Mohammad Javad shayegan

Nowadays, semantic interoperability is a new keyword in the Internet of Things (IoT) for the exchange of information between sources. The constant need for interaction and cooperation has resulted in the creation of the Semantic Web with the help of tools and reasoners which manage personal information. Given the significance of the IoT and the increasing use of semantic techniques in this field, the present bibliometric investigation was conducted in the domain of semantic reasoning in the IoT. Bibliometrics involves analyzing bibliographic data of scientific sources, and it can be employed to arrive at an analysis of the status quo in a scientific field. In this study, through the analysis of 799 articles retrieved from the Web of Science database, distribution of topic categories, prolific and influential authors, language of articles, publishers of articles and their geographical distribution, the most debated/researched and the most frequently cited articles, and keyword trends...

The financial industry is a pioneer in Blockchain technology. One of the most popular platforms in Token-based banking is the flexible Stellar platform. This platform is open-source, and today, its wide range of features makes it possible for many countries and companies to use it in cryptocurrency and Token-based modern banking. This network charges a fee for each transaction. As well, a percentage of the net amount is generated as the inflation rate of the network due to the increased number of tokens. These fees and inflationary amounts are aggregated into a general account and ultimately distributed among members of the network on a collective vote basis. In this mechanism, network users select an account as the destination for which they wish to transfer assets using their user interface, which is generally a wallet. This account could be the account of charities that need this help. It is then determined the target distribution network based on the voting results of all member...

The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The propo...

Accessing people's personality traits has always been a challenging task. On the other hand, acquiring personality traits based on behavioral data is one of the growing interest of human beings. Numerous researches showed that people spend a large amount of time on social networks and show behaviors that create some personality patterns in cyberspace. One of these social networks that have been widely welcomed in some countries, including Iran, is Telegram. The basis of this research is automatically identifying users' personalities based on their behavior on Telegram. For this purpose, messages from Telegram group users are extracted, and then the personality traits of each member according to the NEO Personality Inventory are identified. For personality analysis, the study is employed three approaches, including; Cosine Similarity, Bayes, and MLP algorithms. Finally, this study provides a recommender system that uses the Cosine similarity algorithm to explore and recommend...

Today's world is struggling with the COVID-19 pandemic, as one of the greatest challenges of the 21st century. During the lockdown caused by this disease, many financial losses have been inflicted on people and all industries. One of the fastest ways to save these industries from the COVID-19 or any possible pandemic in the future is to provide a reliable, fast, smart, and secure solution for people's health assessment. In this article, blockchain technology is used to propose a model which provides and validates the health certificates for people who travel or present in society.  For this purpose, we take advantage of blockchain features such as being unchangeable, errorless, distributed, and a single point of failure nonexistence, high security, and proper use in protecting people's privacy. Since a variety of antibody and human health proving tests against the virus are developing, this study tries simultaneously to design an integrated and secure system to meet the ...

Abstract—The vast amount of information on the World Wide Web is created and published by many different types of providers. Unlike books and journals, most of this information is not subject to editing or peer review by experts. This lack of quality control and the explosion of web sites make the task of finding quality information on the web especially critical. Meanwhile new facilities for producing web pages such as Blogs make this issue more significant because Blogs have simple content management tools enabling nonexperts to build easily updatable web diaries or online journals. On the other hand despite a decade of active research in information quality (IQ) there is no framework for measuring information quality on the Blogs yet. This paper presents a novel experimental framework for ranking quality of information on the Weblog. The results of data analysis revealed seven IQ dimensions for the Weblog. For each dimension, variables and related coefficients were calculated so ...

the vast amount of information on the World Wide Web is created and published by many different types of providers. Unlike books and journals, most of this information is not subject to editing or peer review by experts. This lack of quality control and the explosion of web sites make the task of finding quality information on the web especially critical. Meanwhile new facilities for producing web pages such as Blogs make this issue more significant because Blogs have simple content management tools enabling non-experts to build easily updatable web diaries or online journals. On the other hand despite a decade of active research in information quality (IQ) there is no a content management system (CMS) to facilitate measuring information quality on the Blogs yet. This paper presents a novel CMS for ranking quality of information on the Blog. The CMS includes appropriate criteria for ranking Blogs according to IQ parameters. The developed CMS collect and calculate IQ scores of Blogs ...

