Готові списки джерел за темами / Methods of machine learning / Статті в журналах
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Статті в журналах з теми "Methods of machine learning"

Автор: Grafiati
Опубліковано: 28 травня 2022
Оновлено: 30 травня 2022

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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 known malware, malware, and spam detection, and malware analysis. In this arti- cle, algorithms have been analyzed using data mining collected from various libraries, and analytics with additional emerging data-driven models to provide more effective security solutions. In addition, an analysis was carried out of companies that are en- gaged in cyber attacks using machine learning. According to the research results, it was revealed that the concept of cybersecurity data science allows you to make the computing process more efficient and intelligent compared to traditional processes in the field of cybersecurity. As a result, according to the results of the study, it was revealed that machine learning, namely unsupervised learning, is an effective method of dealing with risks in cybersecurity and cyberattacks.
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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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Анотація:
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 paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.
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10

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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11

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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12

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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Анотація:
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 demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.
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13

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, Adaboost Model, and Gradient Boosting Classifier models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.
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14

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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15

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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16

Hirunyawanakul, Anusara, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Efficient Machine Learning Methods for Hard Disk Drive Yield Prediction Improvement." International Journal of Machine Learning and Computing 10, no. 2 (2020): 240–46. http://dx.doi.org/10.18178/ijmlc.2020.10.2.926.

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17

ÁDÁM, Norbert, Branislav MADOŠ, Marek ČAJKOVSKÝ, Ján HURTUK, and Tomáš TOMČÁK. "Methods of the Data Mining and Machine Learning in Computer Security." Acta Electrotechnica et Informatica 14, no. 2 (2014): 46–50. http://dx.doi.org/10.15546/aeei-2014-0017.

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18

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 screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
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19

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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20

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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21

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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22

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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23

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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24

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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Анотація:
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 of demand than some commonly used alternatives.
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25

Namkung, Junghyun. "Machine learning methods for microbiome studies." Journal of Microbiology 58, no. 3 (2020): 206–16. http://dx.doi.org/10.1007/s12275-020-0066-8.

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26

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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27

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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28

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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29

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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30

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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31

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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32

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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Анотація:
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 data is generated by the SNOWPACK software package.
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33

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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34

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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Анотація:
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 regression problem using the ensemble models Random Forest, Boosting, Gradient Boosting, and Adaptive Boosting (AdaBoost). The comparison was based on the quality indicators RMSE, MAE, and coefficient of determi-nation. In the result Gradient Boosting showed the most accurate prediction. Using this ML model for mobile traffic optimization could improve network performance.
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35

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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36

Pozdnoukhov, A., R. S. Purves, and M. Kanevski. "Applying machine learning methods to avalanche forecasting." Annals of Glaciology 49 (2008): 107–13. http://dx.doi.org/10.3189/172756408787814870.

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Анотація:
AbstractAvalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest-neighbour methods (NN), which are known to have limitations when dealing with high-dimensional data. We apply support vector machines (SVMs) to a dataset from Lochaber, Scotland, UK, to assess their applicability in avalanche forecasting. SVMs belong to a family of theoretically based techniques from machine learning and are designed to deal with high-dimensional data. Initial experiments showed that SVMs gave results that were comparable with NN for categorical and probabilistic forecasts. Experiments utilizing the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.
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37

Randhawa, Princy, Vijay Shanthagiri, and Ajay Kumar. "Recognition of Violent Activity Response Using Machine Learning Methods with Wearable Sensors." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (2019): 592–601. http://dx.doi.org/10.5373/jardcs/v11sp11/20193071.

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38

Asgari-Motlagh, Xaniyar, Mehdi Ketabchy, and Ali Daghighi. "Probabilistic Quantitative Precipitation Forecasting Using Machine Learning Methods and Probable Maximum Precipitation." International Academic Journal of Science and Engineering 06, no. 01 (2019): 1–14. http://dx.doi.org/10.9756/iajse/v6i1/1910001.

