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An entropic approach to technology enable learning and social computing

dc.contributor.authorAlves, Victor
dc.contributor.authorMiranda, José
dc.contributor.authorDawa, Hossam
dc.contributor.authorFernandes, Filipe
dc.contributor.authorPombal, Fernanda
dc.contributor.authorRibeiro, Jorge
dc.contributor.authorFdez-Riverola, Florentino
dc.contributor.authorAnalide, Cesar
dc.contributor.authorVicente, Henrique
dc.contributor.authorNeves, José
dc.date.accessioned2023-02-16T10:32:51Z
dc.date.available2023-02-16T10:32:51Z
dc.date.issued2022-11-24
dc.description.abstractUnderstanding one's own behavior is challenging in itself; understanding a group of different individuals and the many relationships between these individuals is even more complex. Imagine the amazing complexity of a large system made up of thousands of individuals and hundreds of groups, with countless relationships between those individuals and groups. However, despite this difficulty, organizations must be managed. Indeed, ultimately the organization's work is done by people, individually or collectively, alone or in combination with technology. Therefore, organizational behavior management is the central task of management work-it involves understanding the behavior patterns of individuals, groups, and organizations, predicting what behavioral reactions will be elicited by various managerial actions and finally applying this understanding. Undeniably, society's work is often done by organizations, and the role of management is to make organizations do that work. Without it, our entire society would quickly stop operating. Not only would the products you have come to know and love swiftly to evaporate from store shelves; food itself would suddenly become scarce, having drastic effects on huge numbers of people. To this end, the term Technology-Enhanced Learning is used to support workers' learning about technology; the gap between what is understood to be satisfactory and the current level of knowledge of the workforce is addressed by a Logic-programming-based Social Computing Framework entitled An Entropic Approach to Knowledge Representation and Reasoning, which relies on computational structures built on Artificial Neural Networks and Cases-based Thinking, as well as predictions and/or assessments, to empower the level of knowledge of the employees, here in technology, later in other areas.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3233/FAIA220436pt_PT
dc.identifier.eid85146727123
dc.identifier.isbn9781643683560
dc.identifier.urihttp://hdl.handle.net/10400.14/40277
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIOS Press BVpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectCase-based reasoningpt_PT
dc.subjectComputational sustainabilitypt_PT
dc.subjectEntropypt_PT
dc.subjectSocial computingpt_PT
dc.subjectTechnology enable learningpt_PT
dc.titleAn entropic approach to technology enable learning and social computingpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage153pt_PT
oaire.citation.startPage140pt_PT
oaire.citation.titleMachine Learning and Artificial Intelligence - Proceedings of MLIS 2022pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT

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