Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District by S. Khan, A. Bhardwaj, M. Sakthivel

“…This highlights the effectiveness of machine learning classifiers, especially RF and SVM, in accurately mapping LULC patterns in Jammu district, suggesting RF's potential as a reliable tool for remote sensing-based LULC mapping.…”
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Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt by Shaimaa A. El-Dabaa, Farouk I. Metwalli, Ali Maher, Amir Ismail

“…Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. …”
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What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China by Hao Shen, Bo Shu, Jian Zhang, Yaoqian Liu, Ali Li

“…Interaction effects and non-linear patterns were also assessed.ResultsThe study identified three key findings: (1) A significant difference exists between the influencing factors of activity willingness and activity intensity. …”
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The OpenMindat v1.0.0 R package: a machine interface to Mindat open data to facilitate data-intensive geoscience discoveries by X. Que, J. Zhang, W. Chen, J. Ralph, X. Ma

“…<p>Technologies such as machine learning and deep learning are powering the discovery of meaningful patterns in Earth science big data. …”
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Quantifying Ecological Dynamics and Anthropogenic Dominance in Drylands: A Hybrid Modeling Framework Integrating MRSEI and SHAP-Based Explainable Machine Learning in Northwest Chin... by Beilei Zhang, Xin Yang, Mingqun Wang, Liangkai Cheng, Lina Hao

“…The study revealed the spatiotemporal evolution patterns through the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test, and adopted the Light Gradient Boosting Machine (LightGBM) combined with the Shapley Additive Explanation (SHAP) to quantify the contributions of ten natural and anthropogenic driving factors. …”
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Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong by Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung

“…In the first step, we used four machine learning techniques, including the Bernoulli mixture model and partition-based methods, to cluster the comorbidities. …”
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Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine learning by Changjin Wei, Yongfeng Zhu, Caiming Chen, Feipeng Li, Li Zheng

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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…By investigating blade fault patterns and using appropriate diagnostic techniques, it becomes possible to predict potential failures and schedule maintenance proactively. …”
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Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach by Zhenzhen Li, Jianing Fan, Jiajun Fan, Jiaxin Miao, Dawei Lin, Jingyan Zhao, Xiaochun Zhang, Wenzhi Pan, Daxin Zhou, Junbo Ge

“…This study aimed to identify risk factors and develop predictive models for post-operative MR following TAVR using machine learning (ML) techniques to enhance early detection and intervention. …”
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Assessing Uneven Regional Development Using Nighttime Light Satellite Data and Machine Learning Methods: Evidence from County-Level Improved HDI in China by Xiping Zhang, Jianbin Xu, Saiying Zhong, Ziheng Wang

“…The improved HDI for county-level areas in the Ningxia Hui Autonomous Region was validated using a machine learning model, resulting in a Pearson correlation coefficient of 0.93. …”
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Analysis of Ground Subsidence Evolution Characteristics and Attribution Along the Beijing–Xiong’an Intercity Railway with Time-Series InSAR and Explainable Machine-Learning Techniq... by Xin Liu, Huili Gong, Chaofan Zhou, Beibei Chen, Yanmin Su, Jiajun Zhu, Wei Lu

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Pengujian Rule-Based pada Dataset Log Server Menggunakan Support Vector Machine Berbasis Linear Discriminat Analysis untuk Deteksi Malicious Activity by Kurnia Adi Cahyanto, Muhammad Anis Al Hilmi, Muhamad Mustamiin

“…In addition, if there is a file uploaded by a user, it can also be linked in server log analysis in recognizing activity patterns and malicious files. The log dataset that has been obtained is processed using rule-based labeling which will later be tested with a Linear Discriminant Analysis-based Support Vector Machine modeling. …”
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Enhancing medical image classification via federated learning and pre-trained model by Parvathaneni Naga Srinivasu, G. Jaya Lakshmi, Sujatha Canavoy Narahari, Jana Shafi, Jaeyoung Choi, Muhammad Fazal Ijaz

“…The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. …”
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Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Effic... by Shadi Jacob Khoury, Yazeed Zoabi, Mickey Scheinowitz, Noam Shomron

“…In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. …”
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Smartphone-Based Sensing System for Identifying Artificially Marbled Beef Using Texture and Color Analysis to Enhance Food Safety by Hong-Dar Lin, Yi-Ting Hsieh, Chou-Hsien Lin

“…A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. …”
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Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower by Guy Atsmon, Anna Brook, Tom Avikasis Cohen, Fadi Kizel, Hanan Eizenberg, Ran Nisim Lati

“…These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). …”
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ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION by Patient MUSUBAO SWAMBI, Albert Ntumba Nkongolo, Pierre Kafunda Katalay, Rostin Mabela Matendo Makengo, Eugène Mbuyi Mukendi

“…This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. …”
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Key Technology of SSL Encrypted Application Identification Under Imbalance of Application Class by Xuejiao Chen, Pan Wang, Shidong3 Liu

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Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of <i>Helicobacter pylori</i> Infection by Sultan Noman Qasem

“…Recent advancements in machine learning (ML) and quantum machine learning (QML) offer promising non-invasive alternatives capable of analyzing complex datasets to identify patterns not easily discernible by human analysis. …”
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Comparative tribological and drainage performance of additively manufactured outsoles tread designs by Shuo Xu, Shuvodeep De, Meysam Khaleghian, Anahita Emami

“…A specialized friction testing machine was used to measure the friction force of the treads on a glass surface under dry and wet conditions. …”
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