Please use this identifier to cite or link to this item:

Title: Threat Artificial Intelligence and Cyber Security in Health Care Institutions
Authors: Fernandes, Ana
Figueiredo, Margarida
Carvalho, Filomena
Neves, José
Vicente, Henrique
Keywords: Threat Artificial Intelligence
Logic Programming
Knowledge Representation and Reasoning
Artificial Neural Networks
Issue Date: 2021
Citation: Fernandes, A., Figueiredo, M., Carvalho, F., Neves, J. & Vicente, H., Threat Artificial Intelligence and Cyber Security in Health Care Institutions. Studies in Computational Intelligence, 972: 319-342, 2021.
Abstract: In this work we go beyond what is called unsupervised learning, a decision- -making method that results in large numbers of false positives and negatives. The study was carried out in cryopreservation laboratories and aims to gain access to the General Data Protection Regulation (GDPR) implementation. Indeed, on the one hand, using Threat Artificial Intelligence, Chaos, Entropy and Security (TAICE&S) based methodology for problem solving one may mimic behaviors that are similar to the best human analysts. With the entry into force of the GDPR in the health institutions of the European Union (EU), stronger rules (TAICE based) on data protection (Security) mean people have more control over their personal data and businesses benefit from a level playing field. To respond to this challenge, a workable tool had to be built exploring the dynamics between TAICE&S and Logic Programming for Knowledge Representation and Reasoning, leading to the implementation of an agency based on TAICE/Cyber Security based techniques for problem solving, which is consistent with an Artificial Neural Network approach to problem definition. It is therefore possible to provide a full-bodied TAICE method to assist in threat identification and evaluation, activity prediction, mitigation, and response strategies. Using TAI procedures, one may identify patterns and matches in the activity of threat players, that combined with the issues of Chaos and Entropy gives us an opportunity to mimic how qualified specialists react in scenarios where models break off.
ISSN: 1860-949X (paper)
1860-9503 (electronic)
Type: article
Appears in Collections:CIEP - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

Files in This Item:

File Description SizeFormat
2021_AICS_2021_RD.pdf3.4 MBAdobe PDFView/Open
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois