Salinity stress is the most important osmotic stress that limits growth and production of crop via changing ion and osmotic balance. In order to investigation of salinity stress effects on germination، vegetative and some germination indices an experiment conducted by factorial based on randomized completely block designs with 4 replications in 2010 in department of agriculture of Urmia Payame Nor. The factors were 8 genotypes (Shahriar، Sardari، Sardari 39، Sardari 101، Zarin، Arvand، fgs and Azar 2 and 6 level of salinity (0 -2-4-8-12-16) ds/m from Nacl. The results showed that different levels of salinity had significantly effects on germination percent، root dry weight، seedling dry weight، plantlet dry weight، germination rate، germination uniformity، stem length، stress tolerance index and root length. With increasing salinity stress all of traits decreased، so the highest obtained from control treatment and the lowest obtained from salinity level of 16 ds/m. The genotypes respond differently to salinity levels. It seems genotypes of Zarin، Arvand and Sardari have more tolerance to salinity conditions. Genotypes of Sardari 101 and Azar 2 were sensitive to salinity stress.

The purpose of present study is determining semantic network for selected Persian words and designing false memory scale (examination) based on it. This present research is cross sectional studies. In this research, two studies were designed. In the first study, selected word presented to 30 individuals and they should write down related word with free association. Key words (Critical Lure) selected from Roediger Mc Dermot (1995) in false memory scale. In the second study, list of words presented Without key words to 80 participants and recall and recognition of words were measured. Descriptive statistics and Pearson correlation test was used for analysis. Findings show that recall and recognition were significantly correlated (P

Keyword density is one of the main factors to the successful information retrieval in websites. Keyword density is a measure of the amount of keywords in the content to the total words in the same page. The main aim of this study is to investigate the role of keywords in top Universities of Medical Science's library websites with a successful marketing approach. Five top five Universities of Medical Science's libraries websites are the research cases. We selected them on the basis of their WIF ranking. Keyword density analyzer software was used for data gathering. Findings showed that none of the Universities of Medical Science's libraries have appropriate keywords in their website. Isfahan Universities of Medical Sciences Central Library website has used keywords in the main metatag, accordingly this website has obtained the high impact. Other results indicated that Mashhad University of Medical Sciences Central Library website has gained a high rank in Google search engine. Results also showed that the use of keyword density in websites has an important role in improving the rank and the visibility of websites; keyword density also caused successful marketing in Medical University Libraries websites.

Persian words in writing with a diverse and cover all modes of grammatical words with the recruitment of a series of specific rules because it is impossible to extract keywords automatically from Persian texts difficult and complex. This thesis has attempted to use linguistic information and thesaurus, keywords Mnatry be provided. Using the symbol system is structured network can be keywords, including the exchange of words, words and words with hierarchical relationships complete the package has increased. Therefore the agreement between users and search keywords text search and recall is increased. In the first stage non-important words are removed and the public. Supervision in the text are words and more words to clarify the relative importance of using a blower numerical weight is assigned to each word that indicates the effectiveness of the word in connection with the subject and compared with the other words used in the text. Particularly complex operation that makes use of thesaurus keywords are extracted Mnytry that kind of hierarchical category scientific literature in the field of information retrieval is indicated. Test results on different topics several text accurately represents the proposed method and the ability to extract the keywords in accordance with user demand.

Keywords density is one of the main factors for successful retrieval of information from websites. Keywords density is a measure showing the proportion of the number of keywords in the content to the total words in the same page. The main aim of this study was to investigate the role of keywords in top virtual websites of the universities of medical sciences in Iran with a successful marketing approach. The methodology adoted for this study was content analysis. Five top virtual websites of the five universities of medical sciences were selected on the basis of their in their websites. Virtual branch of Mashhad University of Medical Sciences، of course، used WIF ranking. Keywords density analyzer software was used for data collection. The findings showed that none of the virtual websites had appropriate keywords keywords in the main metatag، so this website was of superior efficiency. It was also found that this website has gained a high rank in Google search engine. The findings imply that web designers and managers in assessed universities must incorporate proper and relevant links in order to promote the quality and content of their websites. In fact، the ultimate success of a website is dependent upon factors such as quality، size، language، and being up-to-date، etc. One or two factors cannot determine the success of a website.

Due to ever-increasing information expansion and existing huge amount of unstructured documents, usage of keywords plays a very important role in information retrieval. Because of a manually-extraction of keywords faces various challenges, their automated extraction seems inevitable. In this research, it has been tried to use a thesaurus, (a structured word-net) to automatically extract them. Authors claim that extraction of more meaningful keywords out of documents can be attained via employment of a thesaurus. The keywords extracted by applying thesaurus, can improve the document classification. The steps to be taken to increase the comprehensiveness of search should be such that in the first step the stop words are removed and the remaining words are stemmed. Then, with the help of a thesaurus are found words equivalent, hierarchical and dependent. Then, to determine the relative importance of words, a numerical weight is assigned to each word, which represents effect of the word on the subject matter and in comparison with other words used in the text. According to the steps above and with the help of a thesaurus, an accurate text classification is performed. In this method, the KNN algorithm is used for the classification. Due to the simplicity and effectiveness of this algorithm (KNN), there is a great deal of use in the classification of texts. The cornerstone of KNN is to compare with the text trained and text tested to determine their similarity between. The empirical results show the quality and accuracy of extracted keywords are satisfiable for users. They also confirm that the document classification has been enhanced. In this research, it has been tried to extract more meaningful keywords out of texts using thesaurus (which is a structured word-net) rather than not using it.

