PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS by Edin Osmanbegović, Anel Džinić, Mirza Suljić

“…The name "machine learning" refers to the automated detection of meaningful patterns in large data sets.  …”
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Predicting Students’ Performance Using a Hybrid Machine Learning Approach by Ropafadzo Duwati, Tawanda Mudawarima

“…This presents an opportunity for learning institutions to predict students’ performance using Machine Learning (ML) techniques. This paper explores the application of a hybrid ML approach to students’ performance. …”
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Stock Price Prediction in the Financial Market Using Machine Learning Models by Diogo M. Teixeira, Ramiro S. Barbosa

“…The complexity of advanced models such as XGBoost and GRU is reflected in their overall performance, suggesting that they can be particularly effective at capturing patterns and making accurate predictions in more complex time series, such as stock prices.…”
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An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

“…The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. …”
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Anomaly Detection in Data Streams Using Machine Learning and Deep Learning by Muhammad Amin Daneshwar

“…The goal of this anomaly identification in the data streams is to find patterns that deviate substantially from the way things usually work. …”
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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

“…The highest speed is observed when the copy milling machines are operated. In the cutting conditions standards, only the power action amplitude is specified for woodworking which makes it difficult to determine the noise spectra. …”
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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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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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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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Machine learning-assisted development of polypyrrole-grafted yarns for e-textiles by Matteo Iannacchero, Joakim Löfgren, Mithila Mohan, Patrick Rinke, Jaana Vapaavuori

“…The optimized yarns are woven into prototype fabrics with different patterns, and we demonstrate their applicability as flexible conductive wearable and heaters.…”
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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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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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