Academic literature on the topic 'Cognitive analytics'
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Journal articles on the topic "Cognitive analytics"
Handfield, Robert, Seongkyoon Jeong, and Thomas Choi. "Emerging procurement technology: data analytics and cognitive analytics." International Journal of Physical Distribution & Logistics Management 49, no. 10 (December 10, 2019): 972–1002. http://dx.doi.org/10.1108/ijpdlm-11-2017-0348.
Full textCasimiro, Leni T. "Cognitive Engagement in Online Intercultural Interactions: Beyond Analytics." International Journal of Information and Education Technology 6, no. 6 (2016): 441–47. http://dx.doi.org/10.7763/ijiet.2016.v6.729.
Full textNarayana, T., Sohail Shaik, and S. Kaur. "Predictive Analytics – The Cognitive Analysis." Oriental journal of computer science and technology 10, no. 1 (March 23, 2017): 187–93. http://dx.doi.org/10.13005/ojcst/10.01.25.
Full textEarley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (July 2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.
Full textWang, Yingxu, and Jun Peng. "Big Data Analytics." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 2 (April 2017): 41–56. http://dx.doi.org/10.4018/ijcini.2017040103.
Full textOzdal, Muhammet Mustafa, Serif Yesil, Taemin Kim, Andrey Ayupov, John Greth, Steven Burns, and Ozcan Ozturk. "Graph Analytics Accelerators for Cognitive Systems." IEEE Micro 37, no. 1 (January 2017): 42–51. http://dx.doi.org/10.1109/mm.2017.7.
Full textRika, Havana, Itzhak Aviv, and Roye Weitzfeld. "Unleashing the Potentials of Quantum Probability Theory for Customer Experience Analytics." Big Data and Cognitive Computing 6, no. 4 (November 10, 2022): 135. http://dx.doi.org/10.3390/bdcc6040135.
Full textFisher, Brian, Tera Marie Green, and Richard Arias-Hernández. "Visual Analytics as a Translational Cognitive Science." Topics in Cognitive Science 3, no. 3 (April 11, 2011): 609–25. http://dx.doi.org/10.1111/j.1756-8765.2011.01148.x.
Full textArias-Hernandez, Richard, Tera M. Green, and Brian Fisher. "From Cognitive Amplifiers to Cognitive Prostheses: Understandings of the Material Basis of Cognition in Visual Analytics." Interdisciplinary Science Reviews 37, no. 1 (March 2012): 4–18. http://dx.doi.org/10.1179/0308018812z.0000000001.
Full textRostamzadeh, Neda, Sheikh S. Abdullah, and Kamran Sedig. "Visual Analytics for Electronic Health Records: A Review." Informatics 8, no. 1 (February 23, 2021): 12. http://dx.doi.org/10.3390/informatics8010012.
Full textDissertations / Theses on the topic "Cognitive analytics"
Kovanovic, Vitomir. "Assessing cognitive presence using automated learning analytics methods." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28759.
Full textRao, Rashmi Jayathirtha. "Modeling learning behaviour and cognitive bias from web logs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492560600002105.
Full textSoukieh, Tarek. "How Can Business Analytics Induce Creativity: The Performance Effects of User Interaction with Business Analytics." Cleveland State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=csu1462634733.
Full textCarvalho, André Silva de. "Analytics como uma ferramenta para Consumer Insights." Escola Superior de Propaganda e Marketing, 2017. http://tede2.espm.br/handle/tede/267.
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Being innovative in a more and more competitive market can be anything but trivial. There is a complex variables system to be taken into account throughout an innovation process, and hardly ever will there be enough data to support a research or decision. It is always possible to turn to human inference, or cognitive bias, when enough data is not available, or when time for decision-making is scarce. Consumer Insight technique has been used for this research purpose and aimed at lowering cognitive bias, seeking to find out what are consumers' wishes and needs so that decision-making or innovation could be supported. This paper proposes to mitigate the influence of cognitive bias, by means of data analysis techniques, in search for patterns which can identify opportunities to give both decision-making and search for innovation some support. In order to achive this purpose, unstructured data from 26.514 telephone talks had in a big financial market company between 01.12.2016 e 31.12.2016 have been used. Analysis has been carried out with the transcript from voice into text concomitantly with Text Mining and Social Network analysis. The results have led us to identify main client demands from a sales perspective, cancellation resquest, as well as the reason for inefficiency in offering new products from elements of higher centrality identified in the word association networks. It is implied that the combined use of analytical techniques applied to unstructured data may give rise to findings in which cognitive bias is lower.