Much research has been done on the Semantic Web. Semantic enrichment is one branch of this field that has gotten much attention recently. This study aims to use bibliometrics to look at the current state of research in this field. Bibliometrics is the study of scientific sources' bibliographic data, which can be used to assess the current state of a field. Various bibliometric analyses were performed after extracting the metadata required for bibliometry from the Scopus database. According to the study's findings, more articles in this field have been published since 2018, and they have recently received special attention. Ontology/semantics/semantic web/semantic enrichment are also becoming more important keywords. Furthermore, based on the countries that published the articles, the United States, Germany, the United Kingdom, France, Italy, and Brazil published the most in this field. The article goes on to provide more analysis and illustrations, which will be helpful to r...

Today, ranking systems in universities have been considered by the academic community, and there is a tight competition between world universities to achieve higher ranks. In the meantime, the ranking of university websites is also in the spotlight, and the Webometric research center announces the ranks of university websites twice a year. Examining university rankings indicators and the Webometric ranks of the university indicates that some of these indicators, directly and indirectly, affect each other. On the other hand, a preliminary study of Webometric indicators shows that some Search Engine Optimization (SEO) indicators can affect Webometric ranks. The purpose of this research is to show how far the SEO metrics can affect the website rank of the university. To do this, after extracting 38 points of the significant SEO metrics of the extracted universities using various tools, data analysis was conducted along with applying association rules on the data. The results of the res...

The web is becoming the most important scholarly communication tool and it makes more and more scientific information accessible. In recent years, university Web rankings have become in importance around the world. The central hypothesis of the ranking is that the university’s web presence reflects its global performance, the quality of its departments and services, the impact of its outputs and its international prestige. One of the most important dimensions in university Website ranking is visibility factor and quality of presentation. The dimension includes qualitative and quantitative criteria. The current paper attempts to priorities presentation criteria for university Website then evaluate selected universities’ websites as a case study by some of the qualitative subcriteria. The results show there are strong correlation between quality of presentationrs and university Website ranking. Keywords--Web Quality, University Web Ranking, Quality of Presentation, AHP

In recent years, people spend a lot of time on social networks. They use social networks as a place to comment on personal or public events. Thus, a large amount of information is generated and shared daily in these networks. Using such a massive amount of information can help authorities to react to events accurately and timely. In this study, the social network investigated is Twitter. The main idea of this research is to differentiate among tweets based on some of their features. This study aimed at investigating the performance of event detection by weighting three attributes of tweets; including the followers count, the retweets count, and the user location. The results show that the average execution time and the precision of event detection in the presented method improved 27% and 31%, respectively, than the base method. Another result of this research is the ability to detect all events (including hot events and less important ones) in the presented method.

In recent years, data mining has played an essential role in computer system performance, helping to improve system functionality. One of the most critical and influential data mining algorithms is anomaly detection. Anomaly detection is a process in detecting system abnormality that helps with finding system problems and troubleshooting. Intrusion and fraud detection services used by credit card companies are some examples of anomaly detection in the real world. According to the increasing volumes of the datasets that creates big data, traditional data mining approaches do not have efficient enough results. Various platforms, frameworks, and algorithms for big data mining have been presented to account for this deficiency. For instance, Hadoop and Spark are some of the most used frameworks in this field. Support Vector Machine (SVM) is one of the most popular approaches in anomaly detection, which—according to its distributed and parallel extensions—is widely used in big data minin...

