A machine learning‐based approach for wait‐time estimation in healthcare facilities with multi‐stage queues by Amjed Al‐Mousa, Hamza Al‐Zubaidi, Mohammad Al‐Dweik

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OpenSTARLab: open approach for spatio-temporal agent data analysis in soccer by Calvin Yeung, Kenjiro Ide, Taiga Someya, Keisuke Fujii

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Machine Learning in E-Commerce: Trends, Applications, and Future Challenges by Elias Dritsas, Maria Trigka

“…The rapid evolution of e-commerce has been significantly influenced by the integration of machine learning (ML) and data science techniques. …”
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Machine learning in predicting firm performance: a systematic review by Yaseen Hezam, Hoa Luong, Lilian Anthonysamy

“…This study investigates the application of machine learning (ML) techniques in predicting firm performance, responding to the challenges posed by the large volumes of data required for accurate predictions. …”
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Methodological Validation of Machine Learning Models for Non-Technical Loss Detection in Electric Power Systems: A Case Study in an Ecuadorian Electricity Distributor by Carlos Arias-Marín, Antonio Barragán-Escandón, Marco Toledo-Orozco, Xavier Serrano-Guerrero

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Big Data Analytics in E-commerce Driving Business Decisions Through Customer Behavior Insights by Pande Pooja, Kulkarni Adokshaja Krishnarao, P Balamurugan, S Balakrishnan, Ramalingam Vanaja, R Revathi

Subjects: “…big data analytics…”
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Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation by Milad Shoryabi, Ahmad Hajipour, Afshin Shoeibi, Ali Foroutannia

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Machine learning-based model for behavioural analysis in rodents applied to the forced swim test by Andrea Della Valle, Sara De Carlo, Gregorio Sonsini, Sebastiano Pilati, Andrea Perali, Massimo Ubaldi, Roberto Ciccocioppo

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Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data by Tongtong Jin, Ayitijiang· Halili

“…This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. …”
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Optimized machine learning mechanism for big data healthcare system to predict disease risk factor by Venkata Nagaraju Thatha, Silpa Chalichalamala, Udayaraju Pamula, D. Pramodh Krishna, Manjunath Chinthakunta, Srihari Varma Mantena, Shariff Vahiduddin, Ramesh Vatambeti

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Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health by Vladislav Kaverinsky, Dmytro Vakulenko, Liudmyla Vakulenko, Kyrylo Malakhov

“…Through dimensionality reduction using UMAP, two distinct classes emerged, categorized as lighter and more severe cases. Utilizing machine learning methods, an automated classifier was developed based on correlated ULF indicators, which led to improved classification accuracy. …”
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Analyzing click data with AI: implications for student performance prediction and learning assessment by Mahdi-Reza Borna, Hanan Saadat, Aref Tavassoli Hojjati, Elham Akbari

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Editorial: Application of novel statistical and machine-learning methods to high-dimensional clinical cancer and (multi-) omics data volume II by Chao Xu, Shaolong Cao, Md Ashad Alam

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Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning by Mehdi Tavafi, Kalpdrum Passi, Robert Ohle

Subjects: “…machine learning…”
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In-depth Analysis on Machine Learning Approaches by Abdulhady A. Abdullah, Nergz S. Mohammed, Maryam Khanzadi, Safar M. Asaad, Zrar Kh. Abdul, Halgurd S. Maghdid

“… Machine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. …”
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Adulteration detection in cactus seed oil: Integrating analytical chemistry and machine learning approaches by Said El Harkaoui, Cristina Ortiz Cruz, Aaron Roggenland, Micha Schneider, Sascha Rohn, Stephan Drusch, Bertrand Matthäus

“…This study proposes a machine learning model that integrates analytical measurements, data simulations, and classification techniques to detect adulteration of CO with refined sunflower oil (SO) and determine the detectable limit of adulteration without measuring a huge number of different mixtures. …”
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Big Data in traumatic brain injury; promise and challenges by Denes V Agoston, Dianne Langford

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Using Big Data in Labor Market Analysis: Theoretical Approaches and Methodological Tools by A. V. Vankevich, I. N. Kalinouskaya

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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…The following six machine-learning models were used: k-nearest Neighbors, Random Forest, Support Vector Machines, Gradient Boosting Machines, and Artificial Neural Networks. …”
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Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data by Sayem Kabir, Md Fokrul Akon, Mohammad Rifat Ahmmad Rashid, Maheen Islam, Taskeed Jabid, Mohammad Manzurul Islam, Md Sawkat Ali

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