Social network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. These communities are made of entities that are very closely related. Communities are defined as groups of nodes or summits that have strong relationships among themselves rather than between themselves. The clustering of social networks is important for revealing the basic structures of social networks and discovering the hyperlink of systems on human beings and their interactions. Social networks can be represented by graphs where users are shown with the nodes of the graph and the relationships between the users are shown with the edges. Communities are detected through clustering algorithms. In this paper, we proposed a new clustering algorithm that takes into account the extent of relationships among people. Outcomes from particular data suggest that taking into account the profundity of people-to-people relationships increases the correctness of the aggregation methods.
Recently, supercomputers and high-performance computing (HPC) have caused significant progress in many fields, including industrial and research fields of artificial intelligence, data science, big data, Internet of Things, as well as large scale web applications. Regarding this characteristic, the supercomputer field becomes one of the strategic issues in developed and developing countries. Successful HPC development requires a robust infrastructure that effectively depends on a multidimensional policy and roadmap, covering all aspects of HPC services. In this regard, a comprehensive roadmap is necessary to design and achieve high-performance computing services. This paper proposes a comprehensive roadmap focusing on several strategic areas, including research activities, infrastructure and platforms, data processing, standard regulations, applied services, and business development. The roadmap follows an evolutionary strategy where strategic regions evolve in parallel. Also, in this paper, the Supercomputing Development Readiness Indicators (SDRI) inspired by the "Network Readiness Indicators" (NRI) are presented and the proposed roadmap uses them. The proposed roadmap is then compared with two other hypothetical roadmaps with the timing and priority of different strategic areas in terms of progress and completion time. Evaluation of the proposed roadmap based on the SDRI specification shows the effectiveness of the proposed method in creating and developing the services of a petaflops scale supercomputer.
In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user's age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user's age and recommend movies based on the user's age with 93% accuracy, which is highly satisfactory.
In every country, airports are among the most important air transport systems in that country. When an aircraft flies from one airport to another, it creates a graph that can be completed with information about each flight, such as the number of flights per path, the number of passengers, traffic load, and so on. In the present paper, the airports of Iran and the domestic flights are considered as a network and the structure of the network is analyzed, and then the measures of complex networks such as degree distribution, shortest path length, analysis of centralities, clustering coefficient and their correlation and the way these centralities behave are examined. This analysis shows the Iranian Airport Network (IAN) that has a degree distribution described by the power function. The average path length in this network is 1.9, and the average clustering coefficient is 0.69, which meets the characteristics of a small-world network and is also considered an example of a disassortative network. The purpose of this research is to investigate the network of airports in Iran, which is ultimately important for the expansion of airports, and also to identify the important points of airports.
Statistics, extraction, analysis are vital in sports science. Information technology and data science will significantly increase the quality of research and decisions of sports clubs and organizations. Currently, many coaches and sports institutions use analytics and statistics that are calculated manually. Sports science shows that winning a match depends on different factors. The purpose of the research is to improve team performance by analyzing social networks, communication networks (such as players' passes and transactions during the match), and analyzing repetitive areas. These results are done by analyzing the data collected from 4 matches of the Persepolis team, including three matches from the first half of the Iranian Premier League in 2018-1399 and a Persepolis match against Al-Sharjah. This research examines the issue from two interconnected aspects: 1- Examining the performance of players individually and as part of a social network. 2- explore the communication network between players and land areas. This analysis uses the innovative method of identifying and classifying motifs.
Nowadays, the tourism industry has become one of the most important sectors in the world economy. Due to the perishability of this industry, accurate forecasting of the demand is very important for tourism planning and resource allocation. Studies show that due to the diversity and complexity of the factors affecting tourism demand, the combination of different approaches may increase the forecasting accuracy. The aim of this paper is to forecast the tourism demand of Alisadr cave. For this purpose, a method based on artificial neural networks is presented, in which the results of linear and non-linear methods and short-term and long-term forecasts are combined. This method is applied to a dataset of Alisadr cave tourists. The evaluation results show that in most cases, the proposed combined method can predict the tourism demand with higher accuracy than the monthly and seasonal methods based on neural networks and random forest models. The predictive models obtained from this study can enhance customer service and improve the interaction between users and tourist ticketing web applications and online reservation programs.
In computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS 2018, UNSW-NB15 and MQTT datasets have been used. The evaluation criteria include precision, accuracy, F1 score, and, readability. The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. This algorithm showed the highest known accuracy of 99% on the NSL-KDD dataset. It has reached 97% in all criteria in UNSW-NB15, 96% in all criteria in CIC-IDS 2018, and also, in MQTT for three abstraction levels of features, i.e. packet-based flow features, unidirectional flow, and The two-way flow has reached above 97%, which shows the superiority of this algorithm.
