Dataset collection for automatic generation of commit messages by Ivan A. Kosyanenko, Roman G. Bolbakov

“…The research involved text vectorization of commit messages and evaluation of semantic similarity between the first sentences and full texts of messages using cosine similarity. …”
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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

“…The research compared the prediction accuracy from three solutions: Data Augmentation, Transfer Learning, and the mixing of both methods. The classification models employed in this study were four algorithms: Random Forest, Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Conclusions This study highlights the critical role of identifying risk factors for carotid atherosclerosis in LADA patients. Our use of ML models builds on the growing body of work in diabetes-related cardiovascular risk prediction, and it offers a novel approach by specifically targeting the LADA population. …”
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Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers by Vijoleta Vrhovac, Marko Orošnjak, Kristina Ristić, Nemanja Sremčev, Mitar Jocanović, Jelena Spajić, Nebojša Brkljač

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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…Through training and meticulous hyperparameter tuning, this study compares 7 different AI models fed with experimental data from 21 thrusters and their different configurations, reaching a total of 58 thruster designs, spanning decades of thruster research and development work. …”
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A GIS Approach to Modeling the Ecological Niche of an Ecotype of <i>Bouteloua curtipendula</i> (Michx.) Torr. in Mexican Grasslands by Alma Delia Baez-Gonzalez, Jose Miguel Prieto-Rivero, Alan Alvarez-Holguin, Alicia Melgoza-Castillo, Mario Humberto Royo-Marquez, Jesus Manuel Ochoa-Rivero

“…The challenge for the present study was that only one georeferenced collection site of the ecotype in Chihuahua was available for use in the construction and calibration of the models. GIS software 10.3 was used to develop two potential distribution models: Model A, with variables obtained directly from a vector climate dataset, and Model B, with derived variables. …”
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Comparative Study of Pathomorphological Changes in Animal Lung Tissue when Modeling Infection Caused by the SARS-CoV-2 Virus by A. V. Shipovalov, G. A. Kudrov, E. K. Ivleva, V. V. Omigov, O. V. P’yankov, O. S. Taranov

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Drying of Nettle Using Concentrated Air Collector and Concentrated Photovoltaic Thermal Supported Drying System and Modeling with Machine Learning by Mehmet Onur Karaagac

“…The data obtained from the drying system were modelled using machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and gradient boosting decision trees (GBDT). …”
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A mineral-strength conversion model based on LIBS technology and rapid batch testing and application of uniaxial compressive strength by Qinghe ZHANG, Xiaorui WANG, Chuanbing WANG, Weiguo LI, Fengqiang GONG, Xinsheng ZHANG, Jingguo LI

“…The mass fraction of mineral components is analyzed via the support vector regression (SVR) algorithm. In the end, a mineral-strength conversion model is established to calculate the UCS from the predicted values of mineral component concentrations, and its rationality and scientific validity are validated by the standard mechanical tests. …”
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Estimation and diagnostic for single-index partially functional linear regression model with $ p $-order autoregressive skew-normal errors by Lijie Zhou, Liucang Wu, Bin Yang

“…This paper introduced a novel single-index partially functional linear regression model with $ p $-order autoregressive skew-normal errors, addressing the dual challenges of autocorrelation and skewness in high-dimensional functional data. …”
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Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model. by Arezoo Bozorgmehr, Anika Thielmann, Birgitta Weltermann

“…Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders.…”
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Adaptive deep SVM for detecting early heart disease among cardiac patients by S. N. Netra, N. N. Srinidhi, E. Naresh

“…Later, various experiments are performed in the recommended heart disease detection model over existing models to verify its effectiveness. …”
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Searching for hadronic scale baryonic and dark forces at (g − 2) μ ’s lattice-vs-dispersion front by Kaustubh Agashe, Abhishek Banerjee, Minyuan Jiang, Shmuel Nussinov, Kushan Panchal, Srijit Paul, Gilad Perez, Yotam Soreq

“…This test is particularly sensitive to hadronic scale new physics. Therefore, in this work, we consider an SM extension consisting of a generic, light ~ (100 MeV – 1 GeV) vector boson and study its impact on both tests. …”
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Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…Aim: The aim of this study was to gauge the condition of otoacoustic emissions (OAEs) in workers, followed by modeling and estimating the weight of factors affecting changes in their emissions. …”
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Mean-reverting self-excitation drives evolution: phylogenetic analysis of a literary genre, waka, with a neural language model by Takuma Tanaka

“…Phylogenetic networks were constructed on the basis of the vector representation obtained using a neural language model. …”
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Inverse Optimal Output Regulation for a Class of Uncertain Nonlinear Systems by MENG Guizhi, LÜ Yan

“… For a class of nonlinear systems with unknown parameter vector and disturbance, the inverse optimal output regulation problem driven by a linear exosystem is researched. …”
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Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease by Priyadharsini C, Asnath Victy Phamila Y

“…Globally, a substantial population is at risk of vision impairment, prompting researchers to investigate methods for efficient classification. Methods: This work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). …”
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Research on the evolution and prediction of the heights of water-conducting fracture zones in overlying rocks during layered mining of extremely thick coal seams by MENG Hailun, CHENG Xianggang, QIAO Wei

“…A prediction model was developed for the heights of water-conducting fracture zones in layered mining of extremely thick coal seams based on particle swarm optimization support vector machine regression (PSO-SVR). …”
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Careful design of Large Language Model pipelines enables expert-level retrieval of evidence-based information from syntheses and databases. by Radhika Iyer, Alec Philip Christie, Anil Madhavapeddy, Sam Reynolds, William Sutherland, Sadiq Jaffer

“…A hybrid retrieval strategy that combines keywords and vector embeddings performed best by a substantial margin. …”
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