Radial Basis Function Neural Networks : A Review

Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

An Intelligent Information Retrieval Approach Based on Two Degrees of Uncertainty Fuzzy Ontology

In spite of the voluminous studies in the field of intelligent retrieval systems, effective retrieving of information has been remained an important unsolved problem. Implementations of different conceptual knowledge in the information retrieval process such as ontology have been considered as a solution to enhance the quality of results. Furthermore, the conceptual formalism supported by typical ontology may not be sufficient to represent uncertainty information due to the lack of clear-cut boundaries between concepts of the domains. To tackle this type of problems, one possible solution is to insert fuzzy logic into ontology construction process. In this article, a novel approach for fuzzy ontology generation with two uncertainty degrees is proposed. Hence, by implementing linguistic variables, uncertainty level in domain's concepts (Software Maintenance Engineering (SME) domain) has been modeled, and ontology relations have been modeled by fuzzy theory consequently. Then, we combined these uncertain models and proposed a new ontology with two degrees of uncertainty both in concept expression and relation expression. The generated fuzzy ontology was implemented for expansion of initial user's queries in SME domain. Experimental results showed that the proposed model has better overall retrieval performance comparing to keyword-based or crisp ontology-based retrieval systems

Identifying the Effective Factors in Making Trust in Online Social Networks on the perspective of Iranian experts Using Fuzzy ELECTRE

this paper attempts to rank the effective factors in making trust in social networks to provide the possibility of attracting and increasing users’ trust on these social networks for providers and designers of online social networks. Identifying the effective factors in making trust in social networks is a multi-criteria decision making problem and most of effective factors are ambiguous and uncertain, thereby this article uses Fuzzy ELECTRE to rank them. By implementing Fuzzy ELECTRE on gathered data, respectively «usability factor», «supporting up to date technology factor», «integrity» and «the rate of ethics factor» are on the top of effective factors in making trust in users. In general, «web features» and «technology features» have a higher degree of importance than «security features», «individual-social features» and «cultural features». Ranking of Fuzzy ELECTRE comparison ranking of Fuzzy TOPSIS and Fuzzy ELECTRE method becomes validate because Spearman correlation coefficients is 0/867. Result of sensitivity analysis on changing weight of criteria shows that Fuzzy ELECTRE isn’t affected by ambiguity and uncertainty in inputs

Why Iranian universities have not been successful in implementing e-learning systems? (Comparative analysis of Iranian and Turkish universities in the face of the Corona pandemic)

In the last decade, information technology has been identified as a key factor in most countries’ development, including the countries of Iran’s Outlook 1404, and has been used in particular to restructure and redesign higher education systems in these countries. Turkey is one of the countries experiencing the most growth through ICT development and its impact on higher education is highly evident. In 2020, more than 40% of the population of eight million Turkish students studied in distance courses. In contrast, despite the not-so-delayed and powerful entry of Iranian universities in e-learning field, a very small percentage of the country’s students (less than 10%) studied in distance courses, while this trend is also declining. Given the need to implement e-learning courses in the face of the Corona pandemic, this article seeks to answer the question of why Iranian universities have failed in the field of e-learning (as the most important manifestation of distance learning in the information age), a comparative study of four major Iranian universities (Tehran, Khajeh Nasir, Shiraz, and Isfahan) with four important Turkish universities (Ankara, Hajat Tepe, Ghazi and Technical University of Middle East) in the face of the Corona pandemic is attempted. The ability of universities to create an e-learning environment has been investigated by sending a team of experts to the mentioned universities and collecting real data. The results of this survey show that the capacity of selected universities in Turkey is at the "medium and above average" level, but in Iran is estimated at a "relatively weak" level. Finally, based on an analysis of the reasons for the failure of Iran’s selected universities to implement e-learning, especially in comparison with the competing universities in the outlook 1404, some more coherent planning and policy guidance are provided