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Journal articles on the topic 'The machine learning (Machine Learning)'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

M. Brandao, Iago, and Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING." Eletrônica de Potência 27, no. 03 (2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.

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2

Naeini, Ehsan Zabihi, and Kenton Prindle. "Machine learning and learning from machines." Leading Edge 37, no. 12 (2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.

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3

Çelik, Rumeysa Hilal, Hacı Aslan Onur İşcil, Ecem Bulut, and Saliha Ece Acuner. "Learning molecular machines by machine learning." Eurasian Journal of Science Engineering and Technology 6, no. 2 (2025): 100–120. https://doi.org/10.55696/ejset.1620495.

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Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes. Understanding their three-dimendional (3D) structures is key to deciphering their functions. However, a significant gap exists between the vast number of known protein sequences and the relatively limited number of experimentally determined protein structures. Unraveling the mechanisms of protein folding remains a central challenge in understanding the sequence-structure/dynamics-function relationship. In recent years, machine learning (

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4

Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed sta

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5

Trott, David. "Deceiving Machines: Sabotaging Machine Learning." CHANCE 33, no. 2 (2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.

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6

Siddique, Shumaila. "Machine Learning and Cryptography." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 2540–45. http://dx.doi.org/10.5373/jardcs/v12sp7/20202387.

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7

Charpentier, Arthur, Emmanuel Flachaire, and Antoine Ly. "Econometrics and Machine Learning." Economie et Statistique / Economics and Statistics, no. 505d (April 11, 2019): 147–69. http://dx.doi.org/10.24187/ecostat.2018.505d.1970.

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8

Mor, Laksanya. "Introduction to Machine Learning." International Journal of Science and Research (IJSR) 11, no. 3 (2022): 1522–25. http://dx.doi.org/10.21275/sr22328110600.

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9

Lewis, Ted G., and Peter J. Denning. "Learning machine learning." Communications of the ACM 61, no. 12 (2018): 24–27. http://dx.doi.org/10.1145/3286868.

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10

Rasi, Mr Ajmal, Dr Rajasimha A. Makram, and Ms Shilpa Das. "Topic Detection using Machine Learning." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 1433–36. http://dx.doi.org/10.31142/ijtsrd14272.

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11

Mudiraj, Nakkala Srinivas. "Detecting Phishing using Machine Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 488–90. http://dx.doi.org/10.31142/ijtsrd23755.

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12

Vinay, Kamisetty. "Disease Diagnosis Using Machine Learning." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 209–14. http://dx.doi.org/10.21275/sr221204093412.

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13

Sindayigaya, Laurent, and Ayon Dey. "Machine Learning Algorithms: A Review." International Journal of Science and Research (IJSR) 11, no. 8 (2022): 1127–33. http://dx.doi.org/10.21275/sr22815163219.

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14

Malik, Bhumika, and Nivedita Singh. "Intrusion Detection using Machine Learning." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 283–86. http://dx.doi.org/10.21275/mr22310165547.

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15

x, Ritik. "Diabetes Prediction Using Machine Learning." International Journal of Scientific Engineering and Research 9, no. 12 (2021): 22–25. https://doi.org/10.70729/se211220184029.

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16

Mahesh, Batta. "Machine Learning Algorithms - A Review." International Journal of Science and Research (IJSR) 9, no. 1 (2020): 381–86. http://dx.doi.org/10.21275/art20203995.

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17

S, Sharanya. "Glaucoma Detection Using Machine Learning." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 28–37. https://doi.org/10.21275/sr21328181208.

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18

Palaniappan, Devaki, Jayanthi Manoharan, Deepthi Pragalathan, and Shilpashri Murugesan. "Crop Prediction Using Machine Learning." AMBIENT SCIENCE 9, no. 3 (2022): 47–50. http://dx.doi.org/10.21276/ambi.2022.09.3.ga03.