3 Abstract: The World Wide Web (WWW) has become one of the fastest growing electronic information sources. In the Web, people are engaging in interaction with more and more diverse information than ever before, so that the problem of information quality is more significant in the Web than any other information system, especially considering the rate of growth in the number of documents. Meanwhile new facilities for producing web pages such as Blogs make this issue more significant because Blogs have simple content management tools enabling non-experts to build easily updatable web diaries or online journals. On the other hand despite a decade of active research, information quality lacks comprehensive methodology for its assessment and improvement. Especially there is not any framework for measuring information quality on the Blogs yet. This paper establishes a survey on Iranian Blog as our case study presents results of the survey, prioritizes information quality criteria by alloca...

The amount of data generated today regarding volume, generation velocity, and variety is quite immense. This, in turn, has created a great challenge for scientists and researchers. To devise a solution, researchers have suggested a variety of schemes to help alleviate this problem. One of the suggested schemas is Association Rule Mining, and it is primarily focused on finding the associations in transactionlike data. To assist in finding such associations, Frequent Itemsets should be discovered first. Therefore, this research is a new approach to finding Frequent Itemsets and it is based on the Apriori algorithm and Apache Spark distributed platform. Further, we introduce an extended version of Apriori which tends to find Maximal Frequent Itemsets first to help speed up the mining process. The results and comparison to algorithms like YAFIM and HFIM and the original Apriori show the suggested algorithm outperforms them in dense datasets by an average of 38 percent.

Nowadays Microservice, as one of the most important architectural approaches towards Cloud Computing, has caught the attention of many developers. To give a simple definition of microservice, it could be said: Each microservice is completely independent, and implements a part of the business, which is composed of several microservices. Each microservice could be deployed, updated, and scaled, without any impact on other microservices, and it is all automated. Among Agile methods, Continuous Delivery has played an important role in development process of Microservice-based systems. Continuous Delivery provides faster delivery of changes and faster getting customer's feedbacks. Continuous Delivery consists of many sections, one of which is Software Test. One of the challenges of automated deploying of new releases to production environment, is software reliability, that in case it is breached, the beneficiaries would suffer considerable losses. Regression test is one of the tests ...

The financial industry is a pioneer in Blockchain technology. One of the most popular platforms in Token-based banking is the flexible Stellar platform. This platform is open-source, and today, its wide range of features makes it possible for many countries and companies to use it in cryptocurrency and Token-based modern banking. This network charges a fee for each transaction. As well, a percentage of the net amount is generated as the inflation rate of the network due to the increased number of tokens. These fees and inflationary amounts are aggregated into a general account and ultimately distributed among members of the network on a collective vote basis. In this mechanism, network users select an account as the destination for which they wish to transfer assets using their user interface, which is generally a wallet. This account could be the account of charities that need this help. It is then determined the target distribution network based on the voting results of all member...

Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, co...

Improvement of accuracy and optimal function of search engines has always been an area of concern for designers and researchers. Although these search engines work relatively well in simple searches, because the most existing search algorithms are based on search keywords, it can be expected for search engines to face trouble and confusion in some states of advanced searching. A possible solution is implementing Web Resources Categorization before performing the search. This study examines the basic web page clustering algorithms with the help of a k-means algorithm and optimizes its performance by solving its problems. The main issue is in the initial selection of clusters which can have a significant impact on the final clustering. Therefore, this research study proposes a new method for optimizing the core algorithm using cellular learning automata algorithm based on the Genetic Algorithm.

Today people use the Internet to find the answer to their questions. They mostly rather ask other users on Community Question Answering (CQA) sites for an answer than just searching the web. However, as Social Media becomes more popular, users tend to ask their questions on these networks, and ignore the benefits CQA sites offer. On the other hand, automatic Question Answering (QA) systems are unable to comprehend questions including images and implementing necessary algorithms for such systems is expensive. In this paper, we propose QA process based on Crowd sourcing, which runs on a QA open system. The system benefits from Crowd sourcing advantages, besides automation techniques. The model is operational and we have demonstrated that questions could be received from different heterogeneous sources, if the suitable procedures are used, and that the answer is obtained from the crowd in the proposed process based on Crowd sourcing. Moreover, the first Iranian crowd sourcing platform ...

