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Journal articles on the topic 'Machine learning'

To see the other types of publications on this topic, follow the link: Machine learning.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 July 2024

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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 (September 22, 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 (December 2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.

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3

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 (December 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 state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.

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4

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

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5

Siddique, Shumaila. "Machine Learning and Cryptography." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2540–45. http://dx.doi.org/10.5373/jardcs/v12sp7/20202387.

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6

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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7

Lewis, Ted G., and Peter J. Denning. "Learning machine learning." Communications of the ACM 61, no. 12 (November 20, 2018): 24–27. http://dx.doi.org/10.1145/3286868.

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8

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 (June 30, 2018): 1433–36. http://dx.doi.org/10.31142/ijtsrd14272.

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9

Mudiraj, Nakkala Srinivas. "Detecting Phishing using Machine Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 488–90. http://dx.doi.org/10.31142/ijtsrd23755.

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10

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

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11

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 (April 28, 2024): 9334–39. http://dx.doi.org/10.55248/gengpi.5.0424.1126.

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12

Dayma, Chetan, and Dr kamal Raj R. "Machine Learning with AI chips." International Journal of Research Publication and Reviews 5, no. 3 (March 2, 2024): 1304–9. http://dx.doi.org/10.55248/gengpi.5.0324.0656.

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13

Mahesh, Batta. "Machine Learning Algorithms - A Review." International Journal of Science and Research (IJSR) 9, no. 1 (January 5, 2020): 381–86. http://dx.doi.org/10.21275/art20203995.

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14

Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (October 29, 2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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15

Kollovieh, M., and D. Bani-Harouni. "Machine Learning." Der Hautarzt 72, no. 8 (July 29, 2021): 719. http://dx.doi.org/10.1007/s00105-021-04834-0.

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16

Fletcher, Seth. "Machine Learning." Scientific American 309, no. 2 (July 17, 2013): 62–68. http://dx.doi.org/10.1038/scientificamerican0813-62.

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17

Cauvin, Bertrand, and Pierre Benning. "Machine Learning." International Journal of 3-D Information Modeling 6, no. 3 (July 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 applied by other infrastructure domains (Roads, Rails, Tunnels, etc.); to check the current functions and capabilities of a buildingSMART Data Dictionary platform; and to define a common term list, in order to facilitate standardization and IFC-Bridge classes' development.

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18

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

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19

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

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20

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

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21

Cussens, J. "Machine learning." Computing & Control Engineering Journal 7, no. 4 (August 1, 1996): 164–68. http://dx.doi.org/10.1049/cce:19960402.

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22

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

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23

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

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24

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

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25

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

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26

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

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27

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

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28

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

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29

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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30

Keeping, Tim. "Machine learning." Preview 2019, no. 199 (March 4, 2019): 37. http://dx.doi.org/10.1080/14432471.2019.1597409.

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31

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

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32

Delhommelle, Jerome. "Machine learning." Molecular Simulation 44, no. 11 (May 23, 2018): 865. http://dx.doi.org/10.1080/08927022.2018.1471777.

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33

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

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34

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 que existe entre la medicina predictiva y el Machine Learning, sus ventajas y los diferentes algoritmos de Machine Learning que se pueden aplicar dependiendo de los tipos de datos.

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35

Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 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 processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.

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36

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

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37

Alpaydın, Ethem. "Machine learning." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 3 (April 4, 2011): 195–203. http://dx.doi.org/10.1002/wics.166.

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38

Martins, Emerson, and Napoleao Verardi Galegale. "Machine learning:." International Journal of Innovation 11, no. 3 (October 6, 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 pesquisa contribui para cobrir tal lacuna, favorecendo o delineamento de ações e pesquisas futuras. Principais resultados: Foram identificados no corpus bibliométrico de ML: autores mais citados e com maior número de publicações, países e universidades mais produtivas, periódicos com maior número de publicações e citações, áreas de conhecimento com maior número de publicações e artigos de maior prestígio. Nos temas e domínios de ML, foram identificados: principais coocorrências de palavras-chaves, temas emergentes (agrupados em cinco clusters) e nuvem de palavras por título e por resumo. Os estudos sobre impacto da aquisição de dados e análise preditiva representam oportunidades para pesquisas futuras. Contribuições teóricas/metodológicas: O protocolo PRISMA possibilitou a identificação e análises quantitativa e qualitativa relevantes dos artigos, consolidando o conhecimento científico sobre o tema. Contribuições sociais/gerenciais: Facilidade de compreender a maturidade das pesquisas sobre ML e Big Data por parte de gestores de empresas e pesquisadores, quanto à viabilidade de investimentos para se obter vantagens competitivas com tais tecnologias.

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39

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

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40

KUMAR, POKURI ASHOK. "Botnet Detection with Machine Learning Classifiers." Journal of Research on the Lepidoptera 51, no. 2 (May 15, 2020): 329–35. http://dx.doi.org/10.36872/lepi/v51i2/301100.

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41

Singh, Yashbir. "Machine Learning Integration in Cardiac Electrophysiology." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 942–44. http://dx.doi.org/10.5373/jardcs/v12sp4/20201565.

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42

Reddy, Gadhe Manideep. "Bit Coin Prediction Using Machine Learning." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 2901–5. http://dx.doi.org/10.37200/ijpr/v24i5/pr201995.

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43

Charde, Dhananjay, and Girish Talmale. "Laptop Price Predictor Using Machine Learning." AMBIENT SCIENCE 09, no. 02 _03 (August 2022): 94–97. http://dx.doi.org/10.21276/ambi.2022.09.2.ta04.

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44

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 (March 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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45

S.S., Anandha Krishnan. "SQL Injection Detection Using Machine Learning." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 300–310. http://dx.doi.org/10.47059/revistageintec.v11i3.1939.

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46

Sadiku, Matthew N. O., Sarhan M. Musa, and Adedamola Omotoso. "Machine Learning in Medicine: A Primer." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 98–100. http://dx.doi.org/10.31142/ijtsrd20255.

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47

Borah, Monalisha Saikia, Bikram Pratim Bhuyan, Mauchumi Saikia Pathak, and P. K. Bhattacharya. "Machine Learning in Predicting Hemoglobin Variants." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 140–43. http://dx.doi.org/10.18178/ijmlc.2018.8.2.677.

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48

Kok, Arvid, Ivana Ilic Mestric, Giavid Valiyev, and Michael Street. "Cyber Threat Prediction with Machine Learning." Information & Security: An International Journal 47, no. 2 (2020): 203–20. http://dx.doi.org/10.11610/isij.4714.

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49

K, Mr Akash, Miss Ahalya K, Mr Dhinesh N, and Miss Diya Shereef M K. "Detecting Fake Images Using Machine Learning." International Journal of Research Publication and Reviews 4, no. 4 (April 8, 2023): 2063–69. http://dx.doi.org/10.55248/gengpi.2023.4.4.35702.

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50

Reddy Kudumula, Umamaheswara. "Enhancing Patient Care with Machine Learning." International Journal of Science and Research (IJSR) 13, no. 3 (March 5, 2024): 1817–19. http://dx.doi.org/10.21275/sr24321093056.

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