The success of e-learning is still a challenging issue. This study presents a model for the evaluation of the success of e-learning in three different faculties. More specifically, the present article answers the question of whether e-learning success variables are different in different faculties. The method of this study was descriptive-survey research. Evaluating research validity was conducted through confirmatory factor analysis, and Cronbach's alpha was used to measure the reliability of the research instrument. The method of the structural equation was used for modeling. The findings reveal that the students’ opinions in the three faculties about the success variables were significantly different. In the Faculty of Engineering, teaching with a coefficient of 0.93, in the Humanities, service quality with a coefficient of 0.9, and in the Arts Faculty, support quality with a coefficient of 0.82 were identified as the highest impact factors. On the other hand, significant commonalities were observed.
Recent developments in electronics and wireless communication play a leading role in manufacturing sensors with reduced power consumption that have wireless connectivity and limited processing capabilities. Due to the limitation of battery in sensor nodes, one of the main challenges in this type of network is energy consumption, which is directly related to the lifetime of the network. Another important issue is to keep nodes connected in the network during data transmission. For these purposes, a connectivity control system is required. By improving the tree growth algorithm in the network graph, an optimal graph using a suitable path for data transmission in the network is designed. Connectivity control significantly improved system performance in terms of network power consumption and lifetime. In this paper, a new algorithm for connectivity and linkage control, based on sequential mode is presented, which has achieved a significant improvement compared to an ordinary algorithm. The outcomes of the proposed algorithm on the selected model show 56% improvement in the remaining battery charge. In addition, the end-to-end delay was reduced by 0.5 m seconds in the network.
Word Sense Disambiguation (WSD) is a long standing task in Natural Language Processing (NLP) that aims to automatically identify the most relevant meaning of the words in a given context. Developing standard WSD test collections can be mentioned as an important prerequisite for developing and evaluating different WSD systems in the language of interest. Although many WSD test collections have been developed for a variety of languages, no standard All-words WSD benchmark is available for Persian. In this paper, we address this shortage for the Persian language by introducing SBU-WSD-Corpus, as the first standard test set for the Persian All-words WSD task. SBU-WSD-Corpus is manually annotated with senses from the Persian WordNet (FarsNet) sense inventory. To this end, three annotators used SAMP (a tool for sense annotation based on FarsNet lexical graph) to perform the annotation task. SBU-WSD-Corpus consists of 19 Persian documents in different domains such as Sports, Science, Arts, etc. It includes 5892 content words of Persian running text and 3371 manually sense annotated words (2073 nouns, 566 verbs, 610 adjectives, and 122 adverbs). Providing baselines for future studies on the Persian All-words WSD task, we evaluate several WSD models on SBU-WSD-Corpus.
This study evaluated the obstacles and challenges of using blockchain technology in Iran's automotive industry. The statistical population of the present study is all specialists and experts who were familiar with blockchain technology. However, because of the impossibility of identifying and studying all the people in the society and the lack of access to all of them, sampling was done employing a purposeful judgment method. Accordingly, ten academic and industrial experts were selected as the expert. In this study, a document study was applied to study the theoretical backgrounds and the related literature. Thus, the latest materials and concepts related to the subject were reviewed by referring to scientific sources, including books, articles, and theses. A survey method was also used in evaluating obstacles and challenges. At this stage, a researcher-made questionnaire was employed to collect experts' views. The process of conducting this research is as follows: first, the theoretical backgrounds and the related literature were examined. Then the questionnaire of the study was designed, distributed among the experts, and collected. After that, the data was analyzed using the Fuzzy Dematel Technique. Based on the findings, among the seven factors of the leading technical and technological component, three sub-components, including the lack of necessary safety against cyber attacks, the storing information problem, and the lack of standardization in different blockchain systems were ranked first to third, respectively, which shows the significance of the technical and technological factors.
Lung infection is the most dangerous sign of Covid 19. X-ray images are the most effective means of diagnosing this virus. In order to detect this disease, deep learning algorithms and machine vision are widely used by computer scientists. Convolutional neural networks (CNN), DenseNet121, Resnet50, and VGG16 were used in this study for the detection of Covid-19 in X-ray images. In the current study, 1341 chest radiographs from the COVID-19 dataset were used to detect COVID-19 including infected and Healthy classes using a modified pre-trained CNN (train and test accuracy of 99.75% and 99.63%, respectively). The DENSENET121 model has a training accuracy of 43.89% and a test accuracy of 57.89%, respectively. The train and test accuracy of ResNet-50 are, respectively, 89.43% and 90%. Additionally, the CNN model has test and train accuracy of 98.13% and 96.73%, respectively. The suggested model has COVID-19 detection accuracy that is at least 1% higher than all other models.