Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning by K. Miyazaki, Y. Marchetti, J. Montgomery, S. Lu, K. Bowman

“…<p>This study employs an explainable machine learning (ML) framework to examine the regional dependencies of surface ozone biases and their underlying drivers in global chemical reanalysis. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In this context, we propose a comprehensive framework that applies machine learning methods to enhance evidence collection and malware activity analysis. …”
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Bioclimatic Drivers of Amur Falcon Habitat Dynamics Using Advanced Machine Learning Models by B. Ghale, S. Mamgain, K. Gupta, A. Roy, H. C. Karnatak

“…To date, no comprehensive study has analyzed how bio-climatic factors influence migration patterns across such a broad range. This study explores the bio-climatic factors influencing the falcon's migration and habitat suitability using remote sensing, GIS, and machine learning models&mdash;Maximum Entropy (MaxEnt) and Random Forest (RF). …”
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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Second, a support vector machine (SVM) is used to classify gait patterns between unilateral hip OA patients and HCs. …”
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Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders by Cengiz Gunay, Krishan Bhalsod

“…Using MATLAB, we are training a machine learning model on electrophysiological data to recognize patterns of post-synaptic events that show potential seizure activity. …”
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Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding by Kulecki Bartłomiej, Belter Dominik

“…The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.…”
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Organ-system-based subclassification of preeclampsia using machine learning predicts pregnancy outcomes by Yanhong Xu, Yizheng Zu, Xiaosi Lu, Yiping Wang, Jiaying Zheng, Xia Xu, Jianying Yan

“…Heatmap and sankey diagram analyses revealed significant overlap between high-risk clusters, with the most frequent combination being H-Cluster 1, K-Cluster 1, L-Cluster 1 and C-Cluster 5. Conclusions Machine learning identified distinct PE subclasses based on organ system dysfunction patterns, each demonstrating unique pregnancy outcomes. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…To ensure the models’ decision-making is compatible with clinical decisions and common practices, we applied explainability techniques such as SHAP to reveal the patterns learned by the models. These patterns were found to be clinically plausible. …”
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Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…To address class imbalance, synthetic minority oversampling technique (SMOTE) was applied. Several machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forests, and Neural Networks, were implemented and evaluated. …”
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Machine Learning Algorithms in EEG Analysis of Kleefstra Syndrome: Current Evidence and Future Directions by Katerina D. Tzimourta

“…Given the growing role of machine learning (ML) in extracting patterns from EEG data in related disorders—such as Angelman, Rett and Fragile X syndromes—this review explores how similar approaches could be adapted for KS. …”
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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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Design Maintenance System on Mixer Machine to Prevent the Breakdown Using Reliability Centered Maintenance by Budhi Santri Kusuma, Mhd. Ardian Syahputra, Roaida Yanti, Dede Ibrahim Muthawali

“…However, many manufacturing companies neglect maintenance, leading to frequent machine breakdowns that can result in machine downtime and financial losses. …”
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Optimising Manufacturing Efficiency: A Data Analytics Solution for Machine Utilisation and Production Insights by Saleh Seyedzadeh, Vyron Christodoulou, Adam Turner, Saeid Lotfian

“…This paper proposes a non-invasive, data-driven methodology for monitoring and optimising machine utilisation in manufacturing environments. …”
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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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ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH by Meisam Saleki, Kamran Mostafaei, Zakaria Bin Endut, Mohammad Nabi Kianpour

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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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MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE ANALYSIS FOR APPLE LEAF DISEASE DETECTION by Anupam Bonkra, Sunil Pathak, Amandeep Kaur

“…To detect these illnesses, a relative examination of machine learning and deep learning models is carried out using the "Apple Leaves Disease Dataset. …”
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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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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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