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Статті в журналах з теми "Methods of machine learning"

Автор: Grafiati

Опубліковано: 28 травня 2022

Оновлено: 31 липня 2025

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1

Mubarakova,, S. R., S. T. Amanzholova,, and R. K. Uskenbayeva,. "USING MACHINE LEARNING METHODS IN CYBERSECURITY." Eurasian Journal of Mathematical and Computer Applications 10, no. 1 (2022): 69–78. http://dx.doi.org/10.32523/2306-6172-2022-10-1-69-78.

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Abstract Cybersecurity is an ever-changing field, with advances in technology that open up new opportunities for cyberattacks. In addition, even though serious secu- rity breaches are often reported, small organizations still have to worry about security breaches as they can often be the target of viruses and phishing. This is why it is so important to ensure the privacy of your user profile in cyberspace. The past few years have seen a rise in machine learning algorithms that address major cybersecu- rity issues such as intrusion detection systems (IDS), detection of new modifications of know

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2

Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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3

Mishra, Dipanshu, Shrikant Mani Tripathi, Akash Chaurasia, and Pawan Kumar Chaurasia. "A Review on Ensemble Learning Methods: Machine Learning Approach." International Journal of Research Publication and Reviews 6, no. 2 (2025): 3795–803. https://doi.org/10.55248/gengpi.6.0225.0971.

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4

Tashev, Sarvar Norboboyevich. "DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS." American Journal of Applied Science and Technology 4, no. 10 (2024): 69–79. http://dx.doi.org/10.37547/ajast/volume04issue10-11.

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With the emergence of the Internet, cyber-attacks and threats have become significant issues. Traditional manual network monitoring and rule-based packet filtering methods have become labor-intensive and less effective in combating attacks. Filtering packets based solely on payload and pattern matching is also inefficient. There is a need for a dynamic model capable of learning packet filtering rules. This article proposes a packet filtering model using Neural Networks. After developing the model classified with training and validation data, it can be utilized to support dynamic packet filteri

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5

Aschepkov, Valeriy. "METHODS OF MACHINE LEARNING IN MODERN METROLOGY." Measuring Equipment and Metrology 85 (2024): 57–60. http://dx.doi.org/10.23939/istcmtm2024.01.057.

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In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data. Attention is paid to the development of hybrid approaches that combine ML methods with tradit

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6

Dauitbayeva, A. O. "MODERN METHODS OF MACHINE LEARNING AND ANALYTICS." ТЕХНИКА ҒЫЛЫМДАРЫ ЖӘНЕ ТЕХНОЛОГИЯ 7, no. 3 (2024): 12–19. https://doi.org/10.52081/tst.2024.v03.i7.039.

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The article proposes algorithms for processing big data to optimize business processes. The methods of data integration, distributed computing and machine learning for analysis and forecasting are considered. Testing on business cases has shown cost reduction and increased accuracy of solutions, confirming the practical value of the developed approaches. In the modern world, the volume of data is growing exponentially, thanks to the development of technology and the ubiquity of digital devices. Petabytes of information are generated daily: This is data from social networks, electronic devices,

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7

Bzdok, Danilo, Martin Krzywinski, and Naomi Altman. "Machine learning: supervised methods." Nature Methods 15, no. 1 (2018): 5–6. http://dx.doi.org/10.1038/nmeth.4551.

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8

Turčaník, Michal. "Network User Behaviour Analysis by Machine Learning Methods." Information & Security: An International Journal 50 (2021): 66–78. http://dx.doi.org/10.11610/isij.5014.

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9

Deviatko, Anna. "Evolution of Automated Testing Methods Using Machine Learning." American Journal of Engineering and Technology 07, no. 05 (2025): 88–100. https://doi.org/10.37547/tajet/volume07issue05-07.

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program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses. By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques. Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage. The paper offers a thorou

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10

Olajide, Olajide Blessing, Soneye Olufemi Sobowale, Ogunniyi Olufunke Kemi, et al. "Diagnosing Malaria and Jaundice Using Selected Machine Learning Methods." International Journal of Research Publication and Reviews 5, no. 11 (2024): 3084–93. http://dx.doi.org/10.55248/gengpi.5.1124.3256.

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11

Skoropad, Pylyp, and Andrii Yuras. "MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING." Measuring Equipment and Metrology 85, no. 2 (2024): 40–45. http://dx.doi.org/10.23939/istcmtm2024.02.040.

