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| 1 | Strategic Information Management 显示文摘 | Michal Gregus Eleonora Benova | 2006 | E-Leader2006，，： | 1 |
| 2 | An Ensemble Methods for Medical Insurance Costs Prediction Task显示文摘The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs.The open dataset is used for data analysis methods development.The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’health.Machine learning is associated withmany expectations,but its quality is determined by choosing a good algorithm and the proper steps to plan,develop,and implement the model.The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy.Pierson coefficient and Boruta algorithm are used for feature selection.The boosting,stacking,and bagging ensembles are built.A comparison with existing machine learning algorithms is given.Boosting modes based on regression tree and stochastic gradient descent is built.Bagged CART and Random Forest algorithms are proposed.The boosting and stacking ensembles shown better accuracy than bagging.The tuning parameters for boosting do not allow to decrease the RMSE too.So,bagging shows its weakness in generalizing the prediction.The stacking is developed using K Nearest Neighbors(KNN),Support Vector Machine(SVM),Regression Tree,Linear Regression,Stochastic Gradient Boosting.The random forest(RF)algorithm is used to combine the predictions.One hundred trees are built forRF.RootMean Square Error(RMSE)has lifted the to 3173.213 in comparison with other predictors.The quality of the developed ensemble for RootMean Squared Error metric is 1.47 better than for the best weak predictor(SVR). | Nataliya Shakhovska Nataliia Melnykova Valentyna Chopiyak Michal Gregus ml | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 3 | Regarding the Optical Properties of Porous Layers Prepared on Si Substrates显示文摘 | Emil Pincik Robert Brunner Hikaru Kobayashi Pavel Vojtek Zuzana Zabudla Milan Mikula Jan Gregus Michal Kucera | 2017 | Journal of Energy and Power Engineering2017，11，11： | 0 |
| 4 | Decoding of Factorial Experimental Design Models Implemented in Production Process显示文摘The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independent variables,it is necessary to take into account the range covered by each.A wide range of choices of different variables is presented in this paper.After calculating the regression model,its variables must be returned to their original values for the model to be easy recognized and represented.In the paper,the procedures of simple first order models,with interactions and with second order models,are presented,which could be a very complicated process.Models without and with the mutual influence of independent variables differ.The encoding and decoding procedure on a model with two independent first-order parameters is presented in details.Also,the procedure of model decoding is presented in the experimental surface roughness parameters models’determination,in the face milling machining process,using the first and second order model central compositional experimental design.The simple calculation procedure is recommended in the case study.Also,a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper. | Borislav Savkovic Pavel Kovac Branislav Dudic Michal Gregus | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 5 | Methods for the Efficient Energy Management in a Smart Mini Greenhouse显示文摘To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption.One of the possible ways is proposed in this research.A multi-level hierarchical system for energy efficiency management of the enterprise is designed,it is based on the modular principle providing rapid modernization.The novelty of the work is the development of new and improvement of the existing methods and models,in particular:1)models for dynamic analysis of IT tools for data acquisition and processing(DAAP)in multilevel energy management systems,which are based on Petri nets;2)method of synthesis of DAAP tools in energy efficiency management information systems(EEMIS)of the enterprise which provides a reduction in hardware and time costs from 10%to 40%;3)method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges,it reduces the cost and energy consumption;4)method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times.The proposed methods,models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse,as a result,energy efficiency has increased by 12%-25%(depending on season and peculiarities of growing plants). | Vasyl Teslyuk Ivan Tsmots Michal Gregus ml. Taras Teslyuk Iryna Kazymyra | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 6 | PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production显示文摘The advent of additive technologies has provided a significant breakthrough in the production of medical implants.It has reduced costs,increased productivity and accuracy of the implant manufacturing process.However,there are problems associated with assessing defects in the microstructure,mechanical and technological properties of alloys,both during their production by powder metallurgy and in the process of 3D printing.Thus traditional research methods of alloys properties demand considerable human,material,and time resources.At the same time,artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure,phase composition,and properties of titanium powder’s alloys.It provides new possibilities for the efficient production of biocompatible implants for various functional purposes.However,the accuracy of the methods and models used should be as high as possible.In this paper we designed a hybrid PNN-SVM(Probabilistic Neural Network-Support Vector Machine)high-precision approach for the intelligent evaluation of alloy properties for additive manufacturing of biomedical implants.We have proposed a new approach for extending the dimensionality of input data space by the outputs of the summation layer of the modified PNN topology.Subsequent classification based on the expanded dataset is performed using SVM.We conducted experimental modeling of the proposed approach using a data set on the properties of titanium alloys Ti-6Al-4V and Ti-Al-V-Zr.We have demonstrated a significant increase in the accuracy of the PNN-SVM scheme compared to the single classifiers that form it and other machine learning methods. | Ivan Izonin Roman Tkachenko Michal Gregus Zoia Duriagina Nataliya Shakhovska | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 7 | Physical Properties and Light-Related Applications ol Black Silicon Structures显示文摘 | Emil Pincik Hikaru Kobayashi Robert Brunner Kentaro Imamura Milan Mikula Michal Kucera Pavel Vojtek Zuzana Zabudla PeterSvec Sr Jan Gregus PeterSvec Jr | 2016 | 材料科学与工程(中英文B版)2016，6，3： | 0 |
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