Assessing distortion in carbon fiber woven fabrics based on machine vision by Shiyue Li, Quanzhou Yao, Lin Ye

“…This work proposes a machine vision method to locate defective areas, identify defects, and describe fiber tow distribution patterns. …”
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Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques by Danilo Augusto Sarti, Tommaso Bardelli, Pier Giacomo Bianchi, Anna Pia Maria Giulini

“…This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). …”
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Improving medical machine learning models with generative balancing for equity and excellence by Brandon Theodorou, Benjamin Danek, Venkat Tummala, Shivam Pankaj Kumar, Bradley Malin, Jimeng Sun

“…Abstract Applying machine learning to clinical outcome prediction is challenging due to imbalanced datasets and sensitive tasks that contain rare yet critical outcomes and where equitable treatment across diverse patient groups is essential. …”
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Power sequence definition under woodworking milling on contour-milling machines by Alexander N. Chukarin, Sergey V. Golosnoy

“…As a result of the conducted research, the mechanism of the force generation under milling; rules of the forces distribution over the projections; and patterns of variation in the cut-off allowance under milling are determined and specified. …”
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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…That is becoming increasingly popular is the use of Machine Learning  algorithms in the decision-making process. …”
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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

“…This study proposes an advanced machine learning-based methodology for detecting energy fraud, leveraging real-world data from energy distribution networks. …”
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Machine learning applied to the design and optimization of polymeric materials: A review by Sudarsan M. Pai, Karim A. Shah, Sruthi Sunder, Rodrigo Q. Albuquerque, Christian Brütting, Holger Ruckdäschel

“…ML approaches can analyze vast amounts of data, uncover hidden patterns, and generate predictive models that significantly reduce the time needed to develop materials with desired properties. …”
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Machine learning tools for deciphering the regulatory logic of enhancers in health and disease by Spyros Foutadakis, Vasiliki Bourika, Ioanna Styliara, Panagiotis Koufargyris, Asimina Safarika, Eleni Karakike

“…Transcriptional enhancers are DNA regulatory elements that control the levels and spatiotemporal patterns of gene expression during development, homeostasis, and pathophysiological processes. …”
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Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships by Theodoros Kyriazos, Mary Poga

“…Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. …”
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Machine learning-based single-sample molecular classifier for cancer grading by Zoia Antysheva, Nikita Kotlov, Mariia V. Guryleva, Ivan Valiev, Viktor Svekolkin, Anna Belozerova, Sheila T. Yong, Dmitry Tabakov, Alexander Bagaev, Vladimir Kushnarev

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Stability Prediction in Sustainable Energy Systems Using Machine Learning Models by Md Sarowar Hossain, Mohammad A. Abido

“…By analyzing diverse datasets covering factors like demand, supply, environmental variables, and grid dynamics, machine learning models can capture complex patterns in power system behavior. …”
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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…ML models, such as support vector machines and ensemble models, use tabular data points to identify patterns and predict fire behavior. …”
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Anomaly detection in virtual machine logs against irrelevant attribute interference. by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…The LADSVM approach excels at detecting anomalies in virtual machine logs characterized by strong sequential patterns and noise. …”
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Enhancing Urban Parking Efficiency Through Machine Learning Model Integration by Sai Sneha Channamallu, Sharareh Kermanshachi, Jay Michael Rosenberger, Apurva Pamidimukkala

“…This study aims to tackle these issues that escalate congestion and pollution and decrease urban productivity, by utilizing machine learning models to accurately predict parking space availability and categorize occupancy levels. …”
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Predicting agricultural drought in central Europe by using machine learning algorithms by Endre Harsányi

“…Thus, this research evaluates the patterns and magnitude of agriculture droughts using Standardized Precipitation Evapotranspiration Index (SPEI) from 1926 to 2020 in eastern Hungary, and assess the performance of six machine learning models (Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Extreme Gradient Boost (XGB), Support Vector Machines (SVM), and Multi-Layer Perceptron (ANN-MLP)) in predicting agriculture droughts. …”
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Examining peptide–gold nanoparticle interactions through explainable machine learning by Malak Gamal Abdelmeguid, Jose Isagani B. Janairo, Nishanth G. Chemmangattuvalappil

“…This work develops an explainable binary machine learning classifier using rough sets as the algorithm and amino acid composition as the features. …”
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A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines by Yurim Choi, Inwhee Joe

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Purchasing Prediction Using Machine Learning Algorithms for Optimizing Inventory Management by Reza Hamdi Prayetno, Rani Destika Purba, Kyrene Wirawan, Kelvin Sweet, Evta Indra

“…The model successfully captured seasonal patterns and trends in sales data, proving its ability to forecast stock requirements. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…Several ML algorithms are analyzed in the study for data from several sources like satellite imagery, drone based sensors, and field data, including Convolutional Neural Networks (CNNs), Random Forest and Support Vector Machines (SVMs). With integration of these data sources, ML models can find patterns, anomalies in health problems. …”
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