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Artigos de revistas sobre o tema "Machine learning"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 25 de novembro de 2024

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1

M. Brandao, Iago, e Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING". Eletrônica de Potência 27, n.º 03 (22 de setembro de 2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.

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2

Naeini, Ehsan Zabihi, e Kenton Prindle. "Machine learning and learning from machines". Leading Edge 37, n.º 12 (dezembro de 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 e Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning". International Journal of Trade, Economics and Finance 11, n.º 6 (dezembro de 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, n.º 2 (2 de abril de 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 (25 de julho de 2020): 2540–45. http://dx.doi.org/10.5373/jardcs/v12sp7/20202387.

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6

Charpentier, Arthur, Emmanuel Flachaire e Antoine Ly. "Econometrics and Machine Learning". Economie et Statistique / Economics and Statistics, n.º 505d (11 de abril de 2019): 147–69. http://dx.doi.org/10.24187/ecostat.2018.505d.1970.

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7

Mor, Laksanya. "Introduction to Machine Learning". International Journal of Science and Research (IJSR) 11, n.º 3 (5 de março de 2022): 1522–25. http://dx.doi.org/10.21275/sr22328110600.

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8

Lewis, Ted G., e Peter J. Denning. "Learning machine learning". Communications of the ACM 61, n.º 12 (20 de novembro de 2018): 24–27. http://dx.doi.org/10.1145/3286868.

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9

Rasi, Mr Ajmal, Dr Rajasimha A. Makram e Ms Shilpa Das. "Topic Detection using Machine Learning". International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 de junho de 2018): 1433–36. http://dx.doi.org/10.31142/ijtsrd14272.

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10

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

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11

Palaniappan, Devaki, Jayanthi Manoharan, Deepthi Pragalathan e Shilpashri Murugesan. "Crop Prediction Using Machine Learning". AMBIENT SCIENCE 9, n.º 3 (novembro de 2022): 47–50. http://dx.doi.org/10.21276/ambi.2022.09.3.ga03.

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12

A, Rohini, T. Kusuma sri, R. Ganodhay, V. Saikiran, T. Balaji e S. Fouziya. "Hydroponics Integrated with Machine Learning". International Journal of Research Publication and Reviews 5, n.º 4 (28 de abril de 2024): 9334–39. http://dx.doi.org/10.55248/gengpi.5.0424.1126.

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13

Dayma, Chetan, e Dr kamal Raj R. "Machine Learning with AI chips". International Journal of Research Publication and Reviews 5, n.º 3 (2 de março de 2024): 1304–9. http://dx.doi.org/10.55248/gengpi.5.0324.0656.

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14

Mahesh, Batta. "Machine Learning Algorithms - A Review". International Journal of Science and Research (IJSR) 9, n.º 1 (5 de janeiro de 2020): 381–86. http://dx.doi.org/10.21275/art20203995.

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15

Vinay, Kamisetty. "Disease Diagnosis Using Machine Learning". International Journal of Science and Research (IJSR) 11, n.º 12 (5 de dezembro de 2022): 209–14. http://dx.doi.org/10.21275/sr221204093412.

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16

Sindayigaya, Laurent, e Ayon Dey. "Machine Learning Algorithms: A Review". International Journal of Science and Research (IJSR) 11, n.º 8 (5 de agosto de 2022): 1127–33. http://dx.doi.org/10.21275/sr22815163219.

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17

Malik, Bhumika, e Nivedita Singh. "Intrusion Detection using Machine Learning". International Journal of Science and Research (IJSR) 11, n.º 5 (5 de maio de 2022): 283–86. http://dx.doi.org/10.21275/mr22310165547.

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18

Kollovieh, M., e D. Bani-Harouni. "Machine Learning". Der Hautarzt 72, n.º 8 (29 de julho de 2021): 719. http://dx.doi.org/10.1007/s00105-021-04834-0.

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19

Fletcher, Seth. "Machine Learning". Scientific American 309, n.º 2 (17 de julho de 2013): 62–68. http://dx.doi.org/10.1038/scientificamerican0813-62.

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20

Cauvin, Bertrand, e Pierre Benning. "Machine Learning". International Journal of 3-D Information Modeling 6, n.º 3 (julho de 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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21

Dietterich, Tom. "Machine learning". ACM Computing Surveys 28, n.º 4es (dezembro de 1996): 3. http://dx.doi.org/10.1145/242224.242229.

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22

Dietterich, T. G. "Machine Learning". Annual Review of Computer Science 4, n.º 1 (junho de 1990): 255–306. http://dx.doi.org/10.1146/annurev.cs.04.060190.001351.

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23

Mitchell, T., B. Buchanan, G. DeJong, T. Dietterich, P. Rosenbloom e A. Waibel. "Machine Learning". Annual Review of Computer Science 4, n.º 1 (junho de 1990): 417–33. http://dx.doi.org/10.1146/annurev.cs.04.060190.002221.

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24

Cussens, J. "Machine learning". Computing & Control Engineering Journal 7, n.º 4 (1 de agosto de 1996): 164–68. http://dx.doi.org/10.1049/cce:19960402.

