Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis by Subhrajit Roy, Diana Mincu, Lev Proleev, Chintan Ghate, Jennifer S. Graves, David F. Steiner, Fletcher Lee Hartsell, Katherine Heller

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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

“…Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. …”
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Spatial Clusters of Gambling Outlet: A Machine Learning Tree-Based Algorithm by Salvador Martínez-Cava, Fernando A. López, MLuz Maté Sánchez-del-Val

“…The results reveal different spatial patterns regarding the locations of these two types of gambling establishments. …”
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A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment by Naveed Ejaz, Salimur Choudhury

“…It highlights the integration of diverse data sources, including remote sensing, in-situ measurements, geospatial layers, and historical fire records and outlines pre-processing and feature engineering techniques to represent climatic, topographic, vegetation, anthropogenic, and temporal fire patterns. The paper categorizes a wide array of machine learning techniques applied in wildfire risk assessment, including traditional, deep learning, spatial, temporal, reinforcement learning, and hybrid approaches. …”
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Synthesis and Functional Optimization of a Vibratory Machine with a Parallel Mechanism Structure by Mircea-Bogdan Tătaru, Alexandru Rus, Tiberiu Vesselényi, Mariana Raţiu, Ioan Ţarcă

“…Vibrations are used in this case to increase the technological speed of separation of these materials. Vibratory machines are limited in functioning to fixed oscillation patterns. …”
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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…This research aimed at applying machine learning models to improve reliability in the development of social science courses. …”
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Early Warning Systems for Plant Diseases in delta regions: Machine Learning Approaches by Biswas Debarghya, Sharma Priti

“…Some patterns and anomalies can indicate the onset of plant diseases, and the algorithms are trained to recognize them. …”
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A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems by Asli Kaya Karakutuk, Ozer Ozdemir

“…In this paper, we introduce a basic method that combines the strengths of fuzzy logic, wavelet theory, and kernel-based extreme learning machines to efficiently classify facial expressions. …”
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Unmasking Machine Learning With Tensor Decomposition: An Illustrative Example for Media and Communication Researchers by Yu Won Oh, Chong Hyun Park

“…This study illustrates tensor decomposition—specifically, PARAFAC2—for media scholars as an interpretable machine learning method for analyzing high-dimensional communication data. …”
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Vertical distribution and variability of soil organic carbon and CaCO3 in deep Colluvisols modeled by hyperspectral imaging by Jessica Reyes-Rojas, Julien Guigue, Daniel Žížala, Vít Penížek, Tomáš Hrdlička, Petra Vokurková, Aleš Vaněk, Tereza Zádorová

“…A variety of nonlinear machine learning techniques such as cubist regression tree (Cubist), random forest (RF), support vector machine regression (SVMR) and one linear technique partial least square regression (PLSR) were compared to determine the most suitable model for the prediction of SOC and CaCO3 content in each profile. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…Moreover, the research conducts a comparative evaluation of various machine learning models to identify the most effective algorithms for different climatic zones and crop types. …”
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Water resource forecasting with machine learning and deep learning: A scientometric analysis by Chanjuan Liu, Jing Xu, Xi’an Li, Zhongyao Yu, Jinran Wu

“…Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging themes, this study analyzed 876 articles published between 2015 and 2022, retrieved from the Web of Science database. …”
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Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey by Ahmed Sharafeldeen, Justin Keowen, Ahmed Shaffie

“…Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential for detecting AD using patient’s speech signals, as subtle changes in speech patterns, such as reduced fluency, pronunciation difficulties, and cognitive decline, can serve as early indicators of the disease, offering a non-invasive and cost-effective method for early diagnosis. …”
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Study Comparison Deep Learning and Support Vector Machine for Face Mask Detection by Rani Kurnia Putri, Muhammad Athoillah*

“…Both algorithms have proven to be powerful tools for any classification problem specially to classify or identify image patterns. However, the performance of machine learning algorithms can be affected by any factor, thus sometimes we found several algorithms that are generally known to be powerful, even showing unsatisfactory results. …”
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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia by Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis

“…These findings emphasize the critical need for enhanced fraud detection frameworks, leveraging machine learning algorithms like Random Forest to identify fraud patterns effectively. …”
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Validation of sleep-based actigraphy machine learning models for prediction of preterm birth by Benjamin C. Warner, Peinan Zhao, Erik D. Herzog, Antonina I. Frolova, Sarah K. England, Chenyang Lu

“…With the advent of smartwatches, it is possible to obtain more reliable and accurate sleep data, which can be utilized to evaluate the impact of specific sleep behaviors in concert with machine learning. We evaluate motion actigraphy data collected from a cohort of participants undergoing pregnancy, and train several machine learning models based on aggregate features engineered from this data. …”
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A quantum inspired machine learning approach for multimodal Parkinson’s disease screening by Diya Vatsavai, Anya Iyer, Ashwin A. Nair

“…For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. …”
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Near viewing behaviors predict educational system in a machine learning model by Ravid Doron, Einat Shneor, Lisa A. Ostrin, Ariela Gordon-Shaag, Ayelet Goldstein

“…Compared to standard school students, intensive school students had significantly more myopic refraction (P < 0.03), spent more time viewing very near distances (P < 0.004) and less time viewing intermediate distances (P < 0.008) and had shorter near-viewing distances (P < 0.0001). Machine learning identified far-viewing episodes > 5 min and viewing distance during near-viewing as predictors of educational background.These findings suggest that educational environments are associated with distinct visual behavior patterns that may be linked to refractive development. …”
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Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks by Mahmut Türk, Heybet Kılıç, Cem Haydaroglu

“…In addition, from the classical methods, 67.5 accuracy rate was obtained with the k-Nearest Neighbor (k-NN) method and 62.25 accuracy rate was obtained with the Support Vector Machines (SVM) method. Comparisons with such traditional methods have revealed the superiority of CNN in determining complex leakage patterns. …”
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Using machine learning to forecast conflict events for use in forced migration models by Yani Xue, Thomas Schincariol, Thomas Chadefaux, Derek Groen

“…Accurate predictions of displacement patterns are crucial for improving the delivery of aid to refugees and other forcibly displaced individuals. …”
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