1,721,905 research outputs found
Guest editorial: special issue on "Intelligent Systems, Design and Applications (ISDA'2009)".
No full textProceedings of The Fourth International Symposium on Information Assurance and Security (IAS 2008)
No full textIntelligent Systems Design and Applications, 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12-14, 2022 - Volume 2
No full textThis book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering
Intelligent Systems Design and Applications, 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12-14, 2022 - Volume 4
No full textThis book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering
Modelling Radial Basis Functions with Rational Logic Rules.
No full textConnectionist systems such as Radial Basis Function Neural Networks and similar architectures are commonly applied to solve problems of learning relations from available examples. To overcome their limits in clarity of representation, they are often interfaced with symbolic rule-based systems, provided that the information they have memorized can be interpreted. In this paper, an implementation of a RBF-like system is presented using only gradual fuzzy rules learned directly from data. It is then shown how it can learn second-order, fuzzy relations
Intelligent Systems Design and Applications, 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12-14, 2022 - Volume 1
No full textThis book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering
Intelligent Systems Design and Applications, 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12-14, 2022 - Volume 3
No full textThis book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering
A General Framework to Analyze the Fault-Tolerance of Unstructured P2P Systems
No full textThis work presents a study on the fault-tolerance of unstructured P2P overlays, modeled as complex networks. A framework is proposed to derive the peers’ degree distribution, once the P2P system is described through the evolution laws characterizing the distributed protocol, the attachment and failure rates. From the degree distribution, estimations may be derived on the mean number of m−neighbors, as well as the diameter of the net. We analyze three different P2P distributed protocols. The analytical tool is compared with results coming from simulation. Outcomes confirm that the approach can be employed to dynamically tune the peers’ attachment rate and maintain the desired topology of the P2P network
Early Prediction of COVID-19 Outcome: Contrasting Clinical Scores and Computational Intelligence Methods
No full textTriaging incoming patients is critical for an optimal allocation of hospital resources, especially during a pandemic, when these tend to be quickly depleted. A typical approach for predicting patients’ outcomes relies on clinical scores such as the Charlson Comorbidity Index (CCI). CCI-based triaging is a reliable approach for estimating the mortality risk in the general patients’ population. However, this score is not optimized for predicting mortality in specific populations such as the one represented by COVID inpatients, often the most represented population in the emergency department cohorts during the current pandemic. Motivated by this, this chapter describes the development of a new COVID-19-specific clinical score: The General Assessment of SARS-CoV-2 patients Score (GASS). The score builds on the clinical experience gained during the first phase of the pandemic, and it is based on both clinical and laboratory data. It was aimed at predicting the 30-day mortality outcome of hospitalized COVID-19 patients and showed markedly better accuracy than the CCI. Furthermore, this chapter introduces an additional predictive model based on a classical Computational Intelligence method. Specifically, it describes the development and validation of a feedforward artificial Neural Network (NN) that automatically maps patients’ clinical and laboratory data to a 30-day mortality-risk score. Critically, the NN-based method was shown to be more accurate at predicting 30-day mortality of COVID-19 patients than both the CCI and GASS scores. However, the intrinsic black-box nature of the NN-based method makes it hard to reach an intuitive understanding of the internal computations underlying its decision process. This might affect its general acceptance among clinicians, and lead them to prefer using the GASS score
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