Artificial neural networks for predicting first-lactation 305-day milk yield in crossbred cattle by SM Usman, A Kumar, A Sherasiya, IM Youssef, MM Abo Ghanima, J Chandrakar, R Tiwari, NP Singh, QS Sahib, T Dutt, AA Swelum

“…By enabling data-driven selection and culling decisions, the approach supports improved productivity, genetic progress, and economic efficiency in the South African livestock sector. …”
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Enhancing sentiment analysis in tourism reviews: A comparative study of algorithms in ASPECT-BASED SENTIMENT ANALYSIS and EMOTION DETECTION by Viktor Handrianus Pranatawijaya, Putu Bagus Adidyana Anugrah Putra, Ressa Priskila, Novera Kristianti

“…This research aims to combine Aspect-Based Sentiment Analysis (ABSA) and emotion detection for a more in-depth analysis of tourism reviews in Palangka Raya City and compare the performance of various algorithms. Review data was taken from Google Maps and analyzed using BoW, LDA, NRC Emotion Lexicon, machine learning, and deep learning algorithms such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Decision Tree (DT), and BERT. …”
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Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms by Binglin Song, Ping Liu, Kangrui Fu, Chun Liu

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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Compared with traditional methods—such as deep neural networks, support vector machines, and linear regression—the proposed model effectively integrates static and dynamic agricultural data. …”
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Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures by Mohammed Maree, Wala’a Shehada

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IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT by Sefri Imanuel Fallo, Lidia Paskalia Nipu

“…A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. …”
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Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms by Yuyang Chen, Jiansheng Zhong, Haixiang Li, Kunzhe Lin, Liangfeng Wei, Shousen Wang

“…Six machine learning algorithms were tested: logistic regression (LR), support vector classification (SVC), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). …”
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Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation by Yike Li, Mingyang Xiao, Yaqian Li, Lulu Lv, Shanshan Zhang, Yuhui Liu, Juan Zhang

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Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey by Manhui Zhang, Xian Xia, Qiqi Wang, Yue Pan, Guanyi Zhang, Zhigang Wang

“…We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. …”
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Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

“…The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Naive Bayes, Decision Trees (DT), and traditional Logistic Regression (LR). …”
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Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana by Frederick Osei Owusu, Helena Addai-Manu, Esther Serwah Agbedinu, Emmanuel Konadu, Lydia Asenso, Mercy Addae, Joseph Osarfo, Brenda Abena Ampah, Douglas Aninng Opoku

“…Methods This was a cross-sectional study that used retrospective data from medical records of pregnant women who delivered at a district hospital in Ghana. A clinical decision support system for predicting CS birth was developed using five machine learning techniques including logistic regression, Support Vector Machines, Naïve Bayes, Random Forest and Extreme Gradient Boosting. …”
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Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai by Mohammad H. Mehraban, Aljawharah A. Alnaser, Samad M. E. Sepasgozar

“…To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. …”
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Predicting response to anti-VEGF therapy in neovascular age-related macular degeneration using random forest and SHAP algorithms by Peng Zhang, Jialiang Duan, Caixia Wang, Xuejing Li, Jing Su, Qingli Shang

“…Eight machine learning methods, including Logistic Regression, Gradient Boosting Decision Tree, Random Forest, CatBoost, Support Vector Machine, XGboost, LightGBM, K Nearest Neighbors were employed to develop the predictive model. …”
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Investigation of Artificial Intelligence Techniques for the Management of Cataract Disease: A Systematic Review by Zahra Karbasi, Michaeel Motaghi Niko, Maryam Zahmatkeshan

“…The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. …”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

“…Six supervised machine learning models, random forest, gradient koosting, k-nearest neighbors, logistic regression, support vector machine, and decision tree, were trained and evaluated using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and SHapley Additive exPlanations value explanations. …”
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A Comparative Study of Loan Approval Prediction Using Machine Learning Methods by Vahid Sinap

“…In this context, the main objective of this research is to develop models for loan approval prediction using machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest and to compare their performances. …”
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Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing by Lifei Wang, Yucheng Gu, Xiaoqing Tian, Jun Wang, Yan Jia, Junjie Xu, Zhen Zhang, Shiying Liu, Shuo Liu

“…Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. …”
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An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer by Subrata Das, Vatsal Patel, Shouvik Chakravarty, Arnab Ghosh, Anirban Mukhopadhyay, Nidhan K. Biswas

“…Three machine learning algorithms—logistic regression, random forest, and support vector machine—along with recursive feature elimination, were used to rank these PCSAs. …”
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Comparative Analysis of Machine Learning Algorithms for Potential Evapotranspiration Estimation Using Limited Data at a High-Altitude Mediterranean Forest by Stefanos Stefanidis, Konstantinos Ioannou, Nikolaos Proutsos, Ilias Karmiris, Panagiotis Stefanidis

“…This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. …”
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Modeling sunflower yield and soil water–salt dynamics with combined fertilizers and irrigation in saline soils using APSIM and deep learning by Qingfeng Miao, Dandan Yu, Haibin Shi, Zhuangzhuang Feng, Weiying Feng, Zhen Li, José Manuel Gonçalves, Isabel Maria Duarte, Yuxin Li

“…Based on sunflower field experiments, four machine learning models (regression trees, random forest, support vector machines, and XGBoost) and two deep learning models (deep neural networks and neural networks) were developed to predict soil salinity. …”
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