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19

A, Rohini, T. Kusuma sri, R. Ganodhay, V. Saikiran, T. Balaji, and S. Fouziya. "Hydroponics Integrated with Machine Learning." International Journal of Research Publication and Reviews 5, no. 4 (2024): 9334–39. http://dx.doi.org/10.55248/gengpi.5.0424.1126.

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20

Dayma, Chetan, and Dr kamal Raj R. "Machine Learning with AI chips." International Journal of Research Publication and Reviews 5, no. 3 (2024): 1304–9. http://dx.doi.org/10.55248/gengpi.5.0324.0656.

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21

Shambavi, S., P. Kamaleshwari, and R. Charumathi. "Diabetic Retinopathy Using Machine Learning." International Journal of Science and Research (IJSR) 14, no. 1 (2025): 471–74. https://doi.org/10.21275/sr25110114513.

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22

Prasad, D. Durga Babu, and Dr A. Ratna Raju. "Helmet Detection Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 16352–57. https://doi.org/10.55248/gengpi.6.0525.19105.

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23

Prashanthi, Hosuru, and Ms Mallarapu Poojitha. "Machine learning based Malicious URLDetection." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12311–14. https://doi.org/10.55248/gengpi.6.0525.18141.

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24

Kollovieh, M., and D. Bani-Harouni. "Machine Learning." Der Hautarzt 72, no. 8 (2021): 719. http://dx.doi.org/10.1007/s00105-021-04834-0.

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25

Fletcher, Seth. "Machine Learning." Scientific American 309, no. 2 (2013): 62–68. http://dx.doi.org/10.1038/scientificamerican0813-62.

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26

Cauvin, Bertrand, and Pierre Benning. "Machine Learning." International Journal of 3-D Information Modeling 6, no. 3 (2017): 1–16. http://dx.doi.org/10.4018/ij3dim.2017070101.

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A Bridge Data Dictionary contains an exhaustive list of terms used in the field of bridges. These terms are classified in systems in order to avoid any lacks, to identify all the expected object attributes, and to allow machines to understand the associated concepts. The main objectives of a Bridge Data Dictionary are many: ensure the sustainability of information over time; facilitate information exchange between the actors of the same project; ensure interoperability between the software packages. Other objectives have been reached during the process: to test a working methodology to be appl

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27

Dietterich, Tom. "Machine learning." ACM Computing Surveys 28, no. 4es (1996): 3. http://dx.doi.org/10.1145/242224.242229.

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28

Dietterich, T. G. "Machine Learning." Annual Review of Computer Science 4, no. 1 (1990): 255–306. http://dx.doi.org/10.1146/annurev.cs.04.060190.001351.

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29

Mitchell, T., B. Buchanan, G. DeJong, T. Dietterich, P. Rosenbloom, and A. Waibel. "Machine Learning." Annual Review of Computer Science 4, no. 1 (1990): 417–33. http://dx.doi.org/10.1146/annurev.cs.04.060190.002221.

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30

Cussens, J. "Machine learning." Computing & Control Engineering Journal 7, no. 4 (1996): 164–68. http://dx.doi.org/10.1049/cce:19960402.

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31

Louridas, Panos, and Christof Ebert. "Machine Learning." IEEE Software 33, no. 5 (2016): 110–15. http://dx.doi.org/10.1109/ms.2016.114.

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32

Martins, Emerson, and Napoleao Verardi Galegale. "Machine learning:." International Journal of Innovation 11, no. 3 (2023): e24056. http://dx.doi.org/10.5585/2023.24056.

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Objetivo: Apresentar uma visão dos artigos científicos publicados nos últimos dez anos sobre o tema aprendizado de máquina, do inglês machine learning (ML), com ênfase nos algoritmos preditivos. Método/abordagem: Análise bibliométrica, com apoio do protocolo PRISMA, para avaliar autores, universidades e países, quanto a produtividade, citações bibliográficas e focos sobre o tema, com amostra de 773 artigos das bases de dados Scopus e Web of Science, no período de 2013 a maio/2023. Originalidade/valor: Há ausência de estudos na literatura que consolidem artigos relacionados a ML e Big Data. A p

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33

Schneider, William F., and Hua Guo. "Machine Learning." Journal of Physical Chemistry A 122, no. 4 (2018): 879. http://dx.doi.org/10.1021/acs.jpca.8b00034.

