Relevant bibliographies by topics / Cognitive analytics

Academic literature on the topic 'Cognitive analytics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Cognitive analytics"

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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.

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Purpose The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics? Design/methodology/approach This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field. Findings The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture. Originality/value While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.
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Casimiro, 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.

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Narayana, 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.

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Predictive analytics plays an important role in the decision-making process and intuitive business decisions, by overthrowing the traditional instinct process. Predictive analytics utilizes data-mining techniques in order to predict the future outcomes with a high level of certainty. This advanced branch of data engineering is composed of various analytical and statistical methods which are used to develop models that predict the future occurrences. This paper examines the concepts of predictive analytics and various mining methods to achieve the prior. In conclusion, paper discusses process and issues involved in the knowledge discovery process.
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Earley, Seth. "Cognitive Computing, Analytics, and Personalization." IT Professional 17, no. 4 (July 2015): 12–18. http://dx.doi.org/10.1109/mitp.2015.55.

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Wang, 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.

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Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.
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Ozdal, 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.

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Rika, 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.

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In information systems research, the advantages of Customer Experience (CX) and its contribution to organizations are largely recognized. The CX analytics evaluate how customers perceive products, ranging from their functional usage to their cognitive states regarding the product, such as emotions, sentiment, and satisfaction. The most recent research in psychology reveals that cognition analytics research based on Classical Probability Theory (CPT) and statistical learning, which is used to evaluate people’s cognitive states, is limited due to their reliance on rational decision-making. However, the cognitive attitudes of customers are characterized by uncertainty and entanglement, resulting in irrational decision-making bias. What is captured by traditional CPT-based data science in the context of cognition aspects of CX analytics is only a small portion of what should be captured. Current CX analytics efforts fall far short of their full potential. In this paper, we set a novel research direction for CX analytics by Quantum Probability Theory (QPT). QPT-based analytics have been introduced recently in psychology research and reveal better cognition assessment under uncertainty, with a high level of irrational behavior. Adopting recent advances in the psychology domain, this paper develops a vision and sets a research agenda for expanding the application of CX analytics by QPT to overcome CPT shortcomings, identifies research areas that contribute to the vision, and proposes elements of a future research agenda. To stimulate debate and research QPT-CX analytics, we attempt a preliminary characterization of the novel method by introducing a QPT-based rich mathematical framework for CX cognitive modeling based on quantum superposition, Bloch sphere, and Hilbert space. We demonstrate the implementation of the QPT-CX model by the use case of customers’ emotional motivator assessments while implementing quantum vector space with a set of mathematical axioms for CX analytics. Finally, we outline the key advantages of quantum CX over classical by supporting theoretical proof for each key.
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Fisher, 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.

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Arias-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.

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Rostamzadeh, 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.

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The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.
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Dissertations / Theses on the topic "Cognitive analytics"

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Kovanovic, Vitomir. "Assessing cognitive presence using automated learning analytics methods." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28759.

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With the increasing pace of technological changes in the modern society, there has been a growing interest from educators, business leaders, and policymakers in teaching important higher-order skills which were identified as necessary for thriving in the present-day globalized economy. In this regard, one of the most widely discussed higher order skills is critical thinking, whose importance in shaping problem solving, decision making, and logical thinking has been recognized. Within the domain of distance and online education, the Community of Inquiry (CoI) model provides a pedagogical framework for understanding the critical dimensions of student learning and factors which impact the development of student critical thinking. The CoI model follows the social-constructivist perspective on learning in which learning is seen as happening in both individual minds of learners and through the discourse within the group of learners. Central to the CoI model is the construct of cognitive presence, which captures the student cognitive engagement and the development of critical thinking and deep thinking skills. However, the assessment of cognitive presence is challenging task, particularly given its latent nature and the inherent physical and time separation between students and instructors in distance education settings. One way to address this problem is to make use of the vast amounts of learning data being collected by learning systems. This thesis presents novel methods for understanding and assessing the levels of cognitive presence based on learning analytics techniques and the data collected by learning environments. We first outline a comprehensive model for cognitive presence assessment which builds on the well-established evidence-cantered design (ECD) assessment framework. The proposed assessment model provides a foundation of the thesis, showing how the developed analytical models and their components fit together and how they can be adjusted for new learning contexts. The thesis shows two distinct and complementary analytical methods for assessing students’ cognitive presence and its development. The first method is based on the automated classification of student discussion messages and captures learning as it is observed in the student dialogue. The second analytics method relies on the analysis of log data of students’ use of the learning platform and captures the individual dimension of the learning process. The developed analytics also extend current theoretical understanding of the cognitive presence construct through data-informed operationalization of cognitive presence with different quantitative measures extracted from the student use of online discussions. We also examine methodological challenges of assessing cognitive presence and other forms of cognitive engagement through the analysis of trace data. Finally, with the intent of enabling for the wider adoption of the CoI model for new online learning modalities, the last two chapters examine the use of developed analytics within the context of Massive Open Online Courses (MOOCs). Given the substantial differences between traditional online and MOOC contexts, we first evaluate the suitability of the CoI model for MOOC settings and then assess students’ cognitive presence using the data collected by the MOOC platform. We conclude the thesis with the discussion of practical application and impact of the present work and the directions for the future research.
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Rao, 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.

