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Open Access Article

1 - Exploratory and confirmatory factor analysis of intra-university factors for Universitie’s promotion at National and International Rankings
Mohammad khanazizi aliakbar aminbidokhti abdolrahim nave ebrahim maghsod farasatkhah

Abstract The present study aimed at designing the internal factors model for promoting universities in national and international Rankings through exploratory mixed method. The statistical population in the qualitative section included policymakers and experts in higher More

Abstract The present study aimed at designing the internal factors model for promoting universities in national and international Rankings through exploratory mixed method. The statistical population in the qualitative section included policymakers and experts in higher education, university administrators and faculty members who were selected through targeted sampling and 17 interviewes were done. Also, in the quantitative section of the statistical society, 600 university administrators ,state universities of Tehran, among which 302 people by Stratified random sampling method were selected. The data in the qualitative section were analyzed through semi-structured interviews and in the quantitative section, a researcher-made questionnaire was extracted from the statistical sample and analyzed using spss and smart-pls software. Validity of the questionnaire was confirmed by experts and a divergent and convergent validity test. The reliability of the questionnaire was also calculated using Cronbach's alpha and Composite Reliability software. According to the findings, the intra-university factors of university promotion can respectively be classified into eight factors including: good governance, quality assurance system, human resources productivity, administrative health, regional development, university autonomy, IT infrastructure and educational facilities. Manuscript profile

Open Access Article

2 - Content rating a nessissity for spam management in social networks
Simin Ghesmati

Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profi More

Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profiles.The purpose of this research is to design a system to manage, diagnose and deal with spam attacks through social networks to reduce the attack effects as greatest extent as possible. In this study, the content rating system is to rate spam, phishing and over 18 contents in accordance with feedbacks from users so that the spam, phishing, or over 18 logos show and contents will not be displayed. Users are to click on the logo to reach the mentioned contents. To determine limit in comment senders is another ability of this social network system. The outcome of this study confirms that applying the content management system, as 95.22% percent of users claimed, was effective to avoid spam displaying and 99.61% percent of users were highly satisfied with the procedures in spam reduction in the implemented social network. Manuscript profile

Open Access Article

3 - Content rating for spam management in social networks a nessissity
Simin Ghesmati

Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profi More

Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profiles.The purpose of this research is to design a system to manage, diagnose and deal with spam attacks through social networks to reduce the attack effects as greatest extent as possible. In this study, the content rating system is to rate spam, phishing and over 18 contents in accordance with feedbacks from users so that the spam, phishing, or over 18 logos show and contents will not be displayed. Users are to click on the logo to reach the mentioned contents. To determine limit in comment senders is another ability of this social network system. The outcome of this study confirms that applying the content management system, as 95.22% percent of users claimed, was effective to avoid spam displaying and 99.61% percent of users were highly satisfied with the procedures in spam reduction in the implemented social network. Manuscript profile

Open Access Article

4 - A new algorithm based on ensemble learning for learning to rank in information retrieval
Azadeh Shakery elham ghanbari

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by More

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking model. The ranking system then makes use of the learned ranking model for ranking prediction. In this paper, a new learning algorithm based on ensemble learning for learning ranking models in information retrieval is proposed. This algorithm iteratively constructs weak learners using a fraction of the training data whose weight distribution is determined based on previous weak learners. The proposed algorithm combines the weak rankers to achieve the final ranking model. This algorithm constructs a ranking model on a fraction of the training data to increase the accuracy and reduce the learning time. Experimental results based on Letor.3 benchmark dataset shows that the proposed algorithm significantly outperforms other ensemble learning algorithms. Manuscript profile

Open Access Article

5 - Identifying and ranking the effective factors on hospital teams' productivity using ANP-BWM approach (A case study: The Sadjad hospital in Tehran)
Abdolreza Karimi Marzieh Mozafari Rambod Barandoost

Health care services are of the most important operations which are usually done inside teams. This paper aims at identifying and ranking the hospital teams' productivity indices. For this purpose, the effective factors that influence hospital teams' productivity have b More

Health care services are of the most important operations which are usually done inside teams. This paper aims at identifying and ranking the hospital teams' productivity indices. For this purpose, the effective factors that influence hospital teams' productivity have been identified through literature study as well as experts survey. These factors then have been reduced to 34 criteria using a screening approach and then categorized into 9 main groups. Internal relations among the factors have been examined using statistical tests. We proposed a new ANP-BWM approach combining the analytical network process (ANP) and best-worst method (BWM) to rank the factors. The proposed ranking approach can significantly reduce the number of pairwise comparisons in ANP method. Finally, in the case of Sadjad hospital, 12 nursing teams have been selected and investigated. Results show that “the quality of services” has the most and “the permanent employment” has the least impact on nursing teams’ productivity. Manuscript profile

