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Journal articles on the topic 'Cognitive Computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

M, Babul Reddy, and Prof Rahul Pawar. "Cognitive Computing in Cloud Environments." International Journal of Research Publication and Reviews 5, no. 3 (2024): 569–73. http://dx.doi.org/10.55248/gengpi.5.0324.0621.

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2

Wang, Yingxu, Victor Raskin, Julia Rayz, et al. "Cognitive Computing." International Journal of Software Science and Computational Intelligence 10, no. 1 (2018): 1–14. http://dx.doi.org/10.4018/ijssci.2018010101.

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Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary fi

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Modha, Dharmendra S., Rajagopal Ananthanarayanan, Steven K. Esser, Anthony Ndirango, Anthony J. Sherbondy, and Raghavendra Singh. "Cognitive computing." Communications of the ACM 54, no. 8 (2011): 62–71. http://dx.doi.org/10.1145/1978542.1978559.

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Pagel, Peter, Edy Portmann, and Karin Vey. "Cognitive Computing." Informatik-Spektrum 41, no. 1 (2018): 1–4. http://dx.doi.org/10.1007/s00287-018-1091-4.

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D’Onofrio, Sara, Edy Portmann, Michel Franzelli, and Christoph Bürki. "Cognitive Computing." Informatik-Spektrum 41, no. 2 (2018): 113–22. http://dx.doi.org/10.1007/s00287-018-1095-0.

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6

Sridharan, Mohan, Gerald Tesauro, and James Hendler. "Cognitive Computing." IEEE Intelligent Systems 32, no. 4 (2017): 3–4. http://dx.doi.org/10.1109/mis.2017.3121554.

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7

Demirkan, Haluk, Seth Earley, and Robert R. Harmon. "Cognitive Computing." IT Professional 19, no. 4 (2017): 16–20. http://dx.doi.org/10.1109/mitp.2017.3051332.

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8

Shama, Firdous. "Cognitive Computing." Research and Applications: Emerging Technologies 3, no. 1 (2021): 1–8. https://doi.org/10.5281/zenodo.4730213.

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<em>Cognitive computing is the new wave of Artificial Intelligence (AI), relying on traditional techniques based on expert systems and also exploiting statistics and mathematical models. In particular, cognitive computing systems can be regarded as a “more human” artificial intelligence. In fact, they mimic human reasoning methodologies, showing special capabilities in dealing with uncertainties and in solving problems that typically entail computation consuming processes. Moreover, they can evolve, exploiting the accumulated experience to learn from the past, both from errors and

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9

Wang, Yingxu, George Baciu, Yiyu Yao, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set

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10

Farrell, Robert G., Jonathan Lenchner, Jeffrey O. Kephjart, et al. "Symbiotic Cognitive Computing." AI Magazine 37, no. 3 (2016): 81–93. http://dx.doi.org/10.1609/aimag.v37i3.2628.

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IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing. We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.

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11

Wang, Yingxu. "On Cognitive Computing." International Journal of Software Science and Computational Intelligence 1, no. 3 (2009): 1–15. http://dx.doi.org/10.4018/jssci.2009070101.

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12

I, Kaushik. "Cognitive Computing in Fault Tolerance of Software." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 50–52. https://doi.org/10.21275/sr21809150244.

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13

Wang, Yu. "The Application of Computer-Based Multimedia Technology in Cognitive Computing." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/3354576.

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With the continuous development of science and technology, people’s research on computer-related technologies is gradually deepening. The proposal of artificial intelligence makes the research of intelligent AI in urgent need for seeking breakthroughs. Among them, cognitive computing methods are important for computers and human brain thinking. Model learning is even more meaningful. This article aims to study the application of computer-based multimedia technology in cognitive computing methods. For this, this article proposes a method of distributed multimedia technology and a method of two-

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14

Gain, Ulla. "The cognitive function and the framework of the functional hierarchy." Applied Computing and Informatics 16, no. 1/2 (2018): 81–116. http://dx.doi.org/10.1016/j.aci.2018.03.003.