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39

Sharif Ullah, A. M. M., and Jun'ichi Tamaki. "2A2-G04 A Human Comprehensible Machine Learning Method." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2010 (2010): _2A2—G04_1—_2A2—G04_3. http://dx.doi.org/10.1299/jsmermd.2010._2a2-g04_1.

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40

Awad, W. A., and S. M. Elseuofi. "Machine Learning methods for E-mail Classification." International Journal of Computer Applications 16, no. 1 (2011): 39–45. http://dx.doi.org/10.5120/1974-2646.

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41

Pradhan, Dr Madhavi Ajay. "Fake News Detection Methods: Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (2020): 971–75. http://dx.doi.org/10.22214/ijraset.2020.29630.

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42

Gitis, Valeri G., and Alexander B. Derendyaev. "Machine Learning Methods for Seismic Hazards Forecast." Geosciences 9, no. 7 (2019): 308. http://dx.doi.org/10.3390/geosciences9070308.

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Анотація:
In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes ( M m a x ), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of M m a x at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of M m a x on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.
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43

Aytac, Tugba, Muhammed Ali Aydin, and Abdul Halim Zaim. "Detection DDOS Attacks Using Machine Learning Methods." Electrica 20, no. 2 (2020): 159–67. http://dx.doi.org/10.5152/electrica.2020.20049.

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44

Ershov, M. D. "First-Order Optimization Methods in Machine Learning." INFORMACIONNYE TEHNOLOGII 25, no. 11 (2019): 662–69. http://dx.doi.org/10.17587/it.25.662-669.

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45

Haghiabi, Amir Hamzeh, Ali Heidar Nasrolahi, and Abbas Parsaie. "Water quality prediction using machine learning methods." Water Quality Research Journal 53, no. 1 (2018): 3–13. http://dx.doi.org/10.2166/wqrj.2018.025.

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Анотація:
Abstract This study investigates the performance of artificial intelligence techniques including artificial neural network (ANN), group method of data handling (GMDH) and support vector machine (SVM) for predicting water quality components of Tireh River located in the southwest of Iran. To develop the ANN and SVM, different types of transfer and kernel functions were tested, respectively. Reviewing the results of ANN and SVM indicated that both models have suitable performance for predicting water quality components. During the process of development of ANN and SVM, it was found that tansig and RBF as transfer and kernel functions have the best performance among the tested functions. Comparison of outcomes of GMDH model with other applied models shows that although this model has acceptable performance for predicting the components of water quality, its accuracy is slightly less than ANN and SVM. The evaluation of the accuracy of the applied models according to the error indexes declared that SVM was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties. By evaluating the results of the models based on the DDR index, it was found that the lowest DDR value was related to the performance of the SVM model.
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46

Santibáñez, Francisco, Carlos Flores, Franco Basso, et al. "Mining Accident Detection Using Machine Learning Methods." IFAC Proceedings Volumes 46, no. 16 (2013): 31–33. http://dx.doi.org/10.3182/20130825-4-us-2038.00051.

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47

Pande, Deepali V., and Shrinivas Deshpande. "Algorithms and Classification through Machine Learning Methods." Journal of Computer Science Engineering and Software Testing 6, no. 2 (2020): 30–33. http://dx.doi.org/10.46610/jocses.2020.v06i02.005.

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Quade, Markus, Thomas Isele, and Markus Abel. "Machine learning control — explainable and analyzable methods." Physica D: Nonlinear Phenomena 412 (November 2020): 132582. http://dx.doi.org/10.1016/j.physd.2020.132582.

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Suleymanov, U., and S. Rustamov. "Automated News Categorization using Machine Learning methods." IOP Conference Series: Materials Science and Engineering 459 (December 7, 2018): 012006. http://dx.doi.org/10.1088/1757-899x/459/1/012006.

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Jianlin Cheng, A. N. Tegge, and P. Baldi. "Machine Learning Methods for Protein Structure Prediction." IEEE Reviews in Biomedical Engineering 1 (2008): 41–49. http://dx.doi.org/10.1109/rbme.2008.2008239.

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