The data being generated today is massive in terms of volume, velocity, and variety. It is a great challenge to derive knowledge from data in this condition. Researchers, therefore have proposed ways to deal with this challenge. frequent itemset mining is one of the proposed ways to distinguish itemsets inside the vast amount of data to aid the operation of a variety of pursuits and businesses, a process termed ‘Association Rule Mining'. However, there are a variety of works done in this area. The introduction of different algorithms, frameworks, and applications throughout the recent decade has produced many interesting approaches. One of the algorithms in this area is the Apriori algorithm. It is a simple yet powerful algorithm. However, the original Apriori is not suitable for big data and due to this reason, researchers have attempted to introduce ways and schemes to adapt it to this new age of data. Because of the number of efforts in this area, having a bird's eye view of the past works is of value. This review aims to present an insight into the works done in the intersection of two matters: big data and the Apriori algorithm. It is concerned with Aprioribased algorithms presented in the recent decade with a focus on the three popular big data platforms: Apache Hadoop, Spark, and Flink. Also, major points of each approach and solution is presented. A conclusion in the end summarizes the points discussed in this paper.

The main purpose of data mining is to discover hidden and valuable knowledge from data. The Apriori algorithm is inefficient due to bulky deals of searching in a dataset. Bearing this in mind, this paper proposes an improved algorithm from Apriori using an intelligent method. Proposing an intelligent method in this study is to fulfill two purposes: First, we demonstrated that to create itemsets, instead of adding one item at each step, several items could be added. With this operation, the number of k-itemset steps will decline. Secondly, we have proved that by storing the transaction number of each itemset, there would be a diminishment in the time required for the dataset searches to find the frequent k-itemset in each step. To evaluate the performance, the Intelligent Apriori (lAP) algorithm has been compared with the MDC algorithm. The results of this experiment exhibit that since the transaction scans used to obtain the itemset momentously reduced in number, there was a considerable fall in the runtime needed to obtain a frequent itemset by the proposed algorithm. In this study, the time required to generate frequent items had a 46% reduction compared to that of the MDC_ Apriori algorithm.

Abstract MinHash is a widely-used method for efficiently estimating the amount of similarity between documents for Near-Duplicate Detection (NDD). However, it is based on the concept of set resemblance rather than near-duplication. In this study, Sectional MinHash (S-MinHash), specifically designed for the detection of near-duplicate documents, is proposed. The proposed method enhances the MinHash data structure with information about the location of the attributes in the document. The method provides an unbiased estimate of the Jaccard coefficient with a smaller variance as compared to the MinHash for same signature sizes. The experiment results showed that the Mean Squared Error (MSE) of the proposed method was around one eighth of the MSE of the MinHash. Also, document NDD with the proposed method resulted in more accuracy in compare to the MinHash and the recent method, the BitHash. The best-captured F-measure was 87.05%. Setting the number of sections s to 2 gave the best results for the tested dataset.

AbstractEfficient and accurate near-duplicate detection is a trending topic of research. Complications arise from the great time and space complexities of existing algorithms. This study proposes a novel pruning strategy to improve pairwise comparison-based near-duplicate detection methods. After parsing the documents into punctuation-delimited blocks called chunks, it decides between the categories of “near duplicate,” “non-duplicate” or “suspicious” by applying certain filtering rules. This early decision makes it possible to disregard many of the non-necessary computations—on average 92.95% of them. Then, for the suspicious pairs, common chunks and short chunks are removed and the remaining subsets are reserved for near-duplicate detection. Size of the remaining subsets is on average 4.42% of the original corpus size. Evaluation results show that near-duplicate detection with the proposed strategy in its best configuration (CHT = 8, τ = 0.1) has F-measure = 87.22% (precision = 86.91% and recall = 87.54%). Its F-measure is comparable with the SpotSig method with less execution time. In addition, applying the proposed strategy in a near-duplicate detection process eliminates the need for preprocessing. It is also tunable to achieve the intended levels of near duplication and noise suppression.