Open Access Article

6 - Proposing a Framework for Ranking International Science and Technology Institutions: The case of International S&T Policymaking Institutions
Effat Norouzi Javad Mashayekh

One of the relevant topics in the field of science, technology and innovation policy is international S&T cooperation. This study proposes a framework for ranking these institutions focusing on a clear example of international S&T cooperation; cooperation in the context More

One of the relevant topics in the field of science, technology and innovation policy is international S&T cooperation. This study proposes a framework for ranking these institutions focusing on a clear example of international S&T cooperation; cooperation in the context of multilateral international institutions, in order to decide on the membership and better utilization of their capacities. Seeing deficit in the history of the assessment of countries’ performance in these institutions, the research has suggested a function-oriented framework based on nine functions to rank these institutions. These functions are as follows: form and support of interaction, communication and networking at the level of individuals, organizations and governments; legitimacy and proper visualization in regional or international level; making regulation and international standards in the field of science and technology; production, processing and distribution of information; monitoring and enforcing international regulations; allocation and sharing of resources; cooperation and joint action; arbitration and resolving international challenges; training, empowerment and capacity building. Then, using the proposed framework, forty-six international active institutions in the field of S&T policy making were ranked in six categories. The result shows that the Islamic Republic of Iran almost poses the membership of those institutions placed second to fourth categories. Finally, some recommendations about the revision of the Islamic Republic of Iran's membership in these institutions as well as the efficient use of their capacity have been proposed. Manuscript profile

Open Access Article

7 - Semantic Word Embedding Using BERT on the Persian Web
shekoofe bostan Ali-Mohammad Zare-Bidoki mohamad reza pajohan

Using the context and order of words in sentence can lead to its better understanding and comprehension. Pre-trained language models have recently achieved great success in natural language processing. Among these models, The BERT algorithm has been increasingly popular More

Using the context and order of words in sentence can lead to its better understanding and comprehension. Pre-trained language models have recently achieved great success in natural language processing. Among these models, The BERT algorithm has been increasingly popular. This problem has not been investigated in Persian language and considered as a challenge in Persian web domain. In this article, the embedding of Persian words forming a sentence was investigated using the BERT algorithm. In the proposed approach, a model was trained based on the Persian web dataset, and the final model was produced with two stages of fine-tuning the model with different architectures. Finally, the features of the model were extracted and evaluated in document ranking. The results obtained from this model are improved compared to results obtained from other investigated models in terms of accuracy compared to the multilingual BERT model by at least one percent. Also, applying the fine-tuning process with our proposed structure on other existing models has resulted in the improvement of the model and embedding accuracy after each fine-tuning process. This process will improve result in around 5% accuracy of the Persian web ranking. Manuscript profile

Open Access Article

8 - Ranking Improvement Using BERT
shekoofe bostan Ali-Mohammad Zare-Bidoki Mohammad-Reza Pajoohan

In today's information age, efficient document ranking plays a crucial role in information retrieval systems. This article proposes a new approach to document ranking using embedding models, with a focus on the BERT language model to improve ranking results. The propose More

In today's information age, efficient document ranking plays a crucial role in information retrieval systems. This article proposes a new approach to document ranking using embedding models, with a focus on the BERT language model to improve ranking results. The proposed approach uses vocabulary embedding methods to represent the semantic representations of user queries and document content. By converting textual data into semantic vectors, the relationships and similarities between queries and documents are evaluated under the proposed ranking relationships with lower cost. The proposed ranking relationships consider various factors to improve accuracy, including vocabulary embedding vectors, keyword location, and the impact of valuable words on ranking based on semantic vectors. Comparative experiments and analyses were conducted to evaluate the effectiveness of the proposed relationships. The empirical results demonstrate the effectiveness of the proposed approach in achieving higher accuracy compared to common ranking methods. These results indicate that the use of embedding models and their combination in proposed ranking relationships significantly improves ranking accuracy up to 0.87 in the best case. This study helps improve document ranking and demonstrates the potential of the BERT embedding model in improving ranking performance. Manuscript profile

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