Predicting Financial Market Volatility with Modern Model and Traditional Model by R. G. Aldeki

“…The major topic investigates how classical methods (ARCH and GARCH) and well-known machine learning algorithms, support vector regression, and hybrid methods. …”
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Multi-Level Depression Severity Detection with Deep Transformers and Enhanced Machine Learning Techniques by Nisar Hussain, Amna Qasim, Gull Mehak, Muhammad Zain, Grigori Sidorov, Alexander Gelbukh, Olga Kolesnikova

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Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models by B. R. Kavya, A. S. Shrikanth, K. S. Sreekeshava

“…In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. …”
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Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

“…Based on these methods, CHD predictive models were constructed using five different algorithms: K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), Decision Tree, and XGBoost. …”
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Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques by Balliu Lorena, Zanaj Blerina, Basha Gledis, Zanaj Elma, Meçe Elinda Kajo

“…It includes the analysis of different algorithms such as Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and hybrid models. …”
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Anomaly detection using machine learning and adopted digital twin concepts in radio environments by Mohamed Hussien Moharam, Omar Hany, Ahmed Hany, Amenah Mahmoud, Mariam Mohamed, Sohila Saeed

“…XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81). …”
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Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran by Mohammad Najafi, Yousef Shiri

“…In this study, five methodologies, including three artificial intelligence models (artificial neural networks [ANNs], support vector regression [SVR], random forest [RF]), a physical model, and a hybrid model, were evaluated for their ability to estimate the ROP on the basis of drilling data from a complex lithological area. …”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

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IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS by Rodolphe Nsimba Malumba, Mardochee Longo Kayembe, Fiston Chrisnovic Balanganayi Kabutakapua, Bopatriciat Boluma Mangata, Trésor MAZAMBI KILONGO, Rufin Tabiaki Tandele, Emmanuel Ntanyungu Ndizieye, Parfum Bukanga Christian

“…The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). …”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…ObjectiveThis study aimed to develop a risk prediction model for post-treatment oligometastasis in nasopharyngeal carcinoma (NPC) by integrating pathomics features and an improved Support vector machine (SVM) algorithm, offering precise early decision support.MethodsThis study retrospectively included 462 NPC patients, without or with oligometastasis defined by ESTRO/EORTC criteria. …”
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The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses by Liu Jun, Hongguo Li, Yu Mao, Lan Hu, Dan Wu

“…The Barthel Index (BI) was used to assess ADL, and the correlation between ADL and SI was examined through statistical analyses. Machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression) were employed to validate the findings and elucidate the underlying relationship between ADL and SI.BackgroundSI poses significant challenges to the health and quality of life of middle-aged and older adults, increasing the demands on community-based and home care services. …”
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RU-OLD: A Comprehensive Analysis of Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning, Deep Learning, and Transformer Models by Muhammad Zain, Nisar Hussain, Amna Qasim, Gull Mehak, Fiaz Ahmad, Grigori Sidorov, Alexander Gelbukh

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Revolutionizing Prenatal Care: Harnessing Machine Learning for Gestational Diabetes Anticipation by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Bruhathi Sathyanarayanan, Kabilan Saravanan

“…Diverse algorithms were tested to compare their accuracies with the complexities of data: K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), and decision tree (DT). …”
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Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach by Ghazi Al-Khateeb, Ali Alnaqbi, Waleed Zeiada

“…Various machine learning techniques, encompassing linear regression, decision trees, support vector machines, ensemble methods, Gaussian process regression, artificial neural networks, and kernel-based approaches, are compared. …”
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Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan by Farnaz, Nuthammachot Narissara, Shabbir Rabia, Iqbal Benazeer

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Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

“…Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…The models evaluated include Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regression (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Lasso, and k-Nearest Neighbors (KNN). …”
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FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

“…This study employs various machine learning algorithms, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes, to assess their accuracy in predicting cardiovascular disease and related conditions This paper makes use of the UCI repository dataset for coaching and testing including some basic parameters such as age and sex. …”
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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. …”
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Intelligent Path Tracking for Single-Track Agricultural Machinery Based on Variable Universe Fuzzy Control and PSO-SVR Steering Compensation by Huanyu Liu, Zhihang Han, Junwei Bao, Jiahao Luo, Hao Yu, Shuang Wang, Xiangnan Liu

“…Additionally, a heading deviation prediction model based on Support Vector Regression (SVR) optimized by Particle Swarm Optimization (PSO) is introduced, and a steering angle compensation controller is designed to improve the turning accuracy. …”
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