There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a model in which the topographical differences are quite distinguishable. In our proposed model, we have translated this topographical situation into quantities, which are attributed to the nodes. The result is an edge-weighted graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.

One of the most important issues in language processing is the problem of ambiguity. In general, the systems developed for disambiguation can be classified into two groups: rule-based and statistical. Markofs procedure is one of the statistical models for disambiguation used frequently in linguistic studies in general, and in computational linguistics in particular. So far, this procedure has been one of the best methods for disambiguation especially for parts of speech, in addition to automatic tagging of language corpora. In this paper, the methods of tagging and disambiguation within the framework of Markof’s model are described and then a simplified version of the formula based on the first edition of ' Constituent-Likelihood Automatic Word-tagging System' (CLAWS) which follows Markof’s procedure will be modeled. In this regard, two sample sentences are chosen, and then for each word or syntactic role, the frequency of occurrences and also its possible transfer from that word to the next one or from the syntactic role of that word to the syntactic role of the next one will be recorded in separate tables.The numeric value related to the multiplication of these numbers, that is, the computation of the probability in the structures of the two words and the sum of these probabilities is the basis of language analysis. The greater the number, the greater the possibilities of the tags found for the words to be correct in new sentences. If instead of the two sample sentences, we analyze the sentences of a language corpus using this method and record the probabilities of two or three - words structures in tables, then we can compute the sum of these probabilities and provide a computer program so as to tag and disambiguate different sentences.

In this research we introduce a new distribution center (DC) location model that incorporates working inventory and safety stock inventory costs at the distribution centers. In addition, the model incorporates transport costs from the suppliers to the DCs that explicitly reflect economies of scale through the use of a fixed cost term.The model was formulated as a non-linear integer-programming problem.The algorithms were tested on the problems with 15 retailers and 5 distribution centers. We use SOLVER of Excel for modeling, solution and sensitivity analysis of the results.Finally, the sensitivity of the results to changes in key parameters including the fixed cost of placing orders was discussed. Significant reductions in these costs might be expected from e-commerce technologies.The model suggests that as these costs decrease, it is optimal to locate additional facilities.

The Intelligent virtual assistant (IVA) also called AI assistant or digital assistant is software developed as a product by organizations like Google, Apple, Microsoft and Amazon. Virtual assistant based on Artificial Intelligence which works and processes on natural language commands given by humans. It helps the user to work more efficiently and also saves time. It is human friendly as it works on natural language commands given by humans. Voice-controlled Intelligent Virtual Assistants (IVAs) have seen gigantic development as of late on cell phones and as independent gadgets in individuals‘ homes. The intelligent virtual assistant is very useful for illiterate and visually impaired people around the world. While research has analyzed the expected advantages and downsides of these gadgets for IVA clients, barely any investigations have exactly assessed the need of security and trust as a singular choice to use IVAs. In this proposed work, different IPA users and non-users (N=1000) are surveyed to understand and analyze the barriers and motivations to adopting IPAs and how users are concerned about data privacy and trust with respect to organizational compliances and social contract related to IPA data and how these concerns have affected the acceptance and use of IPAs. We have used Naï ve Byes Classifier to compute trust in IVA devices and further evaluate probability of using different trusted IVA devices.

Background and Objectives: Visual attention is a high-order cognitive process of the human brain that defines where a human observer attends. Dynamic computational visual attention models are modeled on the behavior of the human brain. They can predict what areas a human will pay attention to when viewing a scene such as a video. However, several types of computational models have been proposed to provide a better understanding of saliency maps in static and dynamic environments; most of these models are used for specific scenes. In this paper, we propose a model that can generate saliency maps in various dynamic environments with complex scenes. Methods: We used a deep learner as a mediating network to combine basic saliency maps with appropriate weighting. Each of these basic saliency maps covers an essential feature of human visual attention, and ultimately the final saliency map is very similar to human visual behavior. Results: The proposed model is run on two datasets, and the generated saliency maps are evaluated by different criteria such as ROC, CC, NSS, SIM, and KLdiv. The results show that the proposed model has a good performance compared to other similar models. Conclusion: The proposed model consists of three main parts, including basic saliency maps, gating network, and combinator. This model was implemented on the ETMD dataset, and the resulting saliency maps (visual attention areas) were compared with some other models in this field by evaluation criteria, and their results were evaluated. The results obtained from the proposed model are acceptable, and based on the accepted evaluation criteria in this area; it performs better than similar models.

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models proposed in several past decades, it is widely recognized that exchange rates are extremely difficult to forecast. Artificial Neural Networks (ANNs) are one of the most accurate and widely used forecasting models that have been successfully applied for exchange rate forecasting. In this paper, a hybrid model is proposed based on the basic concepts of artificial neural networks in order to yield more accurate results than the traditional ANNs in short span of time situations. Three exchange rate data sets—the British pound, the United States dollar, and the Euro against the Iran rial-are used in order to demonstrate the appropriateness and effectiveness of the proposed model. Empirical results of exchange rate forecasting indicate that hybrid modelis generally better than artificial neural networks and other models presented for exchange rate forecasting, in cases where inadequate historical data are available. Therefore, our proposed model can be a suitablealternative model for financial markets to achieve greater forecasting accuracy, especiallyin incomplete data situations.

Introduction: Three types of images can be considered in the mind: Remembering images which have already been seen, making abstract image from all images of a concept already existing in the mind or creating new images for new concepts using existing images and concepts. “Creative Imagery” refers to the latter. Method: computational modelling of Imagery – as a cognitive process – describes mechanisms and processes of imagery. It embodies the description based on computer science. In this paper, the process of human creative imagery is described using Islamic Philosophy approach.Results: According to the Islamic Philosophy, Imaginative faculty can compose and decompose concepts (existing in memory) and forms (existing in Imaginative Faculty) for Creative Mental Imagery. Role of Imaginative Faculty can be modeled with a multi-level probabilistic algorithm. As an example, running path of the algorithm for creating image of “tree with eye and mouth” is shown. The computational model is able to produce those images which human produce for the new concept.Conclusion: Creating new image uses composition of basic elements (forms and concepts). Thus for modelling creative imagery two fundamental aspects should be considered: Appropriate representation of forms and concepts and discovery and modeling mental processes combining these components.