539 research outputs found
Towards neural-symbolic integration: the evolutionary neural logic networks
No full textThis work presents the application of a new methodology for the production of neural logic networks into two real-world problems from the medical domain. Namely, we apply grammar guided genetic programming using cellular encoding for the representation of neural logic networks into population individuals. The application area is consisted of the diagnosis of diabetes and the diagnosis of the course of hepatitis patients. The system is proved able to generate arbitrarily connected and interpretable evolved solutions leading to potential knowledge extraction
Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures
No full textEvolutionary Neural Logic Networks in Two Medical Decision Tasks
No full textTwo real-world problems of the medical domain are addressed in this work using a novel approach belonging to the area of neural-symbolic systems. Specifically,we apply evolutionary techniques for the development of neural logic networks of arbitrary length and topology. The evolutionary algorithm is consisted of grammar guided genetic programming using cellular encoding for the representation of neural logic networks into population individuals. The application area is consisted of the diagnosis of patient postoperative treatment and the diagnosis of the Breast cancer. The extracted solutions maintain their interpretability into simple and comprehensible logical rules. The overall system is shown capable to generate arbitrarily connected and interpretable evolved solutions leading to potential knowledge extraction
Predicting Defects in Software Using Grammar-Guided Genetic Programming
No full textThe knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by means of computational intelligence has only recently become evident. In this paper, we investigate the capability of the genetic programming approach for producing solution composed of decision rules. We applied the model into four software engineering databases of NASA. The overall performance of this system denotes its competitiveness as compared with past methodologies, and is shown capable of producing simple, highly accurate, tangible rules
The Δρομοδείχτης της Ελλάδος of 1824 and Athanasios Stageirites (Τίτλος περίληψης)
No full textσ. [281]-290Κείμενο στα ελληνικά με περίληψη στα αγγλικά με τον τίτλο: The Δρομοδείχτης της Ελλάδος of 1824 and Athanasios StageiritesThe article first examines the close relationship between the publication “Δρομοδείχτης της Ελλάδος” [1824] and the publication “Ηπειρωτικά” (1819) by Athanasios Stageirites and then suggests that Athanasios Stageirites is the likeliest author of the “Δρομοδείχτης της Ελλάδος”.Δωδώνη: Τεύχος Πρώτο: επιστημονική επετηρίδα του Τμήματος Ιστορίας και Αρχαιολογίας της Φιλοσοφικής Σχολής του Πανεπιστημίου Ιωαννίνων; Τόμ. 43-44 (2014-2015
Dataset in support of the Southampton doctoral thesis 'The boatbuilding tradition of the Aegean during the Late Neolithic – Early Bronze Age periods. Typological classification, digital reconstruction and seakeeping assessment'
No full textDataset in support of the Southampton doctoral thesis 'The boatbuilding tradition of the Aegean during the Late Neolithic – Early Bronze Age periods. Typological classification, digital reconstruction and seakeeping assessment' Appendix D - Resistance data and Appendix C - Stability data.
This dataset is focused on two appendices:
Appendix D - Resistance data. D.1 Resistance data produced by the author via MAXSURF Resistance for this thesis.
Appendix C - Stability data
C1. Stability data – STIX and ISO criteria, produced by the author via MAXSURF Stability software for his thesis
This research was funded by Southampton Marine and Maritime Institute (SMMI), Vice-Chancellor's Scholarship, Greek Archaeological Committee UK (GACUK)
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Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies
No full textThe paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin
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