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Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hie

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15

Pagel, Peter, Edy Portmann, and Karin Vey. "Cognitive Computing – Teil 2." Informatik-Spektrum 41, no. 2 (2018): 81–84. http://dx.doi.org/10.1007/s00287-018-1101-6.

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16

Dubhashi, Devdatt. "Singularities and Cognitive Computing." Proceedings 1, no. 3 (2017): 189. http://dx.doi.org/10.3390/is4si-2017-04027.

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17

Veres, Csaba. "Strong Cognitive Symbiosis: Cognitive Computing for Humans." Big Data and Cognitive Computing 1, no. 1 (2017): 6. http://dx.doi.org/10.3390/bdcc1010006.

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18

WANG, YINGXU. "ON FORMAL AND COGNITIVE SEMANTICS FOR SEMANTIC COMPUTING." International Journal of Semantic Computing 04, no. 02 (2010): 203–37. http://dx.doi.org/10.1142/s1793351x10000833.

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Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the

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19

Wang, Yingxu, Newton Howard, Janusz Kacprzyk, et al. "Cognitive Informatics." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 1 (2018): 1–13. http://dx.doi.org/10.4018/ijcini.2018010101.

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Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distin

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20

Cheng, Yihang, Xi Zhang, Xiaojiong Wang, et al. "Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing." International Journal on Semantic Web and Information Systems 17, no. 1 (2021): 67–96. http://dx.doi.org/10.4018/ijswis.2021010104.

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Technology-enhanced learning (TEL) is important in social web. Recently, cognitive computing became significant to analyze sentiment and improve effectiveness in TEL field. So analyzing the development of cognitive computing, what and how its abilities improve TEL are necessary. For solving these issues, this study used systematic review approach based on technology view and enhancement view of TEL. Specifically, this study used topic search results in computer science field of “cognitive computing” and “anticipatory computing” in Web of Science database to do map analysis. Besides development

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21

Manjula, T., and T. Sudha. "Cognitive Computing For Sustainable Agriculture." Asian Journal of Computer Science and Technology 8, no. 3 (2019): 32–34. http://dx.doi.org/10.51983/ajcst-2019.8.3.2738.

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Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase

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22

Dodig-Crnkovic, Gordana, and Robert Lowe. "Morphological Computing and Cognitive Agency." Proceedings 1, no. 3 (2017): 185. http://dx.doi.org/10.3390/proceedings1030185.

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23

Kumar, M., W. P. Horn, J. Kepner, J. E. Moreira, and P. Pattnaik. "IBM POWER9 and cognitive computing." IBM Journal of Research and Development 62, no. 4/5 (2018): 10:1–10:12. http://dx.doi.org/10.1147/jrd.2018.2846958.

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24

Ogiela, Marek R., Ilsun You, Fang-Yie Leu, and Makoto Takizawa. "Modern cognitive and ubiquitous computing." Neurocomputing 122 (December 2013): 1–2. http://dx.doi.org/10.1016/j.neucom.2013.05.009.

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25

D’Onofrio, Sara, and Edy Portmann. "Cognitive Computing in Smart Cities." Informatik-Spektrum 40, no. 1 (2016): 46–57. http://dx.doi.org/10.1007/s00287-016-1006-1.

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26

Banavar, Guruduth, and Martin Cooper. "Turing Lecture 2017 Cognitive Computing." ITNOW 58, no. 4 (2016): 62–63. http://dx.doi.org/10.1093/itnow/bww117.

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27

Schizas, Christos N. "Cognitive computing for supporting eHealth." Health and Technology 7, no. 1 (2016): 11–12. http://dx.doi.org/10.1007/s12553-016-0162-2.

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28

Sellmann, Meinolf. "Meta-Algorithms in Cognitive Computing." IEEE Intelligent Systems 32, no. 4 (2017): 35–39. http://dx.doi.org/10.1109/mis.2017.3121549.

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29

Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.

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30

Lu, Huimin, and Yujie Li. "Cognitive Computing for Intelligence Systems." Mobile Networks and Applications 25, no. 4 (2020): 1434–35. http://dx.doi.org/10.1007/s11036-019-01428-y.