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Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyz- ing data about thermometers. The examination involves the application of a branch of artificial intelligence, in particular machine learning (ML) methods, as a means of automating processes. Such methods facilitate the identification and aggregation of pertinent data, the detection of gaps, and the conversion of unstructured text into an easily analyzable structured format. The paper details the employment of reinforcement learning for the automatic extraction of information from di

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12

Et. al., Zakoldaev D. A. ,. "Machine Learning Methods Performance Evaluation*." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2664–66. http://dx.doi.org/10.17762/turcomat.v12i2.2284.

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In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.

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13

BI, Hua, Hong-Li LIANG, and Jue WANG. "Resampling Methods and Machine Learning." Chinese Journal of Computers 32, no. 5 (2009): 862–77. http://dx.doi.org/10.3724/sp.j.1016.2009.00862.

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14

Ivanova, Lubov Nikolaevna, Andrey Vladimirovich Kurkin, and Sergei Evgenievich Ivanov. "Machine learning methods for forecasting." Nedelya nauki Sankt-Peterburgskogo gosudarstvennogo morskogo tekhnicheskogo universiteta 2, no. 4 (2020): 9. http://dx.doi.org/10.52899/9785883036063_434.

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15

Karachun, Irina, Lyubov Vinnichek, and Andrey Tuskov. "Machine learning methods in finance." SHS Web of Conferences 110 (2021): 05012. http://dx.doi.org/10.1051/shsconf/202111005012.

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This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demons

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16

Hofmann, Thomas, Bernhard Schölkopf, and Alexander J. Smola. "Kernel methods in machine learning." Annals of Statistics 36, no. 3 (2008): 1171–220. http://dx.doi.org/10.1214/009053607000000677.

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17

Rahangdale, Ashwini, and Shital Raut. "Machine Learning Methods for Ranking." International Journal of Software Engineering and Knowledge Engineering 29, no. 06 (2019): 729–61. http://dx.doi.org/10.1142/s021819401930001x.

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Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradig

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18

Facciorusso, Antonio, Raffaele Licinio, and Alfredo Di Leo. "Machine Learning Methods in Gastroenterology." Gastroenterology 149, no. 4 (2015): 1128–29. http://dx.doi.org/10.1053/j.gastro.2015.03.056.

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19

Mitchell, John B. O. "Machine learning methods in chemoinformatics." Wiley Interdisciplinary Reviews: Computational Molecular Science 4, no. 5 (2014): 468–81. http://dx.doi.org/10.1002/wcms.1183.

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20

Shoup, T. E. "Machine learning—Paradigms and methods." Mechanism and Machine Theory 26, no. 3 (1991): 349. http://dx.doi.org/10.1016/0094-114x(91)90075-f.

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21

SHABLIY, Nataliya, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, and Olha MALYSHEVSKA. "KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS." Applied Computer Science 17, no. 4 (2021): 75–83. http://dx.doi.org/10.35784/acs-2021-30.

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The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before t

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22

Nighot, Prof. Priyanka D. "Methods of Machine Learning for Predictive Analytics." International Journal of Advance and Applied Research 6, no. 25(A) (2025): 40–42. https://doi.org/10.5281/zenodo.15300505.

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<strong>Abstract:</strong> Predictive analytics enables businesses to make data-driven decisions by using statistical models, machine learning algorithms, and historical data to estimate future events. The strengths and drawbacks of several important machine learning approaches are examined in this paper, including ensemble methods, neural networks, support vector machines, decision trees, regression models, and others. Applications in the real world in the fields of manufacturing, healthcare, finance, and retail show how revolutionary predictive analytics can be in streamlining procedures and

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23

Wang, Zhiyue. "Customer Segmentation Based on Machine Learning Methods." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 126–32. http://dx.doi.org/10.54097/g70xqb16.

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In recent years, as times change, consumer behavior continues to change rapidly, and their preferences and consumer attitudes change with age and experience. In the generalization of the mass market, it is difficult to identify the needs and desires of customers through various promotional tools. Therefore, customer segmentation can be an option for marketers to offer preferential goods or services to customers. Segmentation can help the company to quickly identify the preferences of the customers and provide them with the desired goods. However, there are significant differences between custo

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24

Qutub, Aseel, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi. "Prediction of Employee Attrition Using Machine Learning and Ensemble Methods." International Journal of Machine Learning and Computing 11, no. 2 (2021): 110–14. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1022.

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Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, A

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25

M Shakor, Suad. "Benchmarking Machine Learning Methods COVID-19 Classification using MCDM technique." International Journal of Science and Research (IJSR) 11, no. 4 (2022): 678–81. http://dx.doi.org/10.21275/sr22411222004.

