Locally rendered high-resolution Land Use/Land Cover Bitmaps from OpenStreetMap Data for geospecific 3D Simulation by D. Frommholz

“…The automatic workflow utilizes free software and open formats, does not require web services nor graphical GIS applications and can be run by novice cartographers. In the process, offline vector data sets from the collaborative mapping project are prepared into self-contained transferable database files that arrange relevant geometric features and attributes as spatially indexed tables for fast access. …”
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Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods by Ahmet Burak Tatar

“…Within this framework, prediction models including Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR), and Multi-Layer Perceptron (MLP) were developed, and their performances were assessed using metrics such as accuracy (R²), error rates (RMSE, MSE, MAE), and computational time. …”
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Constructing a predictive model of negative academic emotions in high school students based on machine learning methods by Shumeng Ma, Ning Jia, Xiuchao Wei, Wanyi Zhang

“…We applied various machine learning models, such as logistic regression, naive Bayes, support vector machine, decision tree, random forest, gradient boosting decision tree, and adaptive boosting, to analyze the students’ negative academic emotions. …”
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The genesis of the understanding of the right to apply to the court (the right to suit) in civil proceedings by Olena Shtefan

“…The conducted research made it possible to conclude that the main research vector of the concept of ’’right to appeal to court’’ (’’right to sue’’) was formed by lawyers in Ancient Rome. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. …”
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Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction by Chaoyu Shi, Bohao Chen, Yao Shi, Jun Zha

“…Grinding, as the most crucial finishing process for bearing rings, influences the surface integrity of bearings through the roughness of the ground surface. …”
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Statistical contextual explanation of quantum paradoxes by Marian Kupczynski

“…“State vector” describes an ensemble of identically prepared physical systems, and a specific “operator” represents a class of equivalent measurements of a physical observable. …”
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A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs by Ahmed Alabd-Aljabar, Zain Raisan, Mohammed Adnan, Salam Dhou

“…The rise in the emphasis on oral diseases has elevated the need to automate the diagnostic process of such diseases. Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. …”
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RGB imaging-based detection of rice leaf blast spot and resistance evaluation at the canopy scale by XIE Pengyao, FU Haowei, TANG Zheng, MA Zhihong, CEN Haiyan

“…Among all the classification models, Gaussian process support vector machine obtained the highest prediction accuracy of 94.30% (proportion of disease spots in each category corresponding to different resistances) on the test dataset. …”
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Development of a machine learning-based surrogate model for friction prediction in textured journal bearings by Yujun Wang, Georg Jacobs, Shuo Zhang, Benjamin Klinghart, Florian König

“…Furthermore, three ML methods are trained and compared to select the most suitable prediction method: artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GPR). …”
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Application of machine learning algorithms in predicting pyrolytic analysis result by Thi Nhut Suong Le, A. V. Bondarev, L. I. Bondareva, A. S. Monakova, A. V. Barshin

“…However, extraction is a laborious and time-consuming process, and the load on laboratory equipment and the time required for analysis is doubled.Aim. …”
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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

“…., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). …”
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Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays by Rozita Konstantinou, Ihsan Kanbaz, Okan Yurduseven, Michail Matthaiou

“…We propose a novel approach for boosting the realized gain of arrays with enhanced directivity, utilizing both active and parasitic dipoles. The optimization process first maximizes the end-fire gain in the active array by selecting the optimal current excitation vector. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Various machine learning methods were evaluated, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting Machines, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Gaussian Processes, as well as ensemble algorithms such as XGBoost, LightGBM, and CatBoost. …”
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Cloning and sequence analysis of myogenin gene in wild boar by XU Huai-liang, YAO Yong-fang, ZHU Qing, PAN Yang

“…The PCR products were ligated into the pMD-18T vector, and then transformed into competent cells of E. coli DH5a. …”
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Egg Microinjection for the Silkworm Bombyx mori by Hayato Yamada, Toshinobu Yaginuma, Keisuke Mase, Teruyuki Niimi

“…Conventional methods involve arranging eggs, pre-pierced with a tungsten needle, followed by solution injection, making the process both time-consuming and technically demanding. …”
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Machine learning in subsurface physical properties and lithofacies prediction in a mining context by A. Balaguera, M. Torné, R. Carbonell, A. Martí, J. Vergés, M. J. Jurado, P. Sánchez-Pastor, A. Farci, D. Davoise, S. Rodríguez

“…A robust quality control process, aided by Machine Learning models (ML), refined the PPR data, ensuring accuracy and reliability. …”
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A model for tobacco growing area classification based on time series features of thermogravimetric analysis by Jiaxu Xia, Yunong Tian, Xianwei Hao, Yuhan Peng, Guanqun Luo, Zhihua Gan

“…Despite these challenges, the model achieves 86.4% accuracy on the test set, significantly surpassing the performance of the traditional Support Vector Machine model, which only achieves 68.2% accuracy. …”
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Network analysis of aging acceleration reveals systematic properties of 11 types of cancers by Xiaoqiong Xia, Mengyu Zhou, Hao Yan, Sijia Li, Xianzheng Sha, Yin Wang

“…The DNAm aging scores were calculated using the Support Vector Machine regression model. The DNAm aging scores of cancers revealed significant aging acceleration compared to adjacent normal tissues. …”
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A Developed LSTM-Ladder-Network-Based Model for Sleep Stage Classification by Ruichen Li, Bei Wang, Tao Zhang, Takenao Sugi

“…Several features are extracted for each epoch and combined with the following epochs to form a cross-epoch vector. The long short-term memory (LSTM) network is added into the basic ladder network (LN) to learn the sequential information of adjacent epochs. …”
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