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31

Shubhodip, Sasmal. "Cognitive Computing in Data Engineering Applications." International Journal of Contemporary Research in Multidisciplinary 3, no. 1 (2024): 175–80. https://doi.org/10.5281/zenodo.10686669.

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The increasing volume and complexity of data in contemporary environments pose significant challenges to traditional data engineering methodologies. In response, this research explores the transformative potential of cognitive computing in data engineering applications. Cognitive computing, encompassing natural language processing, machine learning, and knowledge representation, offers a paradigm shift in how data is processed, analyzed, and utilized for decision-making. This paper investigates the fundamental principles of cognitive computing and its specific applications in data engineering

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32

Wang, Yingxu, Witold Pedrycz, George Baciu, Ping Chen, Guoyin Wang, and Yiyu Yao. "Perspectives on Cognitive Computing and Applications." International Journal of Software Science and Computational Intelligence 2, no. 4 (2010): 32–44. http://dx.doi.org/10.4018/jssci.2010100103.

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Cognitive Computing (CC) is an emerging paradigm of intelligent computing theories and technologies based on cognitive informatics, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. The development of Cognitive Computers (cC) is centric in cognitive computing methodologies. A cC is an intelligent computer for knowledge processing as that of a conventional von Neumann computer for data processing. This paper summarizes the presentations of a set of 6 position papers presented in the ICCI’10 Plenary Panel on Cognitive Comp

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33

Wang, Yingxu, Robert C. Berwick, Simon Haykin, et al. "Cognitive Informatics and Cognitive Computing in Year 10 and Beyond." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (2011): 1–21. http://dx.doi.org/10.4018/jcini.2011100101.

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Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers (CogC) that perceive, infer, and learn. This paper reports a set of nine position statements presented in the pl

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34

Wang, Yingxu, Edmund T. Rolls, Newton Howard, et al. "Cognitive Informatics and Computational Intelligence." International Journal of Software Science and Computational Intelligence 7, no. 2 (2015): 50–69. http://dx.doi.org/10.4018/ijssci.2015040103.

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Cognitive Informatics (CI) is a contemporary multidisciplinary field spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, cognitive linguistics, and cognitive philosophy. Cognitive Computing (CC) is a novel paradigm of intelligent computing methodologies and systems based on CI that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This paper reports a set of position statements presented in the plenary panel of IEEE ICCI*CC'14 on Cognitive Informatic

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35

Chen, Jun, Chao Lu, Haifeng Huang, et al. "Cognitive Computing-Based CDSS in Medical Practice." Health Data Science 2021 (July 22, 2021): 1–13. http://dx.doi.org/10.34133/2021/9819851.

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Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights. (1) A holistic review of both research papers and industrial pract

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36

DONG, ANDY. "Special Issue: Design computing and cognition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 4 (2005): 227–28. http://dx.doi.org/10.1017/s0890060405050158.

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The field of research in design computing and cognition focuses on computational theories and systems that enact design. Design computing and cognition produces a unifying framework to model and explain design beyond the description of “design computing and cognition,” as in “design computing” and “design cognition” as two cognate disciplines. Research in design computing and cognition recognizes not only the essential relationship between human cognitive processes as models of computation but also how models of computation inspire conceptual realizations of human cognition in design. The arti

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37

Zhou, Zhenhua. "Emotional thinking as the foundation of consciousness in artificial intelligence." Cultures of Science 4, no. 3 (2021): 112–23. http://dx.doi.org/10.1177/20966083211052651.

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Current theories of artificial intelligence (AI) generally exclude human emotions. The idea at the core of such theories could be described as ‘cognition is computing’; that is, that human psychological and symbolic representations and the operations involved in structuring such representations in human thinking and intelligence can be converted by AI into a series of cognitive symbolic representations and calculations in a manner that simulates human intelligence. However, after decades of development, the cognitive computing doctrine has encountered many difficulties, both in theory and in p

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38

Haun, Matthias. "A Plea for the Cognitive Solution Attempt: Cognitive Robotics, Cognitive Factory and Cognitive Solutions. Cognitive Robotics as a Model for the Development of Cognitive Solutions." Applied Mechanics and Materials 613 (August 2014): 3–10. http://dx.doi.org/10.4028/www.scientific.net/amm.613.3.