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26

Xin, Yang, Lingshuang Kong, Zhi Liu, et al. "Machine Learning and Deep Learning Methods for Cybersecurity." IEEE Access 6 (2018): 35365–81. http://dx.doi.org/10.1109/access.2018.2836950.

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27

Zine, Mohamed, Fouzi Harrou, Mohammed Terbeche, Mohammed Bellahcene, Abdelkader Dairi, and Ying Sun. "E-Learning Readiness Assessment Using Machine Learning Methods." Sustainability 15, no. 11 (2023): 8924. http://dx.doi.org/10.3390/su15118924.

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Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students’ readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tle

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28

Sharifzade, E. "COMPARATIVE ANALYSIS OF TRADITIONAL FORECASTING METHODS AND METHODS BASED ON MACHINE LEARNING." Sciences of Europe, no. 113 (March 27, 2023): 85–89. https://doi.org/10.5281/zenodo.7773856.

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Artificial intelligence (AI) has been helping to solve business problems for many years. However, the success of AI and machine learning initiatives depends on developing algorithms that can learn through trial and error to improve performance over time. In many cases, existing business operations that complement AI and Machine Learning processes are based on logical instructions and if-then rules or follow a decision matrix.

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29

Wu, Tsung-Chin, Zhirou Zhou, Hongyue Wang, et al. "Advanced machine learning methods in psychiatry: an introduction." General Psychiatry 33, no. 2 (2020): e100197. http://dx.doi.org/10.1136/gpsych-2020-100197.

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Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

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30

Zhang, Yizhi. "Prediction on Tencent Stock Using the Machine Learning Methods." Advances in Economics, Management and Political Sciences 45, no. 1 (2023): 173–82. http://dx.doi.org/10.54254/2754-1169/45/20230279.

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With the development of computer and AI, machine learning has become a significantly popular topic to study about. One application of machine learning is analyzing and predicting stock. This paper first reviews the concepts of machine learning and the reason for using machine learning to predict stocks, then analyzes and predicts the stock of Tencent which is a Chinese corporation mainly making social media and games by using machines learning. Incorporating with methods of Linear Regression and Muti-Layer Perceptron (MLP) regression on basic data and data after log difference, this study demo

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31

Groeniitz, Heiko. "Machine learning methods for classification problems." Śląski Przegląd Statystyczny 18, no. 24 (2020): 241–48. http://dx.doi.org/10.15611/sps.2020.18.14.

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32

Popkov, Yu S. "Mathematical Methods of Randomized Machine Learning." Journal of Mathematical Sciences 254, no. 5 (2021): 652–76. http://dx.doi.org/10.1007/s10958-021-05331-4.

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33

Alekseeva, D., A. Marochkina, and A. Paramonov. "Traffic optimization applying machine learning methods." Telecom IT 9, no. 1 (2021): 1–12. http://dx.doi.org/10.31854/2307-1303-2021-9-1-1-12.

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Future networks bring higher communication requirements in latency, computations, data quality, etc. The attention to various challenges in the network field through the advances of Artificial Intelligence (AI), Machine Learning (ML) and Big Data analysis is growing. The subject of research in this paper is 4G mobile traffic collected during one year. The amount of data retrieved from devices and network management are motivating the trend toward learning-based approaches. The research method is to compare various ML methods for traffic prediction. In terms of ML, to find a solution for a regr

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34

Başakın, Eyyup Ensar, ÖMER EKMEKCİOĞLU, and Mehmet Ozger. "Drought Analysis with Machine Learning Methods." Pamukkale University Journal of Engineering Sciences 25, no. 8 (2019): 985–91. http://dx.doi.org/10.5505/pajes.2019.34392.

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35

Ivanyuk, Vera. "INTRODUCING STUDENTS TO MACHINE LEARNING METHODS." Современная математика и концепции инновационного математического образования 9, no. 1 (2022): 157–62. http://dx.doi.org/10.54965/24129895_2022_9_1_157.

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36

Rezaei Ghahroodi, Zahra, and Zhina Aghamohamadi. "Record Linkage with Machine Learning Methods." Journal of Statistical Sciences 16, no. 1 (2022): 1–24. http://dx.doi.org/10.52547/jss.16.1.1.

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37

Vinuesa, Ricardo, and Soledad Le Clainche. "Machine-Learning Methods for Complex Flows." Energies 15, no. 4 (2022): 1513. http://dx.doi.org/10.3390/en15041513.