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Soukieh, 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.

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Carvalho, 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.
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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.

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Shovman, Mark. "Measuring comprehension of abstract data visualisations." Thesis, Abertay University, 2011. https://rke.abertay.ac.uk/en/studentTheses/4cfbdab1-0f91-4886-8b02-a4a8da48aa72.

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Common visualisation techniques such as bar-charts and scatter-plots are not sufficient for visual analysis of large sets of complex multidimensional data. Technological advancements have led to a proliferation of novel visualisation tools and techniques that attempt to meet this need. A crucial requirement for efficient visualisation tool design is the development of objective criteria for visualisation quality, informed by research in human perception and cognition. This thesis presents a multidisciplinary approach to address this requirement, underpinning the design and implementation of visualisation software with the theory and methodology of cognitive science. An opening survey of visualisation practices in the research environment identifies three primary uses of visualisations: the detection of outliers, the detection of clusters and the detection of trends. This finding, in turn, leads to a formulation of a cognitive account of the visualisation comprehension processes, founded upon established theories of visual perception and reading comprehension. Finally, a psychophysical methodology for objectively assessing visualisation efficiency is developed and used to test the efficiency of a specific visualisation technique, namely an interactive three-dimensional scatterplot, in a series of four experiments. The outcomes of the empirical study are three-fold. On a concrete applicable level, three-dimensional scatterplots are found to be efficient in trend detection but not in outlier detection. On a methodological level, ‘pop-out’ methodology is shown to be suitable for assessing visualisation efficiency. On a theoretical level, the cognitive account of visualisation comprehension processes is enhanced by empirical findings, e.g. the significance of the learning curve parameters. All these provide a contribution to a ‘science of visualisation’ as a coherent scientific paradigm, both benefiting fundamental science and meeting an applied need.
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Wixon, 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.

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Student affect can play a profoundly important role in students' post-school lives. Understanding students' affective states within online learning environments in particular has become an important matter of research, as digital tutoring systems have the potential to intervene at the moment that students are struggling and becoming frustrated, bored or disengaged. However, despite the importance of assessing students' affective states, there is no clear consensus about what emotions are most important to assess, nor how these emotions can be best measured. This dissertation investigates students’ self-reports of their emotions and causal attributions of those emotions collected while they are solving math problems within a mathematics tutoring system. These self-reports are collected in two conditions: through limited choice Likert response and through open response text boxes. The conditions are combined with students’ cognitive attributions to describe epistemic (neither purely affective nor purely cognitive) emotions in order to explain the relationship between observable student behaviors in the MathSpring.org tutoring system and student affect. These factors include beliefs, expectations, motivations, and perceptions of ability and control. A special emphasis of this dissertation is on analyzing the role of causal attributions for the events and appraisals of the learning environment, as possible causes of student behaviors, performance, and affect.
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Chen, 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.

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Kurup, 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.

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Larmuseau, 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.

