Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. …”
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TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines by Sadam Hussain, Usman Naseem, Mansoor Ali, Daly Betzabeth Avendaño Avalos, Servando Cardona-Huerta, Beatriz Alejandra Bosques Palomo, Jose Gerardo Tamez-Peña

“…We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification. …”
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Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA by Alexander Seymour, Christine Kranenburg, Kara Doran

“…Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. …”
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Machine Learning-Assisted Hardness Prediction of Dispersion-Strengthened Tungsten Alloy by Shaowu Dai, Chong Chen, Cong Zhang, Shizhong Wei, Beibei Han, Changji Wang, Kunming Pan, Liujie Xu, Feng Mao, Hua Yu

“…SHAP analysis, based on random forests, shows that the content of reinforcement phase, grain size, and relative density have the most significant impact on the hardness. A random forest model is the most suitable machine learning method for predicting the hardness of dispersion-strengthened tungsten alloys in this work. …”
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Socioeconomic status and lifestyle as factors of multimorbidity among older adults in China: results from the China Health and Retirement Longitudinal Survey by Wei Gong, Wei Gong, Wei Gong, Xiaoxiao Hu, Huimin Cui, Huimin Cui, Yuxin Zhao, Yuxin Zhao, Hong Lin, Hong Lin, Hong Lin, Peng Sun, Peng Sun, Jianjun Yang, Jianjun Yang

“…Eight machine learning algorithms including logistic regression, decision tree, naive Bayes, neural network, support vector machine, random forest, XGBoost and Bayesian Ridge Regression were applied to build predictive models. …”
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Disulfidptosis-Related Genes as Novel Biomarkers and Therapeutic Targets in Dilated Cardiomyopathy by Fan J, Xu S, Zeng Y, Xu A, Wang X, Wang H, Kang P, Fu Y, Fan H, Zhou Y

“…Consensus clustering divided DCM patients into two subtypes with distinct immune profiles. The support vector machine (SVM) model demonstrated superior diagnostic performance (AUC = 0.983), identifying ACTN4, MYH10, TLN1, DSTN, and NCKAP1 as core predictors. …”
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A BOOK AND READING AS FORMS OF CULTURAL MEMORY PRODUCTION by S. A. Vikentyeva, В. V. Маrkov

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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch... by Shaolin Chen, Ting Deng, Qing Yang, Jin Li, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma

“…The stacking ensemble combining Support Vector Machine (SVM) and Decision Tree (DT) showed the highest overall performance, with an AUROC of 0.860 (95% CI: 0.809–0.911), and DCA showed higher clinical utility compared to other models. …”
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Open-Phase Fault Tolerant Model Predictive Current Controller for Asymmetrical Dual Three-Phase Permanent Magnet Synchronous Machine Drive System by Adriano Navarro-Temoche, Josu Jugo, Edorta Ibarra, Inigo Kortabarria, Endika Robles

“…As an alternative, a novel model predictive resonant controller (MPRC) is proposed in this work for fault tolerant operation. …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…We have also designed a hybrid model which involves the combination of Recursive Feature Elimination (RFE) for feature selection, SMOTE-ENN to tackle the problem of data imbalance and Ensemble learning for enhanced predictions.ResultsThe research work also proposed Hybrid Ensemble model on the ILPD and BUPA Liver Disorder Dataset. …”
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A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid by Camille Franklin Mbey, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum

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Methodological Tools for Diagnosing Insolvency (Bankruptcy) of Organizations in the Anti-Crisis Management System by Chernichenko Svetlana G., Kotov Roman M.

“…A comparative analysis of bankruptcy criteria and indicators made it possible to define the degree of adequacy of the set of indicators. Four analytical vectors were defined after thematic grouping of the identified indicators: balance sheet liquidity (current liquidity ratio), property and capital structure (financial dependence and asset mobility ratios), security (working capital ratio with own circulating assets), efficiency (economic profitability, or loss ratio, and the ratio of business activity in the market). …”
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Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics by Song C, Chen J, Zhao C, Song S, Yang T, Huang A, Liu R, Pan Y, Xu C, Chen C, Zhu Q

“…The Support Vector Machine (SVM) model demonstrated the most balanced and optimal performance among the seven developed models. …”
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Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Eval... by Antonio García-Domínguez, Carlos E. Galván-Tejada, Rafael Magallanes-Quintanar, Hamurabi Gamboa-Rosales, Irma González Curiel, Jesús Peralta-Romero, Miguel Cruz

“…Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. …”
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Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model by Lin Zhu, Benao Xing, Xingbao Li, Min Chen, Minping Jia

“…The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra, in comparison with support vector machine (SVM), random forest (RF) model and back propagation (BP) neural network. …”
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A method for identifying relevant topics of pilot simulator training based on clustering of flight safety reports by Z. R. Zabbarov, A. K. Volkov

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Data-driven assessment of climate change and vegetative cover dynamics in traditional oases by Elisa Baioni, Giulia Fiantanese, Giovanni Michele Porta

“…We then assess the feedback between climate and vegetation cover at monthly and yearly scale through multivariate analyses based on vector autoregression (VAR) and vector error correction (VEC) models. …”
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Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield by Rahul Kumar, Ashok Kumar, Karamjit Bhatia, Kottakkaran Sooppy Nisar, Siddharth Singh Chouhan, Priti Maratha, Anoop Kumar Tiwari

“…” These models include Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. …”
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Darkonia at colliders by Yang Bai, Susanne Westhoff

“…Searches for dark matter at colliders can differ dramatically from routine searches if bound states, dubbed darkonia, are produced and decay into visible Standard-Model particles. In this work, we use three representative models with scalar, pseudo-scalar, and vector force carriers to map out the darkonium signatures at both high-energy and low-energy colliders. …”
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Angiogenesis as a Survival Mechanism in Heartworm Disease: The Role of Fructose-Bisphosphate Aldolase and Actin from <i>Dirofilaria immitis</i> in an In Vitro Endothelial Model by Manuel Collado-Cuadrado, Claudia Alarcón-Torrecillas, Alfonso Balmori-de la Puente, Iván Rodríguez-Escolar, Elena Infante González-Mohino, Miguel Pericacho, Rodrigo Morchón

“…Heartworm disease, caused by <i>Dirofilaria immitis,</i> is a vector-borne zoonotic disease, (mainly affecting canids and felids) causing chronic vascular and pulmonary pathology in its early stages, which worsens with parasite load and/or death of adult worms in the pulmonary artery or right heart cavity, and can be fatal to the host. …”
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