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38

Dorzhiev, Ardan Sayanovich. "BANKRUPTCY PREDICTION USING MACHINE LEARNING METHODS." Information Society, no. 1 (2021): 56–67. http://dx.doi.org/10.52605/16059921_2021_01_56.

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39

Kenny, Mr John. "Price Prediction using Machine Learning Methods." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (2021): 661–68. http://dx.doi.org/10.22214/ijraset.2021.34259.

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40

Thangalakshmi, Dr S., and Dr K. Sivasami. "Machine Learning Methods for Marine Systems." IOP Conference Series: Materials Science and Engineering 1177, no. 1 (2021): 012002. http://dx.doi.org/10.1088/1757-899x/1177/1/012002.

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41

Radeev, N. A. "Avalanches Forecasting Using Machine Learning Methods." Vestnik NSU. Series: Information Technologies 19, no. 2 (2021): 92–101. http://dx.doi.org/10.25205/1818-7900-2021-19-2-92-101.

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The occurrence of snow avalanches is mainly influenced by meteorological conditions and the configuration of snow cover layers. Machine learning methods have predictive power and are capable of predicting new events. From the trained machine learning models, an ensemble is obtained that predicts the possibility of avalanches. The model obtained in the article uses avalanche data, meteorological data and generated data on the state of snow cover for training. This allows the resulting solution to be used in more mountainous areas than solutions using a wider range of less available data.Snow da

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42

Patel, Lauv, Tripti Shukla, Xiuzhen Huang, David W. Ussery, and Shanzhi Wang. "Machine Learning Methods in Drug Discovery." Molecules 25, no. 22 (2020): 5277. http://dx.doi.org/10.3390/molecules25225277.

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Анотація:

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screenin

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43

Lvovich, I. Ya, Ya E. Lvovich, A. P. Preobrazhensky, and O. N. Choporov. "The Features of Machine Learning Methods." INFORMACIONNYE TEHNOLOGII 26, no. 9 (2020): 499–506. http://dx.doi.org/10.17587/it.26.499-506.

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44

P, KIRAN RAO, and KUMAR R. SANDEEP. "MACHINE LEARNING METHODS FOR CLOUD COMPUTING." i-manager’s Journal on Cloud Computing 3, no. 4 (2016): 7. http://dx.doi.org/10.26634/jcc.3.4.13593.

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45

Gower, Robert M., Mark Schmidt, Francis Bach, and Peter Richtarik. "Variance-Reduced Methods for Machine Learning." Proceedings of the IEEE 108, no. 11 (2020): 1968–83. http://dx.doi.org/10.1109/jproc.2020.3028013.

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46

Bajari, Patrick, Denis Nekipelov, Stephen P. Ryan, and Miaoyu Yang. "Machine Learning Methods for Demand Estimation." American Economic Review 105, no. 5 (2015): 481–85. http://dx.doi.org/10.1257/aer.p20151021.

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We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions

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47

Barla, A., G. Jurman, S. Riccadonna, S. Merler, M. Chierici, and C. Furlanello. "Machine learning methods for predictive proteomics." Briefings in Bioinformatics 9, no. 2 (2007): 119–28. http://dx.doi.org/10.1093/bib/bbn008.

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Mengoni, Riccardo, and Alessandra Di Pierro. "Kernel methods in Quantum Machine Learning." Quantum Machine Intelligence 1, no. 3-4 (2019): 65–71. http://dx.doi.org/10.1007/s42484-019-00007-4.

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Granata, Francesco, and Giovanni de Marinis. "Machine learning methods for wastewater hydraulics." Flow Measurement and Instrumentation 57 (October 2017): 1–9. http://dx.doi.org/10.1016/j.flowmeasinst.2017.08.004.

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Wang, Simiao, Shengqi You, and Shenwei Zhou. "Loan Prediction Using Machine Learning Methods." Advances in Economics, Management and Political Sciences 5, no. 1 (2023): 210–15. http://dx.doi.org/10.54254/2754-1169/5/20220081.

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Анотація:

Credit risk has always been the most important risk faced by commercial banks. Credit risk management has important practical significance for preventing credit risk. With the emerging of machine learning algorithms, numerous frameworks, including linear regression, support vector machine, random forest and decision tree are proposed with satisfying performance and robust accuracy. This paper will focus on predicting credit outcomes and calculating forecast accuracy from a given dataset. This paper adopts three algorithms, decision tree, random forest and logistic regression, to calculate the

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