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L’éducation change en termes de ce qui est appris et de contexte dans lequel l'apprentissage a lieu. Cela peut être en grande partie lié aux exigences en constante évolution du marché du travail actuel. En outre, la disponibilité des technologies de l'information a modifié les limites traditionnelles de l'éducation en matière de temps, d'espace et d'accès à l'information. Du point de vue de la pédagogie, la combinaison de ces deux phénomènes constitue un grand défi pour les chercheurs et les pédagogues qui doivent mettre en œuvre une pédagogie qui réponde aux exigences du contexte actuel de l'apprentissage (Ng, 2015). Le modèle de conception pédagogique à quatre composantes (modèle 4C/ID; van Merriënboer et al., 2002) est un modèle de conception pédagogique basé sur la recherche qui s'est avéré efficace pour promouvoir l’ apprentissage complexe. Néanmoins, offrir un environnement d'apprentissage en ligne basé sur un modèle de conception pédagogique basé sur la recherche n'est pas nécessairement une garantie de son efficacité. Comme l'apprenant est un agent actif dans le processus d'apprentissage en ligne, l'efficacité des environnements d'apprentissage dépend largement des caractéristiques cognitives et motivationnelles-affectives de l'étudiant. Afin d'étudier les caractéristiques qui peuvent influencer l'efficacité d'un cours en ligne basé sur les 4C/ID et la manière dont l'efficacité peut être facilitée, le projet de recherche actuel a été divisé en deux pistes de recherche. Sur la base de trois études, la première piste de recherche a examiné l'influence des caractéristiques cognitives et motivationnelles-affectives des étudiants sur l'efficacité des environnements d'apprentissage en ligne. Plus particulièrement, les études 1 et 2 ont examiné l'influence de l'acceptation de la technologie par les étudiants et la perception de la qualité de l'enseignement par les étudiants. En outre, l'étude 3 a examiné l'influence des connaissances antérieures et des caractéristiques motivationnelles des élèves sur les différentes utilisations des composantes et les résultats de l'apprentissage. Les résultats des études 1 et 2 révèlent l'importance de l'acceptation des technologies par les étudiants et de la perception de la qualité de l'enseignement sur respectivement la quantité et la qualité de l'utilisation et les résultats d'apprentissage des étudiants. En outre, les résultats de l'étude 3 indiquent que (1) les connaissances antérieures et la motivation intrinsèque des étudiants peuvent influencer les différences d'utilisation et que (2) les connaissances antérieures des étudiants et les différences d'utilisation des composantes influencent positivement les résultats d'apprentissage des étudiants. Par conséquent, la piste de recherche 1 indique que les différences individuelles peuvent influencer l'efficacité d'un environnement d'apprentissage en ligne. Néanmoins, des recherches antérieures indiquent que l'influence des différences individuelles peut être modéré en alignant l'environnement d'apprentissage sur les besoins d'apprentissage des étudiants. Afin d'aligner le cours en ligne sur les besoins d'apprentissage des étudiants, nous devrions être en mesure de détecter le processus d'apprentissage au cours de l'apprentissage complexe en ligne. Par conséquent, la deuxième piste de la recherche a examiné dans deux études si des données physiologiques liées à la peau (études 4 et 5) et les données physiologiques cardiovasculaires (étude 5) peuvent être utilisées pour évaluer la charge cognitive pendant le processus de résolution de problèmes en ligne. Les résultats de l'étude 4 révèlent que les changements de charge cognitive peuvent être détectés par l'EDA lorsque les différences de charge cognitive sont élevées. Les résultats de l'étude 5 semblent indiquer que la surcharge cognitive induit un stress qui a été évalué via la température de la peau et la fréquence cardiaque
The 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
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Books on the topic "Cognitive analytics"

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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.

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Sharma, 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.

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Pickvance, Deborah, ed. Cognitive Analytic Supervision. Abingdon, Oxon ; New York, NY : Routledge, 2017. |: Routledge, 2016. http://dx.doi.org/10.4324/9781315716145.

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B, Kerr Ian, ed. Introducing cognitive analytic therapy: Principles and practice. Chichester: Wiley, 2002.

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Buscema, Massimo. Intelligent Data Mining in Law Enforcement Analytics: New Neural Networks Applied to Real Problems. Dordrecht: Springer Netherlands, 2013.

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Ryle, Anthony. Cognitive-analytic therapy: Active participation in change : a new integration in brief psychotherapy. Chichester [England]: Wiley, 1990.

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Ryle, 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.

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Anthony, Ryle, ed. Cognitive analytic therapy: Developments in theory and practice. Chichester: Wiley, 1995.

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Human cognitive abilities: A survey of factor-analytic studies. Cambridge: Cambridge University Press, 1993.

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Bieber, Irving. Cognitive psychoanalysis: Cognitive processes in psychopathology. Northvale, N.J: J. Aronson, 1995.

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Book chapters on the topic "Cognitive analytics"

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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.

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Wang, 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.

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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 field of cognitive computing.
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Salvador, 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.

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In the last years, the volume of information is growing faster than ever before, moving from small to huge, structured to unstructured datasets like text, image, audio and video. The purpose of processing the data is aimed to extract relevant information on trends, challenges and opportunities; all these studies with large volumes of data. The increase in the power of parallel computing enabled the use of Machine Learning (ML) techniques to take advantage of the processing capabilities offered by new architectures on large volumes of data. For this reason, it is necessary to find mechanisms that allow classify and organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will be reviewed. This work analyzes different studies carried out on the use of ML for processing large volumes of data (Big Multimedia Data) and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data.
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Pitoglou, 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.

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Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.
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Baran, 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.