Em um mercado cada vez mais competitivo, ser inovador pode ser um diferencial, porém não é uma atividade trivial. Existe um sistema de variáveis complexas que deve ser considerado ao longo de um processo de inovação e nem sempre há dados suficientes que suportem uma pesquisa ou decisão. A inferência humana, ou viés cognitivo, pode ser uma alternativa quando não existem dados suficientes ou quando o tempo para a tomada de decisão é menor que o necessário. A técnica de Consumer Insight foi utilizada nesta pesquisa com o objetivo de diminuir o viés cognitivo, buscando descobrir os anseios e necessidades do consumidor, para suportar o processo de tomada de decisão ou inovação. Este estudo apresenta uma proposta para mitigar a influência do viés cognitivo, a partir de técnicas de análise de dados, em busca de padrões que possam identificar as oportunidades para suportar o processo decisório ou a busca pela inovação. Neste trabalho foram utilizados dados não estruturados de 26.514 conversas telefônicas realizadas no período de 01/12/2016 a 31/12/2016, provenientes de uma empresa do mercado financeiro. A metodologia analítica consistiu na transcrição de voz para texto e no uso associado de técnicas de Text Mining e Análise de Redes Sociais. Os resultados obtidos permitiram identificar as principais demandas dos clientes na perspectiva de vendas, pedido de cancelamento e a razão da ineficiência das ofertas de novos produtos, a partir dos elementos de maior centralidade identificados nas redes de associação de palavras. Implica-se que o uso combinado de técnicas analíticas em dados não estruturados pode permitir a obtenção de achados com menor influência do viés cognitivo.
Schumacher, Clara [Verfasser], and Dirk [Akademischer Betreuer] Ifenthaler. "Cognitive, metacognitive and motivational perspectives on Learning Analytics : Synthesizing self-regulated learning, assessment, and feedback with Learning Analytics / Clara Schumacher ; Betreuer: Dirk Ifenthaler." Mannheim : Universitätsbibliothek Mannheim, 2020. http://d-nb.info/1204828741/34.
Full textShovman, Mark. "Measuring comprehension of abstract data visualisations." Thesis, Abertay University, 2011. https://rke.abertay.ac.uk/en/studentTheses/4cfbdab1-0f91-4886-8b02-a4a8da48aa72.
Full textWixon, Naomi. "An Inductive Method of Measuring Students’ Cognitive and Affective Processes via Self-Reports in Digital Learning Environments." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/504.
Full textChen, Sheng-Bo. "Examining the Effect of Self-Regulated Learning on Cognitive Engagement in Mastery-Based Online Courses: A Learning Analytics Perspective." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577788376743384.
Full textKurup, Unmesh. "Design and use of a bimodal cognitive architecture for diagrammatic reasoning and cognitive modeling." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1198526352.
Full textLarmuseau, Charlotte. "Learning Analytics pour la compréhension des processus d'apprentissage dans les environnements d'apprentissage en ligne." Thesis, Lille, 2020. http://www.theses.fr/2020LILUI082.
Full textThe current learning landscape is evolving in terms of what is learned and the context in which learning takes place. This can largely be related to the continuously changing requirements of today’s labor market. Additionally, the availability of information technology has changed the traditional educational boundaries of time, space, and informational access. From an instructional design perspective, the combination of both phenomena poses a great challenge for researchers and instructional designers to implement instruction that meets the requirements of the current learning landscape (Ng, 2015). A research-based instructional design model that has proven to be effective in promoting complex learning is the four-component instructional design model (4C/ID-model; van Merriënboer et al., 2002). Nonetheless, offering an online learning environment based on a research-based instructional design model is not necessarily a guarantee for its effectiveness. As the learner is an active agent in the online learning process, the effectiveness of learning environments largely depends on student cognitive and motivational-affective characteristics. In order to investigate characteristics that can influence the effectiveness of a 4C/ID-based online course and how effectiveness can be facilitated, the current research project was divided into respectively research track 1 and 2. On the basis of three studies, research track 1 examined the influence of students’ cognitive and motivational-affective characteristics. More particularly, Study 1 and 2 investigated the influence of students’ technology acceptance and students’ perceptions of instructional quality. Additionally, study 3 investigated the influence of students’ prior knowledge and motivational characteristics. Findings of study 1 and 2, reveal the importance of students’ technology acceptance and perceived instructional quality on respectively the quantity and quality of use and students’ learning outcomes. Additionally, findings of study 3 indicate that (1) students’ prior knowledge and task value can influence differences in use and that (2) students’ prior knowledge and differences in use positively influences students’ learning outcomes. As a result, research track 1 indicates that individual differences can influence the effectiveness of a 4C/ID-based online course. Nonetheless, former research indicates that the influence of individual differences can be reduced by aligning the learning environment with students’ learning needs. In order to align the online course with students’ learning needs, we should be able to detect learning process during online complex learning. Consequently, research track 2 explored in two studies whether physiological measures such as skin response measures (Study 4 and 5) and cardiovascular measures (Study 5) can be used to assess cognitive load during the online problem-solving process. Findings of study 4 reveal that changes in cognitive load can be detected by electrodermal activity when differences in cognitive load are high. Findings of study 5 appear to indicate that cognitive overload induces stress which was assessed via skin temperature and heart rate
Books on the topic "Cognitive analytics"
Hurwitz, Judith, Marcia Kaufman, and Adrian Bowles, eds. Cognitive Computing and Big Data Analytics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119183648.
