zhan, Wei Huang

“…Most current researches primarily focus on improving experimental accuracy using large models, often neglecting the deployment challenges. …”
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Enhancing sex determination in forensic anthropology: A comparative analysis of cranial measurements using artificial neural network by Matheus Jhonnata Santos Mota, Alberto Calson Alves Vieira, Lucas Silva Lima, João Victor Melquiades Sátiro, Carlos Mathias de Menezes Neto, Patrízia Lisieux Prado Paixão, Gabriel Pedro Gonçalves Lopes, Lauro Roberto de Azevedo Setton, Carlos Eduardo de Andrade, Richard Halti Cabral

“…Traditional methods often suffer from subjectivity and variability, prompting a shift towards morphometric approaches for improved precision. In this context, this study aims to identify the most accurate cranial measurements for sex estimation and ascertain the minimum number of variables needed to match the performance of a 12-measure model using a neural network. …”
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ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions by Pavan Kumar S, Nirav Pravinbhai Bhatt

“…Moreover, it enhances sampling algorithms by enabling loopless sample generation. In summary, ThermOptCOBRA significantly improves TIC handling in GEMs, advancing metabolic model quality for deeper insights into cellular metabolism.…”
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Forecasting the volatility of Financial Time Series by Tree Ensembles by O. S. Vidmant

“…The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. …”
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Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study by An Zhou, Kui Chen, Kui Chen, Yonghui Wei, Qu Ye, Qu Ye, Yuanming Xiao, Rong Shi, Jiangang Wang, Wei-Dong Li

“…Clinical utility was evaluated through decision curve analysis.ResultsCompared with the more complex algorithms, the elastic net model demonstrated superior performance (AUC 0.754). …”
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A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection by Guanqing Kong, Guanqing Kong, Shuang Ma, Shuang Ma, Wei Zhao, Wei Zhao, Haifeng Wang, Haifeng Wang, Qingxi Fu, Qingxi Fu, Jiuru Wang

“…To extract features strongly correlated with epileptic classification detection, an improved particle swarm optimization (PSO) algorithm and Pearson correlation analysis are combined. …”
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Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification by Yunna Mentari Indah, Rafika Aristawidya, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah

“…These findings contribute to the field of statistical modeling by demonstrating how ensemble methods can improve classification accuracy and provide valuable insights for data-driven policymaking, thus enhancing regional development planning and supporting future HDI-related research.…”
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Two Power Allocation and Beamforming Strategies for Active IRS-aided Wireless Network via Machine Learning by Q. Cheng, J. Bai, X. Wang, B. Shi, W. Gao, F. Shu

“…Simulation results show that the proposed algorithms can effectively improve the rate performance compared to fixed PA strategies, only optimizing PA factor, aided by passive IRS, and without IRS.…”
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Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…Predicting student attrition accurately can help institutions implement timely interventions to improve retention. Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. …”
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Wineinformatics: Wine Score Prediction with Wine Price and Reviews by Yuka Nagayoshi, Bernard Chen

“…Using this dataset, classifiers, and normalization methods, the models with and without the price feature were compared. …”
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A Novel Active RFID and TinyML based system for livestock Localization in Pakistan by Syed Atir Raza Shirazi, Maham Fatima, Abdul Wahab, Sadaf Ali

“…To realize the full potential of this unique Active RFID and TinyML-based livestock localization system in Pakistan, further research should focus on optimizing localization algorithms, enhancing TinyML models, and exploring interaction with upcoming technologies …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…However, challenges persist, including data privacy, algorithmic bias, ethical dilemmas, and regulatory hurdles, emphasizing the need for robust governance. …”
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Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…Following this, the cervical cells are segmented and classified using a combination of machine learning algorithms, such as shallow machine learning models and Convolutional Neural Networks (CNN). …”
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Ambulatory Oxygen for Pulmonary Fibrosis (OxyPuF): a randomised controlled trial and acceptability study by Rachel L Adams, Alisha Maher, Nicola Gale, Anjali Crawshaw, David Thickett, Alice M Turner

“…Traditional qualitative analysis and arts-based coproduction analysis approaches were used to produce a short film. An economic model was planned but could not occur due to early termination. …”
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To explore the molecular mechanisms and shared genetic characteristics of rheumatoid arthritis and cervical cancer based on multiple omics and clinical samples by Xifeng Xu, Yujie Huang, Wu Wei, Zhong Lin, Yongjin Luo, Kaiyi Meng

“…Single-cell enrichment scores were calculated using RA-specific transcriptome differentially expressed genes, and prognostic models were developed using 10 machine learning algorithms to assess cervical cancer differential gene expression associated with RA scores. …”
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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
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