Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation by B. Shamreen Ahamed, Meenakshi S. Arya, Auxilia Osvin V. Nancy

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Occluded face recognition using optimum features based on efficient preprocessing and machine learning by Rajesh H. Khobragade, Dinesh B. Bhoyar, Ajay Paithane, Suresh Kurumbanshi

“…The proposed work outperforms state of art techniques concerning classification accuracy obtained using Support Vector Machine.…”
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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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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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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…Through interpretable AI techniques, we identify several key patterns: (1) Machine learning with explainability methods effectively reveals nuanced factor-achievement relationships; (2) Behavioral metrics (hw_score, ans_score, discus_score, attend_score) show consistent positive associations; (3) High-achievers demonstrate both superior collaborative skills and preference for technology-enhanced environments; (4) Gamification frequency (s&v_num) significantly boosts outcomes; while (5) Assignment frequency (hw_num) exhibits counterproductive effects. …”
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An innovative efficiency of incubator to enhance organization supportive business using machine learning approach. by Xin Li, Qian Zhang, Hanjie Gu, Salwa Othmen, Somia Asklany, Chahira Lhioui, Ali Elrashidi, Paolo Mercorelli

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Enhancing Defect Classification in Solar Panels With Electroluminescence Imaging and Advanced Machine Learning Strategies by Fatih Demir

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Programmable spatial magnetization stereolithographic printing of biomimetic soft machines with thin-walled structures by Xianghe Meng, Shishi Li, Xingjian Shen, Chenyao Tian, Liyang Mao, Hui Xie

“…Abstract Soft machines respond to external magnetic stimuli with targeted shape changes and motions due to anisotropic magnetization, showing great potential in biomimetic applications. …”
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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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Finding the original mass: A machine learning model and its deployment for lithic scrapers. by Guillermo Bustos-Pérez

“…This allows for the wide spread implementation of a highly precise machine learning model for predicting initial mass of flake blanks successively retouched into scrapers.…”
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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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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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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. …”
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Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases by Roshan Kumar, V Srirama, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga

“…Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. …”
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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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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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