Influence of the Level of Defective Insulation Resistance of Electrical Machine Windings on Parameters of no-Load Current by A. V. Isaev, Yu. V. Suhodolov, S. V. Sizikov, A. A. Lomtev, V. A. Lychkovsky

“…The purpose of the work was to consider and evaluate the possibility of a method that, based on the analysis of the parameters and characteristics of the no-load current, will allow to characterize the state of the current-carrying parts of the windings of diagnosed electric machines. The paper presents the results of experimental studies of the patterns of influence of the level of defective resistance of interturn insulation in the windings of electric machines on the parameters of the no-load current, including the features of changes in the parameters of its spectral components. …”
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Environmental impacts of economic growth: A STIRPAT analysis using machine learning algorithms by J. Krishnendu, Biswajit Patra

“…While the inverted U-shaped EKC is observed in most cases, more complex patterns, such as an M-shaped curve, emerge as countries progress economically. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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Artificial Intelligence for Smoking Detection: A Review of Machine Learning and Deep Learning Approaches by Mohammed Al-Hayali, Fawziya Ramo

“…Recent advances in deep learning, machine learning, Artificial Intelligence (AI), big data analytics, and computer vision have greatly enhanced smoking detection. …”
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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia. by Daniel R Schrider, Julien Ayroles, Daniel R Matute, Andrew D Kern

“…We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. …”
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The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies by Suchitra Deviprasad, N. Madhumithaa, I. Walter Vikas, Archana Yadav, Geetha Manoharan

“…The ML-based task automation framework utilizes automation bots which can simulate all processes of HR management such as recruitment, time attendance, tracking employee records, scheduling calendar, and office administration tasks. The machine learning-based task automation framework utilizes predictive analytics to identify trends, patterns, behaviour, anomalies, and important insights from the large volumes of structured and unstructured data.…”
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Analyzing and Rewarding Credit Card Spending Habits in India: a Machine Learning Approach by Renuka Agrawal, Aryan Khanna, Safa Hamdare

“…The proposed work addresses this gap by leveraging machine learning techniques to analyze and assess credit card spending patterns and propose design targeted reward programs. …”
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Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review by Marcelo Leiva-Bianchi, Nicolas Castillo, César A. Astudillo, Francisco Ahumada-Méndez

“…Abstract This study presents a systematic review (SR) and meta-analysis (MA) on the use of machine learning (ML) methods for detecting online grooming, a form of manipulation and child sexual abuse. …”
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Predicting Mobile Payment Behavior Through Explainable Machine Learning and Application Usage Analysis by Myounggu Lee, Insu Choi, Woo-Chang Kim

“…This study presents a comprehensive framework for predicting mobile payment behavior by integrating demographic, situational, and behavioral factors, focusing on patterns in mobile application usage. To address the complexity of the data, we use a combination of machine-learning models, including extreme gradient boosting, light gradient boosting machine, and CatBoost, along with Shapley additive explanations (SHAP) to improve interpretability. …”
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Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall by Se-Dong Jang, Jae-Hwan Yoo, Yeon-Su Lee, Byunghyun Kim

“…This study addresses these limitations by proposing a machine learning-based flood prediction method using a Random Forest model. …”
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Planning and layout of tourism and leisure facilities based on POI big data and machine learning. by Shifeng Wu, Jiangyun Wang, Yinuo Jia, Jintian Yang, Jixiu Li

“…Drawing on POI and demographic data, and considering the distribution patterns of existing tourism and leisure facilities, this research applies machine learning to quantitatively simulate the optimal siting of such amenities. …”
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Prediction of building subsidence in Vietnam using machine learning techniques based on leveling results by Dinh Trong Tran, Ngoc Dung Luong, Dinh Huy Nguyen

“…However, traditional prediction methods based on mathematical models have limitations in capturing complex subsidence patterns. Machine learning techniques have shown promise in enhancing subsidence prediction accuracy. …”
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Domain Knowledge Preservation in Financial Machine Learning: Evidence from Autocallable Note Pricing by Mohammed Ahnouch, Lotfi Elaachak, Erwan Le Saout

“…The component-wise analysis reveals complex interactions between autocall mechanisms and higher-order sensitivities, particularly affecting vanna and volga patterns near barrier levels. These findings provide empirical evidence that financial machine learning benefits from domain-specific feature engineering principles that preserve economic relationships. …”
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A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing by In-Bae Lee, Youngjin Kim, Sojung Kim

“…Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. …”
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Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério, Luís F. F. M. Santos

“…The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. …”
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Machine learning based disruption prediction using long short-term memory in KSTAR by Jeongwon Lee, Jayhyun Kim, Jinsu Kim, Sang-hee Hahn, Hyunsun Han, Giwook Shin, Yong-Su Na, Yong Un Nam

“…This study presents a machine learning model for predicting plasma disruptions using the KSTAR database. …”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…Background: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. …”
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Exploring New Paradigms in Time Series Prediction by Integrating Computer Simulations and Machine Learning by Pengcheng Wu, Yan Shi, Pengfei Zhao, Zhengzhao Gu

“…To address existing limitations, we propose a novel paradigm that integrates machine learning with computer simulations by embedding simulation-derived constraints and structural priors directly into the model training process. …”
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Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning by Giulio Angiolini, Giovanna Maria Dimitri

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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…By leveraging Azure Machine Learning's capabilities, researchers and healthcare professionals can analyze large datasets containing patient information and medical records to identify patterns and risk factors associated with heart attacks. …”
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