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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods
Published 2025-01-01“…Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. …”
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Missing values imputation using Fuzzy K-Top Matching Value
Published 2023-01-01Get full text
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GATCGGenerator: New Software for Generation of Quasirandom Nucleotide Sequences
Published 2023-09-01Get full text
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Robotic Hand–Eye Calibration Method Using Arbitrary Targets Based on Refined Two-Step Registration
Published 2025-05-01Get full text
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Multi-objective Windy Postman Problem in a Fuzzy Transportation Network
Published 2025-07-01Get full text
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Machine Learning in the National Economy
Published 2025-07-01“…The main methods include an analysis of scientific literature, statistical data analysis, modeling using machine learning algorithms, and practical implementation of economic models with programming languages such as Python and machine learning libraries.To analyze economic data, methods such as linear regression, decision trees, and neural networks were selected, as they effectively predict changes in key macroeconomic indexes such as GDP, inflation, exchange rates, and unemployment levels. …”
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Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data
Published 2025-08-01“…Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph
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House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning
Published 2022-12-01“…It is one of the main contribution of the work is that through this the house prediction model based on DHA Karachi data is developed and as per best of our knowledge till today there is no prediction of housing for the country’s important has been developed. has This research paper mainly focuses on real time Defense Housing Authority (DHA) Karachi data, applying different regression algorithms like Decision tree, Random forest and linear regression to find the sales price prediction of the house and compare the performance of these models. …”
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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms
Published 2025-07-01“…In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. …”
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Comparison of machine learning models for coronavirus prediction
Published 2022-03-01Get full text
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A Comparative Study of Loan Approval Prediction Using Machine Learning Methods
Published 2024-06-01“…Machine learning models can automate this process and make the lending process faster and more efficient. 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 Model for Detecting Attack in Service Supply Chain
Published 2025-06-01“…The study employs machine learning methods to increase the detection of service supply chain attacks, including Decision Trees, Random Forest, and XGBoost algorithms. These models were assessed in accordance with accuracy, precision, recall, and the F1-score, with Random Forest topping the list with an accuracy of 96.1%, followed by Decision Trees with 95.0% accuracy and XGBoost with 94.7% accuracy. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank
Published 2021-01-01“…Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. …”
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An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia
Published 2025-06-01“…Based on the training algorithm and after 146 periods, the training error decreased, but the validation error increased (Fig. 7). …”
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