The amount of accessible information on Internet is increasing, rapidly, such that general- purpose SEARCH ENGINEs are unable to cover and index half of this information. A small fraction of this information represents a topic or domain. Domain specific SEARCH ENGINEs are suitable approach to reach a high precision and recalL However, these approaches suffer from querying mechanism. In this paper, the current problems of SEARCH ENGINEs in querying mechanism is surveyed and an integrated architecture for domain specific SEARCH ENGINEs, called AK USEARCH ENGINE, has been introduced, which its goal is to improve querying mechanism of users through automatic expanding of users' queries and learning from past SEARCHes using Case Based Reasoning (CBR). To expand the users' query automatically and with related information, a new concept called "domain specific concept hierarchy" is introduced and an algorithm for learning this hierarchy from specific domains is designed, implemented and evaluated. The result of using this concept hierarchy in the proposed architecture shows a significant improvement in comparison to the original architecture. The implementation results show that this architecture is effective in preventing from repetitive SEARCHes and presenting results with better quality to the user.

Analysis of published news content is one of the most important issues in information retrieval. Much reSEARCH has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detection is the task of discovering and grouping documents that describe the same event. It also facilitates better navigation of users in news spaces by presenting an understandable structure of news events. With rapid and increasing growth of online news, the need for SEARCH ENGINEs to retrieve news events is felt more than ever. The main assumption of event detection is that the words associated with an event appear in the same time windows and similar documents. Accordingly, in this reSEARCH, we propose a retrospective and feature-pivot method that clusters words into groups according to semantic and temporal features. We then use these words to produce a time frame and a human readable text description. The proposed method is evaluated on the All The News dataset, which consists of two hundred thousand articles from 15 news sources in 2016 and compared to other methods. The evaluation shows that the proposed method outperforms previous methods in terms of precision and recall.

The main objective of this study was to investigate whether SEARCHing firm's ticker symbol and name in Google can predict its future stock market activities. In so doing, the data related to firms' ticker symbol and firms' name were collected using Google Trend and stock market activity was measured using four proxies, namely abnormal return, return volatility, stock trading volume and stock trading count. In order to meet the main objective of the study, multiple regression and panel regression were used over13082 firmmonth observation during the years between 2005 and 2018. The results showed that the future market activity, including return volatility, stock trading volume and stock trading count, increased with SEARCHing the firms' ticker and name in Google. However, there was no significant relationship between the future abnormal return and Google SEARCHes. Findings also showed that future market activity can be predicted using Google SEARCHes. In addition, there was a more significant relationship between the SEARCHed firms' ticker symbol than the SEARCHed firms' name and future market activity.

SEARCH ENGINEs can be introduced as a best tool for managing, retrieving and extracting important information from a massive set of web data. These ENGINEs are scheduled to SEARCH the vast web environment and collect countless pages stored in every corner of the web. SEARCH ENGINEs providers are always looking for improving the relationship between the results and reducing response times to users, but both of these can be influenced by the automated traffic sent by the bots. This article first defines bots and challenges of detecting them. Then, it provides a method named ‘boof’ for detecting SEARCH robots. In ‘the boof method’, to achieve high accuracy in detecting anomaly robots, many different parameters are used to model the users’ behavior. After determining the priority of parameters in detecting users, decision tree is made and attempted to categorize users into groups of humans, bots, legal bots and the unknown. Robots detected in the decision tree, enable another part of the robot detection system to identify robots even with low request rate. This is done by detecting the botnet behavior pattern. Evaluation of the proposed method on test data shows 97.7 percent accuracy in recognizing users that this improves the accuracy of at least 9, 9 percent compared to the methods examined previously in this area. This is a significant digit that influences decision-making about 2230 users during each day.

Purpose: To analyze the reasons for which users select retrieved images from Google SEARCH ENGINE and investigate the relationship between users’ demographic variables and criteria for relevance judgment.Methodology: Thirty (30) graduate students in school of ENGINEering of Ferdowsi University of Mashhad were surveyed. Data gathered using a questionnaire asking about the reasons for selecting images and users’ relevance judgment. Objective criteria were obtained from related literature and validated by a number of experts.Results: There was no significant correlation between individual characteristics (experience and skill, educational level, age and gender) and users’ views toward the objective relevance criteria. The “user’s overall impression that an image may be useful” with 100% agreement was the main reason for image selection and the “uncertainty and going for the next image” with 33.33% was the main reason for rejecting an image.Conclusion: Users select images based on their overall impression, mental experience and their knowledge. The relevance judgment by users is not related to their familiarity with web elements.

Objective: With the development of the web, image SEARCH is considered to be one of the most important approaches and one of the major challenges for users. On the other hand, relevance as a cognitive concept has always been of interest and dates back to the time when one tried to retrieve and make good use of information. The inadequacy of the algorithmic approach proved that it is only the user himself who can judge the relevance of the document to its need and use. As a result, mental attitude has replaced military relevance in relevance studies. The purpose of this study is to investigate the viewpoints of graduate students of Ferdowsi University of Mashhad on subjective and objective criteria of relevance judgment in image retrieval from Google SEARCH ENGINE in order to provide solutions to improve the retrieval approach in image retrieval systems. Methodology: This is an applied reSEARCH using survey method. The statistical population of this study is graduate students of Ferdowsi University. Thirty male and female of BA, MA and doctoral students from various faculties of Ferdowsi University of Mashhad formed the sample of this study. The reSEARCH data were collected through a questionnaire consisting of four sections. The first and the second part of the questionnaire before the beginning of the SEARCH session and the third and fourth part of the questionnaire after the end of the SEARCH session were given to the subjects. In this regard, subjective and objective criteria of judgment of the users in relation to the images were studied in order to determine with what criteria the users judge the relevance of the images and make their choice. Findings: The findings showed that, in both subjective and objective judgment stages, except for "subject" criteria, "updating", "image attraction", "accessibility" and "information quality" criteria were important. The most relevant criteria for judging students regarding the relevance of images retrieved in Google's SEARCH ENGINE were identified. In addition to the stated criteria, it was observed that the users focused on the "information effect" with emphasis on criteria such as "interesting" and "enjoyable" and the average score above these criteria indicates their importance. The findings also indicated that users place less importance on the creator(s) of the image as a specific criterion in the web environment, and these criteria are not among their top priorities in information selection. In general, it can be said that the judgment of the user on the basis of their needs, feelings and situation is very important. It is recommended that further reSEARCH be carried out in order to obtain new findings in order to assist in the design of more efficient and more appropriate image SEARCH ENGINEs that are consistent with user behavior and characteristics. Conclusion: What distinguishes the present study from other studies is the image SEARCH approach that has been studied with emphasis on Iranian users, Google SEARCH ENGINE, and in two stages of subjective judgment and objective judgment that differs from the other reSEARCHes.