Full textSharma, Sanjiv, Valiur Rahaman, and G. R. Sinha, eds. Big Data Analytics in Cognitive Social Media and Literary Texts. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4729-1.
Full textPickvance, Deborah, ed. Cognitive Analytic Supervision. Abingdon, Oxon ; New York, NY : Routledge, 2017. |: Routledge, 2016. http://dx.doi.org/10.4324/9781315716145.
Full textB, Kerr Ian, ed. Introducing cognitive analytic therapy: Principles and practice. Chichester: Wiley, 2002.
Find full textBuscema, Massimo. Intelligent Data Mining in Law Enforcement Analytics: New Neural Networks Applied to Real Problems. Dordrecht: Springer Netherlands, 2013.
Find full textRyle, Anthony. Cognitive-analytic therapy: Active participation in change : a new integration in brief psychotherapy. Chichester [England]: Wiley, 1990.
Find full textRyle, Anthony, and Ian B. Kerr, eds. Introducing Cognitive Analytic Therapy. West Sussex, England: John Wiley & Sons Ltd, 2002. http://dx.doi.org/10.1002/9780470713587.
Full textAnthony, Ryle, ed. Cognitive analytic therapy: Developments in theory and practice. Chichester: Wiley, 1995.
Find full textHuman cognitive abilities: A survey of factor-analytic studies. Cambridge: Cambridge University Press, 1993.
Find full textBieber, Irving. Cognitive psychoanalysis: Cognitive processes in psychopathology. Northvale, N.J: J. Aronson, 1995.
Find full textBook chapters on the topic "Cognitive analytics"
Gudivada, V. N., M. T. Irfan, E. Fathi, and D. L. Rao. "Cognitive Analytics." In Handbook of Statistics, 169–205. Elsevier, 2016. http://dx.doi.org/10.1016/bs.host.2016.07.010.
Full textWang, Yingxu, Victor Raskin, Julia M. Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, and Newton Howard. "Cognitive Computing." In Cognitive Analytics, 37–51. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch004.
Full textSalvador, Jaime, Zoila Ruiz, and Jose Garcia-Rodriguez. "A Review of Infrastructures to Process Big Multimedia Data." In Cognitive Analytics, 1–12. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch001.
Full textPitoglou, Stavros. "Machine Learning in Healthcare, Introduction and Real World Application Considerations." In Cognitive Analytics, 13–23. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch002.
Full textBaran, Mette L. "It is All in the Design." In Cognitive Analytics, 24–36. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch003.
Full textWang, Yingxu, Lotfi A. Zadeh, Bernard Widrow, Newton Howard, Françoise Beaufays, George Baciu, D. Frank Hsu, et al. "Abstract Intelligence." In Cognitive Analytics, 52–69. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch005.
Full textRahman, Nayem. "Data Mining Problems Classification and Techniques." In Cognitive Analytics, 70–93. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch006.
Full textManzoor, Amir. "Designs of Mixed Method Research." In Cognitive Analytics, 95–121. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch007.
Full textMalik, Sapna, and Kiran Khatter. "Malicious Application Detection and Classification System for Android Mobiles." In Cognitive Analytics, 122–42. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch008.
Full textTripathy, Abinash, and Santanu Kumar Rath. "Classification of Sentiment of Reviews using Supervised Machine Learning Techniques." In Cognitive Analytics, 143–63. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch009.