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34

Schneider, William F., and Hua Guo. "Machine Learning." Journal of Physical Chemistry C 122, no. 4 (2018): 1889. http://dx.doi.org/10.1021/acs.jpcc.8b00036.

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35

Müller, Uwe. "Machine Learning." Wissensmanagement 1, no. 3 (2019): 31–33. http://dx.doi.org/10.1007/s43443-019-0010-0.

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36

Schneider, William F., and Hua Guo. "Machine Learning." Journal of Physical Chemistry B 122, no. 4 (2018): 1347. http://dx.doi.org/10.1021/acs.jpcb.8b00035.

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37

Macesic, Nenad, Fernanda Polubriaginof, and Nicholas P. Tatonetti. "Machine learning." Current Opinion in Infectious Diseases 30, no. 6 (2017): 511–17. http://dx.doi.org/10.1097/qco.0000000000000406.

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38

Schneider, William F., and Hua Guo. "Machine Learning." Journal of Physical Chemistry Letters 9, no. 3 (2018): 569. http://dx.doi.org/10.1021/acs.jpclett.8b00009.

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39

Pereira, Daniel Silveira. "Machine Learning." Controlling 32, no. 2 (2020): 65–66. http://dx.doi.org/10.15358/0935-0381-2020-2-65.

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40

Keeping, Tim. "Machine learning." Preview 2019, no. 199 (2019): 37. http://dx.doi.org/10.1080/14432471.2019.1597409.

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41

Jackson, A. H. "Machine learning." Expert Systems 5, no. 2 (1988): 132–50. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00341.x.

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42

Delhommelle, Jerome. "Machine learning." Molecular Simulation 44, no. 11 (2018): 865. http://dx.doi.org/10.1080/08927022.2018.1471777.

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43

Toensmeier, Pat. "Machine Learning." Plastics Engineering 74, no. 5 (2018): 40–47. http://dx.doi.org/10.1002/j.1941-9635.2018.tb01886.x.

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44

Blanquicett Benavides, Luis Alfredo, and Luis Fernando Murillo Fernández. "Machine Learning." Revista Sistemas, no. 165 (December 20, 2022): 34–45. http://dx.doi.org/10.29236/sistemas.n165a6.

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El sector salud tiene involucrado una gran cantidad de procesos y procedimientos generadores de todo tipo de información que en muchos casos no están disponibles de forma libre para los profesionales de diferentes áreas y en especial de las ciencias computacionales.¿Qué sucedería si toda esta información pudiera estar disponible? La medicina preventiva y predictiva podría desarrollarse con mayor rapidez, desarrollando modelos predictivos a través de algoritmos de Machine Learning, como apoyo a los profesionales de la salud en la toma de decisiones. Este artículo permite conocer la convergencia

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45

Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 864–69. http://dx.doi.org/10.22214/ijraset.2023.55224.

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Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning pr

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46

Ono, Kanta. "Machine Learning." Synchrotron Radiation News 35, no. 4 (2022): 2. http://dx.doi.org/10.1080/08940886.2022.2114736.

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47

Alpaydın, Ethem. "Machine learning." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 3 (2011): 195–203. http://dx.doi.org/10.1002/wics.166.

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48

Abou-Zamzam, Ahmed M. "Learning from machine learning." Journal of Vascular Surgery 75, no. 1 (2022): 286. http://dx.doi.org/10.1016/j.jvs.2021.07.119.

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49

Raju Cherukuri, Bangar. "Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1539–49. http://dx.doi.org/10.21275/ms241022095645.

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50

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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