Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making by Arnoldo Armenta-Castro, Orlando de la Rosa, Alberto Aguayo-Acosta, Mariel Araceli Oyervides-Muñoz, Antonio Flores-Tlacuahuac, Roberto Parra-Saldívar, Juan Eduardo Sosa-Hernández

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Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading by Tien-Thinh Le

“…In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. …”
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RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

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Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India by Devanantham Abijith, Subbarayan Saravanan, K S S Parthasarathy, Nagireddy Masthan Reddy, Janardhanam Niraimathi, Ahmed Ali Bindajam, Javed Mallick, Maged Muteb Alharbi, Hazem Ghassan Abdo

“…This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. …”
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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability by Sajid Ullah, Xiuchen Qiao, Aqil Tariq

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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Design Retrospective population cohort study.Setting South London (2007–2013).Participants n=56 258 pupils with linked education and health data.Primary outcome measures Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. …”
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Machine learning-assisted design of Ti–V–Nb–Mo refractory high-entropy alloys with higher ductility and specific yield strength by Yan Li, Junjie Gong, Shilong Liang, Wei Wu, Yongxin Wang, Zheng Chen

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Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

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Insights into the contribution of multiple factors on Ixodes ricinus abundance across Europe spanning 20 years using different machine learning algorithms by Samantha Lansdell, Abin Zorto, Misaki Seto, Edessa Negera, Saeed Sharif, Sally Cutler

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Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models by RN Singh, P. Krishnan, C. Bharadwaj, Sonam Sah, B. Das

“…Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. …”
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Novel approach for noninvasive pelvic floor muscle strength measurement using extracorporeal surface perineal pressure measurement and machine learning modeling by Ui-jae Hwang, Sun-hee Ahn, Hyeon-ju Lee, Yurin Jeon, Myung Jae Jeon

“…Seven ESPP variables were calculated based on ESPP data and intra- and inter-rater reliabilities were assessed. Machine learning algorithms predicted bladder base displacement from ESPP variables. …”
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Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation by Chuanren Zhuang, Ruomeng Hu, Ke Li, Zhengshuang Liu, Songjie Bai, Sheng Zhang, Xuehuan Wen

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Novel Unsupervised Machine Learning Method for Identifying Falling from Height Hazards in Building Information Models through Path Simulation Sampling by Yu Yan, Shen Zhang, Xingyu Wang, Xuechun Li

“…To address this issue, we proposed a new identification method for FFH risk points by incorporating BIM, path simulation, and machine learning techniques. Our approach, based on the likelihood exposure consequence criterion, efficiently identified and prioritized hazardous FFH risk points. …”
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PhyIndBC: Development of a machine learning tool for screening of potential breast cancer inhibitors from phytochemicalsGitHub by Agneesh Pratim Das, Subhash M. Agarwal

Subjects: “…Machine learning…”
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Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing by Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu

“…In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. …”
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Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms by Billy Ogwel, Vincent H. Mzazi, Alex O. Awuor, Caleb Okonji, Raphael O. Anyango, Caren Oreso, John B. Ochieng, Stephen Munga, Dilruba Nasrin, Kirkby D. Tickell, Patricia B. Pavlinac, Karen L. Kotloff, Richard Omore

Subjects: “…Machine Learning…”
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Retraction Note: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

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BÜYÜK VERİ ANALİZİNDE YAPAY ZEKÂ VE MAKİNE ÖĞRENMESİ UYGULAMALARI - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPLICATIONS IN BIG DATA ANALYSIS by Muhammet Atalay, Enes Çelik

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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

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