Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study by Shailesh Appukuttan, Shailesh Appukuttan, Aude-Marie Grapperon, Aude-Marie Grapperon, Mounir Mohamed El Mendili, Hugo Dary, Maxime Guye, Annie Verschueren, Jean-Philippe Ranjeva, Shahram Attarian, Wafaa Zaaraoui, Matthieu Gilson

“…In this study, we systematically evaluated the impact of various machine learning pipeline configurations, including scaling methods, feature selection, dimensionality reduction, and hyperparameter optimization. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

“…In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. …”
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Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…The framework integrates several predictive models—including Random Forest, Support Vector Machines, and Logistic Regression—developed in Python using the scikit-learn library. …”
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Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate by Bhukya Govardhan Naik, G. Nakkeeran, Dipankar Roy, G. Uday Kiran, Kalyani Gurram, Gade Venkata Ramanjaneyulu, George Uwadiegwu Alaneme, Mutiu Shola Bakare

“…This study explores the use of Waste Glass Coarse Aggregate (WGCA) as a partial replacement for natural coarse aggregates in concrete, assessing its effects on mechanical performance and utilizing machine learning models for predictive analysis. The assessment involved concrete mixtures with replacement levels of 0%, 5%, 10%, and 15% WGCA, focusing on their compressive, tensile, and flexural strengths at both 7 and 28 days of curing. …”
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Predicting the infecting dengue serotype from antibody titre data using machine learning. by Bethan Cracknell Daniels, Darunee Buddhari, Taweewun Hunsawong, Sopon Iamsirithaworn, Aaron R Farmer, Derek A T Cummings, Kathryn B Anderson, Ilaria Dorigatti

“…Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9-89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…Additionally, k-fold cross-validation was conducted to ensure robust model evaluation.<h4>Results</h4>This study confirms a significant decline in under-5 mortality in Bangladesh over the study period, with machine learning models providing accurate predictions of future trends. …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…This study employs remote sensing and machine learning techniques to evaluate the effectiveness of such interventions in a village forest area in central Malawi. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. …”
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Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD by Gabriel Cubillos, Javier Perez-Valenzuela, Herman Aguirre, Luz Martínez, Lorena Castro, Gabriel Mezzano, Claudio A. Perez

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AGE AND GENDER CLASSIFICATION FROM IRIS IMAGES OF THE EYE USING MACHINE LEARNING TECHNIQUES by Martins E. Irhebhude, Adeola O. Kolawole, Halima Abemi

“…The 3D histogram with PCA recorded an excellent classification performance accuracy of 99.27% as against the EfficientNet deep learning model which recorded 52.29%. The recommended feature technique can help to adequately classify gender and age from iris images leading to a more robust recognition model. …”
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Research on the Mechanism of the Impact of Green View Index of Urban Streets on Thermal Environment: A Machine Learning-Driven Empirical Study in Hangzhou, China by Qiguan Wang, Yanjun Hu, Hai Yan

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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. …”
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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook by Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng

“…Abstract The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. …”
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POD-Based Machine Learning Approach for Coupled EM-Thermal Analysis in Microwave Heating by Jeong-Wan Lee, Gyu-Sik Choi, Sung-Jun Yang

“…In this paper, we propose a machine learning-based approach for reduced-order modeling that efficiently predicts the specific absorption rate (SAR) distributions in coupled EM and thermal analyses. …”
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Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis by Joost Verduijn, Eva Degroote, André G. Skirtach

“…Data analysis was performed by machine learning (ML), here SVMs, where the model utilizing the spectra directly into a support vector machine (SVM) outperforms other SVM strategies correctly predicting 73% of all spectra. …”
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Machine Learning Reveals Microbial Taxa Associated with a Swim across the Pacific Ocean by Garry Lewis, Sebastian Reczek, Osayenmwen Omozusi, Taylor Hogue, Marc D. Cook, Jarrad Hampton-Marcell

“…Multivariate analysis was used to analyze the microbial community structure, and machine learning (random forest) was used to model the microbial dynamics over time using R statistical programming. …”
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Smart Irrigation System with IoT, Machine Learning, and Solar Power for Efficient Plant Care by Justin M. A. Capcha-Ochoa, Jefferson A. Chahua-Benito, Miguel A. Serafin-Cayllahua, Sebastian E. Mamani-Martinez, Jesus G. Mendivil-Imbertis, Jordan I. Mendoza-Fernandez, Roberto J. M. Casas-Miranda, Maritza Cabana-Cáceres, Cristian Castro-Vargas

“…This study aims to develop an intelligent irrigation system based on the Internet of Things (IoT) and machine learning to optimize water use, improve plant monitoring, and enhance security. …”
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Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling by Hyginus Obinna Ozioko, Emmanuel Ebube Eze

“…ANOVA results (F = 12.97, p = 1.84E− 07) confirmed significant strength differences, with post-hoc tests indicating no significant effect at 2–7% replacement but notable reductions at ≥ 10% (p < 0.05). Three machine learning models: linear regression, polynomial regression, and artificial neural networks, were developed to predict compressive strength. …”
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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine by Andre F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

“…Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. …”
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