Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm by Kharisma Gunanta Ginting, Nugroho Prasetyo, Al Vino Gunawan, Magdalena Sihombing, Adli Abdillah Nababan

“…Visualization of the relationships between numerical features also reveals significant correlation patterns between physiological variables and patient anxiety levels. …”
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Predicting User Purchases From Clickstream Data: A Comparative Analysis of Clickstream Data Representations and Machine Learning Models by A. Aylin Tokuc, Tamer Dag

“…Unlike prior research, which typically focuses on single representations, our approach combines aggregated session-level summaries with granular, sequential user actions to capture both long-term and short-term behavioral patterns. Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. …”
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Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery by Frederick M. Lang, Benjamin C. Lee, Dor Lotan, Mert. R. Sabuncu, Veli K. Topkara

“…By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. …”
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Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems by Vasilios Plakandaras, Fahad Khan Khadim, Vassiliki Kazana, Emmanouil Anagnostou, Amvrossios C Bagtzoglou

“…Harnessing the analytical power of machine learning models in extracting patterns from data, the informational content of social surveys coupled with hydrological data for the survey region from a calibrated MODFLOW-NWT groundwater (GW) model, we draw inferences on the importance of socioeconomic rather than hydrological variables as drivers in agricultural decisions about crop selection and planting period, underscoring those factors as potential criteria in drawing successful agricultural policies for crop yield optimization in the Great South area. …”
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A Deep Learning Model Leveraging Time-Series System Call Data to Detect Malware Attacks in Virtual Machines by A. Alfred Raja Melvin, Jaspher W. Kathrine, Andrew Jeyabose, D. Cenitta

“…The raw VMM system call traces are transformed into novel Time Series System Call patterns and utilized by a deep learning algorithm for training and building the classifier model. …”
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Analisis Visual dan Machine Learning untuk Mengukur Validitas Dokumen Akademik Perpustakaan: Studi pada Data Turnitin by Hesti Ari Wardani, Imam Yuadi

“…This study provides in-depth insights into the patterns of academic document validity and offers a data-driven approach to improving the quality of academic evaluation in higher education. …”
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Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models by Farzaneh Ansari, Ali Neshasteh-Riz, Reza Paydar, Fathollah Mohagheghi, Sahar Felegari, Manijeh Beigi, Susan Cheraghi

“…We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. …”
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Predicting biking preferences in Kigali city: A comparative study of traditional statistical models and ensemble machine learning models by Jean Marie Vianney Ntamwiza, Hannibal Bwire

“…Feature importance indicated that day and month are critical factors in bike preference prediction, reflecting significant daily and seasonal patterns. Air quality factors (high ozone and PM2.5) and weather factors (temperature and rainfall) impacted the preferences. …”
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Social media interaction and built environment effects on urban walking experience: A machine learning analysis of Shanghai Citywalk. by Xingrui Chen, Yu Sun, Filzani Illia Binti Ibrahim, Myzatul Aishah Binti Kamarazaly, Siti Norzaini Binti Zainal Abidin, Suqiu Tang

“…Additionally, spatial autocorrelation analysis of emotional scores reveals spatial clustering patterns, underscoring the critical role of interactions between virtual social behavior and physical spatial elements in emotional generation. …”
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A Novel Hybrid Model for Loan Default Prediction in Maritime Finance Based on Topological Data Analysis and Machine Learning by Mohammad Amin Kheneifar, Babak Amiri

“…This study proposes a novel framework integrating topological data analysis (TDA) and machine learning (ML) to enhance default prediction in maritime finance. …”
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Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm by Ting Liu, Kai Liu, Wuyi Luo, Jiange Kou, Haoran Zhan, Guangkai Yu, Qing Guo, Yan Shi

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End-Region Losses in High-Power Electrical Machines: Impact of Material Thickness on Eddy Current Losses in Clamping Structures by Walid Mohand Oussaid, Abdelmounaïm Tounzi, Raphaël Romary, Abdelkader Benabou, Walid Boughanmi, Daniel Laloy

“…High-power electrical machines often utilize clamping structures composed of various materials with specific geometric dimensions to secure the stator laminations. …”
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Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery by Ratovoson Robert Andriambololonaharisoamalala, Petra Helmholz, Dimitri Bulatov, Ivana Ivanova, Yongze Song, Susannah Soon, Eriita Jones

“…Satellite-derived land surface temperature (LST) plays a vital role in analysing urban thermal patterns. However, current satellite thermal infrared (TIR) sensors have a low spatial resolution, making it difficult to accurately capture the complex thermal variations within urban areas. …”
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Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

“…We ranked differentially expressed genes (DEGs) using feature-weighting algorithms, such as Relief and kernel-based methods, to model expression patterns between sensitive and tolerant cultivars. …”
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Developing an Urban Landscape Fumigation Service Robot: A Machine-Learned, Gen-AI-Based Design Trade Study by Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Prabakaran Veerajagadheswar, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. …”
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Prediction of Voice Therapy Outcomes Using Machine Learning Approaches and SHAP Analysis: A K-VRQOL-Based Analysis by Ji Hye Park, Ah Ra Jung, Ji-Na Lee, Ji-Yeoun Lee

“…SHAP analysis reveals interpretable patterns, highlighting jitter and speaking fundamental frequency (SFF) as key contributors in high-performing models. …”
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A Comparative study on the impact of urbanisation on microclimate in Cairo (Egypt) and London (UK) using remote sensing and Machine Learning by L. Sabobeh, T. Ali, M. Md. Mortula

“…Several machine learning (ML) algorithms were compared, with Support Vector Machine (SVM) ultimately selected for its superior performance. …”
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Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning by Zhou An, Meichun Zeng, Xianhua Wang

“…Immune infiltration analysis revealed distinct patterns of immune cell infiltration in malignant LNs compared to those in benign controls. …”
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Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems by Dinesh Kumar Nishad, Vandna Rani Verma, Pushkar Rajput, Sandeep Gupta, Anurag Dwivedi, Dharti Raj Shah

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A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things by M. Wasim Abbas Ashraf, Arvind R. Singh, A. Pandian, Rajkumar Singh Rathore, Mohit Bajaj, Ievgen Zaitsev

“…Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. …”
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