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25

Louridas, Panos, e Christof Ebert. "Machine Learning". IEEE Software 33, n.º 5 (setembro de 2016): 110–15. http://dx.doi.org/10.1109/ms.2016.114.

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26

Schneider, William F., e Hua Guo. "Machine Learning". Journal of Physical Chemistry A 122, n.º 4 (fevereiro de 2018): 879. http://dx.doi.org/10.1021/acs.jpca.8b00034.

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27

Schneider, William F., e Hua Guo. "Machine Learning". Journal of Physical Chemistry C 122, n.º 4 (fevereiro de 2018): 1889. http://dx.doi.org/10.1021/acs.jpcc.8b00036.

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28

Müller, Uwe. "Machine Learning". Wissensmanagement 1, n.º 3 (outubro de 2019): 31–33. http://dx.doi.org/10.1007/s43443-019-0010-0.

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29

Schneider, William F., e Hua Guo. "Machine Learning". Journal of Physical Chemistry B 122, n.º 4 (fevereiro de 2018): 1347. http://dx.doi.org/10.1021/acs.jpcb.8b00035.

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30

Macesic, Nenad, Fernanda Polubriaginof e Nicholas P. Tatonetti. "Machine learning". Current Opinion in Infectious Diseases 30, n.º 6 (dezembro de 2017): 511–17. http://dx.doi.org/10.1097/qco.0000000000000406.

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31

Schneider, William F., e Hua Guo. "Machine Learning". Journal of Physical Chemistry Letters 9, n.º 3 (fevereiro de 2018): 569. http://dx.doi.org/10.1021/acs.jpclett.8b00009.

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32

Pereira, Daniel Silveira. "Machine Learning". Controlling 32, n.º 2 (2020): 65–66. http://dx.doi.org/10.15358/0935-0381-2020-2-65.

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33

Keeping, Tim. "Machine learning". Preview 2019, n.º 199 (4 de março de 2019): 37. http://dx.doi.org/10.1080/14432471.2019.1597409.

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34

Jackson, A. H. "Machine learning". Expert Systems 5, n.º 2 (maio de 1988): 132–50. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00341.x.

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35

Delhommelle, Jerome. "Machine learning". Molecular Simulation 44, n.º 11 (23 de maio de 2018): 865. http://dx.doi.org/10.1080/08927022.2018.1471777.

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36

Toensmeier, Pat. "Machine Learning". Plastics Engineering 74, n.º 5 (maio de 2018): 40–47. http://dx.doi.org/10.1002/j.1941-9635.2018.tb01886.x.

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37

Blanquicett Benavides, Luis Alfredo, e Luis Fernando Murillo Fernández. "Machine Learning". Revista Sistemas, n.º 165 (20 de dezembro de 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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38

Pandey, Mrs Arjoo. "Machine Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 8 (31 de agosto de 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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39

Ono, Kanta. "Machine Learning". Synchrotron Radiation News 35, n.º 4 (4 de julho de 2022): 2. http://dx.doi.org/10.1080/08940886.2022.2114736.

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40

Alpaydın, Ethem. "Machine learning". Wiley Interdisciplinary Reviews: Computational Statistics 3, n.º 3 (4 de abril de 2011): 195–203. http://dx.doi.org/10.1002/wics.166.

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41

Martins, Emerson, e Napoleao Verardi Galegale. "Machine learning:". International Journal of Innovation 11, n.º 3 (6 de outubro de 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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42

Akgül, İsmail, e Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS". NWSA Academic Journals 17, n.º 4 (29 de outubro de 2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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43

Raju Cherukuri, Bangar. "Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments". International Journal of Science and Research (IJSR) 13, n.º 10 (5 de outubro de 2024): 1539–49. http://dx.doi.org/10.21275/ms241022095645.

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44

Abou-Zamzam, Ahmed M. "Learning from machine learning". Journal of Vascular Surgery 75, n.º 1 (janeiro de 2022): 286. http://dx.doi.org/10.1016/j.jvs.2021.07.119.

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45

TURAN, SELIN CEREN, e MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS". Journal of Science and Arts 22, n.º 2 (30 de junho de 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.

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Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared

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46

KUMAR, POKURI ASHOK. "Botnet Detection with Machine Learning Classifiers". Journal of Research on the Lepidoptera 51, n.º 2 (15 de maio de 2020): 329–35. http://dx.doi.org/10.36872/lepi/v51i2/301100.

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47

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

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48

Reddy, Gadhe Manideep. "Bit Coin Prediction Using Machine Learning". International Journal of Psychosocial Rehabilitation 24, n.º 5 (20 de abril de 2020): 2901–5. http://dx.doi.org/10.37200/ijpr/v24i5/pr201995.

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49

Charde, Dhananjay, e Girish Talmale. "Laptop Price Predictor Using Machine Learning". AMBIENT SCIENCE 09, n.º 02 _03 (agosto de 2022): 94–97. http://dx.doi.org/10.21276/ambi.2022.09.2.ta04.

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50

Mubarakova,, S. R., S. T. Amanzholova, e R. K. Uskenbayeva,. "USING MACHINE LEARNING METHODS IN CYBERSECURITY". Eurasian Journal of Mathematical and Computer Applications 10, n.º 1 (março de 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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