Implementation of Several Data Mining Strategies on Electronic Nose Data for Identifying Gluten in Cheese by Mohammad Nasiri-Galeh, Mahdi Ghasemi-Varnamkhasti

“…It is  not enough to make decisions and judge the data unless discovering the relationships and patterns between the data obtained to determine the relation of new data recorded by the device to the type of cheese, For this purpose, data mining and machine learning methods have been used in this research. …”
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Artificial intelligence in predicting pathogenic microorganisms’ antimicrobial resistance: challenges, progress, and prospects by Yan Li, Xiaoyan Cui, Xiaoyan Yang, Guangqia Liu, Juan Zhang

“…The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. …”
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Equivalence Between Optical Flow, the Unrest Index, and Walking Distance to Estimate the Welfare of Broiler Chickens by Danilo Florentino Pereira, Irenilza de Alencar Nääs, Saman Abdanan Mehdizadeh

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Investigation of the application of an automated monitoring system for detecting transmission cable deterioration in Nigeria: A case study of transmission cable lines between Offa... by C.S. Omoniabipi, R. Agbadede, K.C. Emmanuel, O.J. Adewuni, I. Allison

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Reinforcement learning-based alpha-list iterated greedy for production scheduling by Kuo-Ching Ying, Pourya Pourhejazy, Shih-Han Cheng

“…In line with the emerging trend in the optimization literature, this study introduces the Reinforcement-learning-based Alpha-List Iterated Greedy (RAIG) algorithm to contribute to the advances in machine learning-based optimization, notably for solving combinatorial problems. …”
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Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data by Bo Lu, Bo Lu, Qingyun Liu, Hao Liu, Tianxiang Long, Tianxiang Long

“…By applying the XGBoost algorithm and SHAP (SHapley Additive exPlanations), an explainable machine learning framework was established to evaluate the importance of various factors, explore the nonlinear relationships between variables and walking activity, and analyze the interaction effects among these variables.ResultsThe findings underscore the significant impact of several key factors, including the proportion of sports land, proximity to water bodies, and Normalized Difference Vegetation Index NDVI, alongside the notable influence of six distinct campus area types. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Traditional statistical methods based on hydrostatic-season-time (HST) theory, while having clear physical meanings and being easy to implement, are limited by their inherent linear assumptions, resulting in constrained prediction accuracy. Machine learning models such as random forest, support vector regression, and extreme learning machine (ELM) extend statistical approaches but still lack the ability to establish temporal dependencies due to their static input-output mapping relationships. …”
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Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling by Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong, Deshun Zhang, Jing Li, Manqin Yao, Wei Yong, Mengjia Li, Yujie Huang

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Analysis of Microbiome for AP and CRC Discrimination by Alessio Rotelli, Ali Salman, Leandro Di Gloria, Giulia Nannini, Elena Niccolai, Alessio Luschi, Amedeo Amedei, Ernesto Iadanza

“…Subsequently, the synthesised data quality was evaluated using a logistic regression model in parallel with an optimised support vector machine algorithm (polynomial kernel). The data quality is considered good when neither of the two algorithms can discriminate between real and synthetic data, showing low accuracy, F1 score, and precision values. …”
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An Innovative Analysis of Time Series-Based Detection Models for Improved Cancer Detection in Modern Healthcare Environments by Uma Shankari Srinivasan, Venkat Pavithra, Kaliappan Sutha, Sridevi Ramachandiran, Nallathambi Indumathi

“…This enhanced version of time series analysis incorporates multiple layers of data sources and uses advanced machine learning algorithms to identify patterns that could signal the presence of a tumor. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.…”
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Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model by Lufeng Chen, Qingquan Chen, Zhimin Huang, Ling Yao, Jiajing Zhuang, Haibin Lu, Yifu Zeng, Jimin Fan, Ailing Song, Yixiang Zhang

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Transcriptomic exploration yields novel perspectives on the regulatory network underlying trichome initiation in Gossypium arboreum hypocotyl by Yuxing Xie, Luying Yang, Zewei Zhao, Mingquan Ding, Yuefen Cao, Xin Hu, Junkang Rong

“…Additionally, integrated weighted gene co-expression network analysis (WGCNA) and Cytoscape analyses identified 20 core regulatory genes from a total of 59 candidates linked to epidermal development. Utilizing three machine learning algorithms (SVM-RFE, Boruta, and LASSO), we consistently prioritized five key regulators: Ga02G1392 (TBR), Ga03G0474 (OMR1), Ga12G2860 (ACO1), Ga11G2117 (BBX19), and Ga12G2864 (CUE). …”
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ML-Based Control Strategy for PHEV Under Predictive Vehicle Usage Behaviour by Aleksandr Doikin, Aleksandr Korsunovs, Felician Campean, Oscar García-Afonso, Enrico Agostinelli

“…This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. …”
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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

“…Our feature selection process utilizes ensemble learning algorithms integrated with regularized regression techniques to analyze various parameters. …”
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Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review by Sergio Agudelo-Pérez, Daniel Botero-Rosas, Laura Rodríguez-Alvarado, Julián Espitia-Angel, Lina Raigoso-Díaz

“…Prediction of exclusive breastfeeding patterns: AI models, such as decision trees and machine learning algorithms, identify factors influencing breastfeeding practices, including maternal experience, hospital policies, and social determinants, highlighting actionable predictors for intervention. 2. …”
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Artificial Intelligence: The Latest Advances in the Diagnosis of Bladder Cancer by Satyendra Singh, Ram Mohan Shukla

“…This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. …”
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Implementation of Generative Artificial Intelligence in Sociological Research by V. E. Drach, Yu. V. Torkunova

“…The paper examines methodologies for generating surveys, processing respondents' answers, and analyzing big data using machine learning algorithms. The focus is on specific cases of GAI applications in sociological research, as well as examples of successful projects.Results and discussion. …”
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Application of artificial intelligence in electrochemical diagnostics for human health by Koushlesh Ranjan, Basanti Barar, Minakshi Prasad, Gaya Prasad

“…The modern-day miracle, Artificial Intelligence (AI) offers transformative solutions to these challenges. The applications of machine learning (ML) algorithms and AI in electrochemical data analysis have significantly enhanced the sensitivity and specificity of diagnostic methods. …”
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Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete by Celal Cakiroglu, Tanvir Hassan Tusher, Md. Shahjalal, Kamrul Islam, AHM Muntasir Billah, Moncef L. Nehdi

“…Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. …”
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