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A cognitive approach conceptualizes the goods producing enterprise and its production factors (such as divisions, units, sections or resources like machines or robots) as well as the enterprise’s products (such as valves) as cognitive models. These models are then put into shape with the help of cognitive computing techniques.

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39

Wang, Yingxu, James A. Anderson, George Baciu, et al. "Perspectives on eBrain and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 4 (2012): 1–21. http://dx.doi.org/10.4018/jcini.2012100101.

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Cognitive Informatics (CI) is a discipline spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, and cognitive linguistics. CI aims to investigate the internal information processing mechanisms and processes of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing and computational intelligence. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’12 on eBrain and Cognitive Computers

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40

Zhanatauov, S. U. "COGNITIVE COMPUTING: MODELS, CALCULATIONS, APPLICATIONS, RESULTS." Theoretical & Applied Science 97, no. 05 (2021): 594–610. http://dx.doi.org/10.15863/tas.2021.05.97.91.

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41

POZNANSKI, ROMAN R. "MODEL-BASED NEUROIMAGING FOR COGNITIVE COMPUTING." Journal of Integrative Neuroscience 08, no. 03 (2009): 345–69. http://dx.doi.org/10.1142/s021963520900223x.

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42

Ogiela, Marek R., Ilsun You, Fatos Xhafa, and Hoon Ko. "Towards context, cognitive, and secure computing." Computers & Mathematics with Applications 63, no. 2 (2012): 337–38. http://dx.doi.org/10.1016/j.camwa.2011.12.056.

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43

Yao, Yiyu. "Three-Way Decisions and Cognitive Computing." Cognitive Computation 8, no. 4 (2016): 543–54. http://dx.doi.org/10.1007/s12559-016-9397-5.

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44

Sager, Sebastian, Katja Mombaur, and Joachim Funke. "Scientific computing for the cognitive sciences." Journal of Computational Science 4, no. 4 (2013): 242–44. http://dx.doi.org/10.1016/j.jocs.2012.12.001.

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45

Wang, Guoyin. "DGCC: data-driven granular cognitive computing." Granular Computing 2, no. 4 (2017): 343–55. http://dx.doi.org/10.1007/s41066-017-0048-3.

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46

Faix, Marvin, Emmanuel Mazer, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy, and Jorge Lobo. "Cognitive Computation." International Journal of Software Science and Computational Intelligence 9, no. 3 (2017): 37–58. http://dx.doi.org/10.4018/ijssci.2017070103.

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Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of a

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47

Wang, Yingxu. "Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing." Fundamenta Informaticae 90, no. 3 (2009): 283–303. http://dx.doi.org/10.3233/fi-2009-0019.

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48

Niu, Jiaojiao, Chenchen Huang, Jinhai Li, and Min Fan. "Parallel computing techniques for concept-cognitive learning based on granular computing." International Journal of Machine Learning and Cybernetics 9, no. 11 (2018): 1785–805. http://dx.doi.org/10.1007/s13042-018-0783-z.

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49

Chen, Sixing, Jun Kang, Suchi Liu, and Yifan Sun. "Cognitive computing on unstructured data for customer co-innovation." European Journal of Marketing 54, no. 3 (2019): 570–93. http://dx.doi.org/10.1108/ejm-01-2019-0092.

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Purpose This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation. Design/methodology/approach The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory. Findings The paper identifies a suite of methodologies

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50

Faccia, Alessio. "National Payment Switches and the Power of Cognitive Computing against Fintech Fraud." Big Data and Cognitive Computing 7, no. 2 (2023): 76. http://dx.doi.org/10.3390/bdcc7020076.

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National Payment Switches (NPSs) and International Payment Switches (IPSs), including major players such as SWIFT, Mastercard, and CHIPS, have become vital to the financial infrastructure, facilitating secure and efficient transactions among local financial institutions. Nonetheless, the growing adoption of digital payments has heightened the risk of financial fraud. Consequently, NPSs, under the direct ownership of Central Banks (CBs), are increasingly adopting advanced technologies, such as cognitive computing, to bolster their fraud detection capabilities in their respective countries. This

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