Browsing by Author "Fernandes, Filipe"
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- An entropic approach to technology enable learning and social computingPublication . Alves, Victor; Miranda, José; Dawa, Hossam; Fernandes, Filipe; Pombal, Fernanda; Ribeiro, Jorge; Fdez-Riverola, Florentino; Analide, Cesar; Vicente, Henrique; Neves, JoséUnderstanding 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.
- Contributions of artificial intelligence to decision making in nursing: a scoping review protocolPublication . Fernandes, Filipe; Santos, Paulo; Sá, Luís; Neves, JoséBackground: Artificial intelligence (AI) techniques and methodologies for problem solving are emerging as formal tools essential to assist in nursing care. Given their potential to improve workflows and to guide decision making, several studies have been developed; however, little is known about their impact, particularly on decision making. Objective: The aim of this study was to map the existing research on the use of AI in decision making in nursing. With this review protocol, we aimed to map the existing research on the use of AI in nursing decision making. Methods: A scoping review was conducted following the framework proposed by the Joanna Briggs Institute (JBI). The search strategy was tailored to each database/repository to identify relevant studies. The contained articles were the targets of the data extraction, which was conducted by two independent researchers. In the event of discrepancies, a third researcher was consulted. Results: This review included quantitative, qualitative and mixed method studies. Primary studies, systematic reviews, dissertations, opinion texts and gray literature were considered according to the three steps that the JBI has defined for scoping reviews. Conclusions: This scoping review synthesized knowledge that could help advance new scientific developments and find significant and valuable outcomes for patients, caregivers and leaders in decision making. This review was also intended to encourage the development of research lines that may be useful for the development of AI tools for decision making.