Keywords and key phrases are subsets of most relevant words or phrases that summarize contents of a document while they play a critical role in information and document retrieval. Keyword extraction from scientific text is challenging and time-consuming due to the technical and multi-subject nature of the text, while the number of documents requiring keywords is increasing. There are various algorithms and methods developed for automatic keyword extraction. Rapid Automatic Keyword Extraction (RAKE) is a popular algorithm in this domain. RAKE’s decisions are based on the observation that keywords generally contain multiple words and they rarely include stopwords and words with minimum lexical meanings. Candidate keywords are a set of single-word or multi-word sequences selected based on the scores assigned to them by some scoring criteria in RAKE.In this research, a new modified version of RAKE algorithm is proposed in which candidate keyword scoring scheme is improved to increase precision and recall in the keyword extraction process. The proposed algorithm is to cover some of the main weaknesses of RAKE algorithm, especially in Persian scientific documents. To study the weaknesses of RAKE algorithm and evaluating the proposed modified version of RAKE, a set of metadata of Persian theses and dissertations are used. The result of test and evaluation of the proposed algorithm confirm improvement in precision, recall and F-measure.We study effectiveness of RAKE in extracting keywords from Persian texts. We find that RAKE algorithm often extracts long phrases with redundant words on Persian texts, leading to low accuracy. In this paper, we study sources of scoring inefficiency of RAKE algorithm and propose an improved version of RAKE algorithm with a novel scoring mechanism. Our scoring mechanism overcomes some of the weaknesses in RAKE’s original scoring for Persian texts and yields better results. Our evaluations on Persian corpus demonstrate that our improved RAKE algorithm outperforms original RAKE algorithm by extracting more accurate keyword. Our results show that improved RAKE achieves more than 20% higher precision and recall on average compared to original RAKE.

With the growing number of Persian electronic documents and texts, the use of quick and inexpensive   methods to access desired texts from the extensive collection of these documents becomes more important. One of the effective techniques to achieve this goal is the extraction of the keywords which represent the main concept of the text. For this purpose, the frequency of a word in the text can not be a proper indication of its significance and its crucial role. Also, most of the keyword extraction methods ignore the concept and semantic of the text. On the other hand, the unstructured nature of new texts in news and electronic  documents makes it difficult to extract these words. In this paper, an automated, unsupervised method for keywords extraction in the Persian language that does not have a proper structure is proposed. This method not only takes into account the probability of occurrence of a word and its frequency in the text, but it also understands the concept and semantic of the text by learning word2vec model on the text. In the proposed method, which is a combination of statistical and machine learning methods, after learning word2vec on the text, the words that have the smallest distance with other words are extracted. Then, a statistical equation is proposed to calculate the score of each extracted word using co-occurence and frequency. Finally, words which have the highest scores are selected as the keywords. The evaluations indicate that the efficiency of the method by the F-measure is 53.92% which is 11% superior to other methods.

Web is the most important medium for access and dissemination of information and therefore, academic libraries have developed websites for communication. Having a website with the highest ranking is a target for each university. As a result, they provide a list of titles, keywords and descriptions likely to promote their rank. Density keywords enhance the quality of information retrieval. This research aims to investigate the quantity and quality of keyword density on Iranian university websites.
This research was done with a content analysis approach. Research population consisted of fifteen websites related to the central libraries of MSRT universities in Tehran. Data analysis was done using a keyword density analyzing application software.
Findings: the survey indicated that none of the studied libraries had used proper keywords in their websites. The central library of the University of Tehran scored the highest visibility because it used more relevant keywords in its website tags. This library also ranked high in Google search results.
The research revealed that using keywords density and proper tag content on library websites can remarkably enhance library website rank and visibility which leads to better retrieval.
=matnechekideenglishboldChar>Keywords: keyword Density, Web Design, University Central Library, Meta Tag.

The domain of data quality is one of the growing and important areas in the field of information systems. The exact recognition of this field on the one hand and the recognition of the features of the new sub-fields of this field and its interdisciplinary of it will be of great importance to researchers. This knowledge is important for them to decide on the research process and the choice of the field of activity. For this purpose, in this study, using the graph of keyword co-occurrence is conducted on more than 9000 papers. Based on this study, it has been found that these domains have been more interdisciplinary and focus on the relationship between several fields of study. In other words, these keywords are more closely associated with the keywords in the other clusters than with the keywords they are in the same cluster. According to this study, the latest are is big data that focused on integration issues and missing data in this area.