Full textConference papers on the topic "Cognitive analytics"
Pérez, Jacobo Garnacho, and David Gómez Toledo. "Cognitive Analytics of Smart Cities." In dg.o '17: 18th Annual International Conference on Digital Government Research. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3085228.3085265.
Full textGuven, Sinem, Pawel Jasionowski, Karin Murthy, Krishna Tunga, and George Stark. "COACH: Cognitive analytics for change." In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, 2017. http://dx.doi.org/10.23919/inm.2017.7987365.
Full textGe, Jike, Zuqin Chen, Can Liu, Jun Peng, Wenbo He, and Nan Zhu. "A RST-based stateful data analytics within spark." In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2017. http://dx.doi.org/10.1109/icci-cc.2017.8109779.
Full textBaciu, George, Yunzhe Wang, and Chenhui Li. "Cognitive visual analytics of multi-dimensional cloud system monitoring data." In 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862053.
Full textGibson, Andrew, Kirsty Kitto, and Jill Willis. "A cognitive processing framework for learning analytics." In Proceedins of the Fourth International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567574.2567610.
Full textInsaurralde, Carlos C., and Erik Blasch. "Cognitive Decision Support System for Avionics Analytics." In 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). IEEE, 2019. http://dx.doi.org/10.1109/dasc43569.2019.9081734.
Full textInsaurralde, Carlos C., and Erik Blasch. "Cognitive Computing Intelligence to Assist Avionics Analytics." In 2020 IEEE/AIAA 39th Digital Avionics Systems Conference (DASC). IEEE, 2020. http://dx.doi.org/10.1109/dasc50938.2020.9256796.
Full textDahlbom, A., and T. Helldin. "Supporting threat evaluation through visual analytics." In 2013 IEEE International Multi-Cognitive Methods in SituationDisciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2013). IEEE, 2013. http://dx.doi.org/10.1109/cogsima.2013.6523840.
Full textOliveira, Eduardo Araujo, Rianne Conijn, Paula De Barba, Kelly Trezise, Menno van Zaanen, and Gregor Kennedy. "Writing analytics across essay tasks with different cognitive load demands." In ASCILITE 2020: ASCILITE’s First Virtual Conference. University of New England, Armidale, 2020. http://dx.doi.org/10.14742/ascilite2020.0121.
Full textPan, Zhaotai, Yi Ge, Yu Chen Zhou, Jing Chang Huang, Yu Ling Zheng, Ning Zhang, Xiao Xing Liang, et al. "Cognitive Acoustic Analytics Service for Internet of Things." In 2017 IEEE International Conference on Cognitive Computing (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/ieee.iccc.2017.20.
Full textReports on the topic "Cognitive analytics"
Greitzer, Frank L., Christine F. Noonan, and Lyndsey Franklin. Cognitive Foundations for Visual Analytics. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1013936.
Full textMarsh, Melinda. Advanced Analytic Cognition: Thinking Dispositions. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada606062.
Full textMarsh, Melinda. Advanced Analytic Cognition: Critical Thinking. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada606648.
Full textBowden, Tim, Shaun Hutchins, John Jacobs, Lila Laux, and Steven Peters. Testing Cognitive Behavior With Emphasis on Analytical Propensity of Service Members. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada563440.
Full textAnderson, Daniel. Analytic Thinking Predicts Vaccine Endorsement: Linking Cognitive Style and Affective Orientation Toward Childhood Vaccination. Portland State University Library, January 2016. http://dx.doi.org/10.15760/honors.220.
Full textStuart, Nicole, Karina Dorrington, Andrew Sheridan, and Carmela Pestell. The Neuropsychological Correlates of Sluggish Cognitive Tempo: A Systematic Review Protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0102.
Full textBullock, Theodore H. Comparative Analytical Study of Evoked and Event Related Potentials as Correlates of Cognitive Processes. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada261388.
Full textBullock, Theodore H., and Erol Basar. Comparative Analytical Study of Evoked and Event Related Potentials as Correlates of Cognitive Processes. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada226331.
Full textKampman, Christina M., Charles A. Mangio, Thomas L. Parry, and Bonnie J. Wilkinson. Framework for Analytic Cognition (FAC): A Guide for Doing All-Source Intelligence Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada568691.
Full textLuparenko, Liliia. The Use of Electronic Open Journal Systems in Scientific and Pedagogic Research: Results of Experiment. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4465.
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