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This chapter introduces the various design choices researchers need to decide on prior to conducting the study. In the first section of this chapter a detailed description of research design is followed by an explanation that the type of information that is collected is based on whether the research question is descriptive, explanatory, or exploratory. The major strategic implementation methods for quantitative, qualitative, and mixed methods are then discussed. The three strategies for mixed methods research Parallel Convergent, Sequential, and Embedded Design are presented in detail along with the rationale for their use. Finally, in the last section, the strands or sequencing of the data collection phase of the study is explained.
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Wang, 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.

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Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.
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Rahman, 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.

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Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.
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Manzoor, 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.

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Mixed methods research is becoming an increasingly popular approach in the discipline fields of sociology, psychology, education and health sciences. Calls for the integration of quantitative and qualitative research methods have been advanced in these fields. A key feature of mixed methods research is its methodological pluralism, which frequently results in research which provides broader perspectives than those offered by mono-method designs. The central premise of mixed methods is that the use of quantitative and qualitative approaches in combination provides a better understanding of research problems and complex phenomena than either approach alone. The purpose of this chapter is to review designs of mixed methods research. The study surveys the common designs of mixed methods research and examine the main characteristics of each in terms of purposes, strengths, and issues, and posits suggestions on the application of these designs.
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Malik, 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.

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The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.
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Tripathy, 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.

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Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
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Conference papers on the topic "Cognitive analytics"

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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.

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Guven, 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.

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Ge, 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.

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Baciu, 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.

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Gibson, 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.

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Insaurralde, 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.

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Insaurralde, 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.

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Dahlbom, 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.

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Oliveira, 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.

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Essay tasks are a widely used form of assessment in higher education. Writing analytics can assist with challenges related to using essay tasks at scale and to identifying different issues in academic integrity. In this paper, we combined two techniques to investigate how students’ writing analytics varied across essay tasks with different cognitive load, considering both their typing behavior (i.e., writing process) and writing style (i.e., writing product). We also examined their relationship across these essay tasks. Findings showed that writing processes change across tasks with different cognitive load: when cognitive load increases, the interword intervals (indicator of planning and/or reviewing processes) increased, the burst length (indicator of translation processes) decreased, and the number of revisions per minute (indicator of reviewing processes) decreased. In contrast to the relation between the writing process and cognitive load, the relation between the writing product and cognitive load was found less clear. The results showed small and mixed effects of the tasks differing in cognitive load on the different writing product metrics. Hence, although the writing product follows from the writing process, the relation between cognitive load and the writing product and process appears to be less straightforward.
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Pan, 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.

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Reports on the topic "Cognitive analytics"

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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.

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Marsh, Melinda. Advanced Analytic Cognition: Thinking Dispositions. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada606062.

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Marsh, Melinda. Advanced Analytic Cognition: Critical Thinking. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada606648.

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Bowden, 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.

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Anderson, 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.

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Stuart, 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.

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Review question / Objective: The objective the current review is to delineate the cognitive profile of SCT, particularly where it is similar to or different from ADHD-related inattention. In addition, the review will provide an analysis of methodological factors that might account for discrepancies in research findings and guidance for future studies. Condition being studied: Sluggish cognitive tempo (SCT) is a constellation of symptoms originally identified among children with the inattentive subtype of attention deficit hyperactivity disorder (ADHD-I). These symptoms include daydreaming, inconsistent alertness, hypoactivity and lethargy. Although there is considerable overlap with ADHD-I, factor analytic and convergent and discriminant validity studies suggest that SCT is a distinct construct. Moreover, there is evidence that SCT may be common in a number of other disorders, including depression and autism - suggesting that SCT might represent an important transdiagnostic construct.
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Bullock, 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.

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Bullock, 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.

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Kampman, 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.

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Luparenko, 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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The article deals with the problem of the use of electronic open journal systems in scientific and pedagogical research as well as the formation of ICT competence of researchers on the use of such systems. The concepts of electronic journal system (EJS) are considered. The most common kinds of electronic journal systems are revealed (proprietary, local (in-house), open and cloud journal systems). The criteria for accessing the effectiveness of the electronic open journal systems (EOJS) use in scientific and pedagogical research are described (normative, organizational and communication, effective), as well as their indicators. The organizational and pedagogical model of EOJS use in scientific and pedagogical researches is developed. The definition of "ICT competence of researchers on the use of EOJS in scientific and pedagogical research" is provided; its components are described; criteria (axiological, cognitive, praxeological, adaptive) and indicators of its formation are defined. The model of formation of this competence is provided. The main stages of the experimental process (2010–2018) are described. The results of the formation of ICT-competence of researchers and information-analytical monitoring of the scientific journals of the National Academy of Educational Sciences of Ukraine are presented.
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