Annually, WEB search engine providers spend a lot of money on re-ranking DOCUMENTs in search engine result pages (SERP). Click models provide advantageous information for re-ranking DOCUMENTs in SERPs through MODELING interactions among users and search engines. Here, three modules are employed to predict users' clicks on SERPs simultaneously, the first module tries to predict users' click behaviors using Probabilistic Graphical Models, the second module is a Time-series Deep Neural Click Model which predicts users' clicks on DOCUMENTs and finally, the third module is a similarity-based measure which creates a graph of DOCUMENT-query relations and uses SimRank Algorithm to predict the similarity. After running these three simultaneous processes, three click probability values are fed to an MLP classifier as inputs. The MLP classifier learns to decide on top of the three preceding modules, then it predicts a probability value which shows how probable a DOCUMENT is to be clicked by a user. The proposed system is evaluated on the Yandex dataset as a standard click log dataset. The results demonstrate the superiority of our model over the well-known click models in terms of perplexity.

The main purpose of this project was to design a WEB-based health DOCUMENT automation program for fitness clubs. This research was carried out using information such as fitness features of the clubs and the types of users. The existing literature on this subject was reviewed to reflect the limitations and suggestions of previous studies in the program design. The types of services and required processes were identified by field surveys, and program designing continued in both the database and user sections. Creating a WEBbased context, predicting user education and the possibility of developing a smart phone app were among the factors that were considered by previous reviews of similar healthrelated software. The results of this design can help improve specialized services. This design can also be employed at the University Health Center to determine the essential characteristics of each level of user.

This paper discusses about the future of the World Wide WEB development, called Semantic WEB. Undoubtedly, WEB service is one of the most important services on the Internet, which has had the greatest impact on the generalization of the Internet in human societies. Internet penetration has been an effective factor in growth of the volume of information on the WEB. The massive growth of information on the WEB has led to some problems, the most important one is search query. Nowadays, search engines use different techniques to deliver high quality results, but we still see that search results are not ideal. It should also be noted that information retrieval techniques to a certain extent can increase the search accuracy. Most of the WEB content is designed for human usage and machines are only able to understand and manipulate data at word level. This is the major limitation for providing better services to WEB users. The solution provided for this topic is to display the content of the WEB in such a way that it can be readily understood and comprehensible to the machine. This solution, which will lead to a huge transformation on the WEB is called the Semantic WEB and will begin. Better results for responding to the search for semantic WEB users, is the purpose of this research. In the proposed method, the expression, searched by the user, will be examined according to the related topics. The response obtained from this section enters to a rating system, which is consisted of a fuzzy decision-making system and a hierarchical clustering system, to return better results to the user. It should be noted that the proposed method does not require any prior knowledge for clustering the data. In addition, accuracy and comprehensiveness of the response are measured. Finally, the F test is applied to obtain a criterion for evaluating the performance of the algorithm and systems. The results of the test show that the method presented in this paper can provide a more precise and comprehensive response than its similar methods and it increases the accuracy up to 1. 22%, on average.

The principal aim of a search engine is to provide the sorted results according to the user’ s requirements. To achieve this aim, it employs the ranking methods to rank the WEB DOCUMENTs based on their significance and relevance to the user’ s query. The novelty of this work is to provide a user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the ranking system is considered as the agent of the learning system and the selection of DOCUMENTs is displayed to the user as the agent's action. The reinforcement signal in this system is calculated based on the user's click on the DOCUMENTs. Action-values in the RRLUFF algorithm are calculated for each feature of the DOCUMENT-query pair. In the RRLUFF method, each feature is scored based on the number of the DOCUMENTs related to the query and their position in the ranked list of that feature. For learning, the DOCUMENTs are sorted according to the modified scores for the next query. Then according to the position of a DOCUMENT in the ranking list, some DOCUMENTs are selected based on the random distribution of their scores to display to the user. The OHSUMED and DOTIR benchmark datasets are used to evaluate the proposed method. The evaluation results indicate that the proposed method is more effective than the related methods in terms of P@n, NDCG@n, MAP, and NWN.

Background and Aim: In patients with complete denture, some clinicians have used modelling plastic impression compound (MPIC) along tissue conditioner (TC) materials simultaneously. Little information is available on the composition of these materials and the interaction between them. The purpose of this study was to evaluate the influence of two components of MPIC on the structure and chemical composition of TC.Materials and Methods: In this experimental study, MPIC specimens were provided in 25×2 mm discs. Specimens were randomly divided into three groups and were immersed in ethanol 70%, plasticizer (dibutyl phthalate) and a mixture of them (ethanol 70% and dibutyl phthalate). All of the discs were weighed with a digital balance before and 2, 4, 6 and 24 hours after immersion. Values were analyzed by non parametric Kruskal-Wallis (a= 0.05) and SPSS 16 for Windows (SPSS Inc., Chicago, IL) was used for statistical analysis.Results: Statistical analysis indicated significant differences among all groups (p>.05).Conclusion: Dibutyl phthalate (DBP) had high impact on the solubility of MP, while the mixture of dibutyl phthalate (DBP) and ethanol demonstrated the highest impact.