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This article is a review on news retrieval and mining research areas in recent years based on a qualitative approach. It addresses news retrieval and mining in four main categories of News Retrieval and Extraction, News Content Analysis, News Propagation Analysis, and News Visualization. Each indicated category entails various research areas that have been investigated through several studies. This study depicts the immense extent of news retrieval and mining, the interconnected methods, tools, and theoretical foundations as well as the evaluation methods and the results. The study helps to gain a better understanding of news mining research areas.

Ontology as the base of semantic web is used in many applications. Different ontologies in the same domain lead some heterogeneities and ontology matching systems are developed for resolved them. Heterogeneities have arisen owing to the fact that these ontologies have been created by various people through diverse methods. Nowadays, using large-scale ontologies in some applications such as medical fields seems inevitable. By using large-scale ontologies, some problems like the shortage of memory consumption and long duration of execution appeared in ontology matching systems. In this paper, large-scale ontology matching systems are studied and proposed a general architecture for them. Then large-scale ontology matching systems classified based on the partitioning large ontologies into several sub-ontologies, as known as the modularization, decomposition, summarization, clustering, and divide and conquer categories. This new classification will be useful for future research works in this field. In order to find out the efficiency of the ontology matching systems the results of OAEI (Ontology Alignment Evaluation Initiative) for the period 2011 to 2015 are compared. In spite of great progress, increasing accuracy is required in some section such as conference and benchmark sections.

Ontology matching plays a crucial role to resolve semantic heterogeneities within knowledge-based systems. However, ontologies contain a massive number of concepts, resulting in performance impediments during the ontology matching process. With the increasing number of ontology concepts, there is a growing need to focus more on large-scale matching problems. To this end, in this paper, we come up with a new partitioning-based matching approach, where a new clustering method for partitioning concepts of ontologies is introduced. The proposed method, called SeeCOnt, is a seeding-based clustering technique aiming to reduce the complexity of comparison by only using clusters’ seed. In particular, SeeCOnt first identifies and determines the seeds of clusters based on the highest ranked concepts using a distribution condition, then the remaining concepts are placed into the proper cluster by defining and utilizing a membership function. The SeeCOnt method can improve the memory consuming problem in the large-scale matching problem, as well as it increases the matching quality. The experimental evaluation shows that SeeCOnt, compared with the top ten participant systems in OAEI, demonstrates acceptable results.

The popularity and amazing attractiveness of cryptocurrencies, and especially Bitcoin, absorb countless enthusiasts daily. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. For this purpose, in this study, the diagnosis of the collective anomaly was used, and instead of diagnosing the anomaly of individual addresses and wallets, the anomaly of users was examined, and the anomaly was more visible among users who had multiple wallets. In addition to using the collective anomaly detection method in this study, the Trimmed_Kmeans algorithm was used for clustering and the proposed method succeeded in identifying 14 users who had committed theft, fraud, ...

Driving accidents have been always counted as one of the most ostensible causes of deaths in the societies today. Statistics and reports indicate that the road accidents in Iran rank several times more than the ones in the developed countries. In the current paper, the rules and factors influencing the traffic road accidents of Iran have been extracted along with extracting a local data model after collecting the data from a variety of sources followed by data aggregation and combination, data cleaning, and separating the inappropriate data. This was done by employing appropriate data mining methods, such as clustering and decision tree. The utilized data was based on 10000 accidents during 2011 to 2013 in Isfahan Province, Iran. The experimental results have revealed that of the Decision Tree approaches, C5.0 algorithm outperforms the other algorithms with a lower error rate and a higher accuracy rate. Our research analysis also shows that in determining the accident type, three mo...

Since Word Wide Web contains large set ofdata in different languages, retrieving language specific information creates a new challenge in information retrieval called language specific crawling. In this paper, a new approach is purposed for language specific crawling in ...

... Year: 2011 Volume: 3 - Issue: 1. Title: A Content Management System for Building Quality Blogs. Author: Mohammad Javad Kargar. Abstract: the vast amount of information on the World Wide Web is created and published by many different types of providers. ...

Abstract— The vast amount of information on the World Wide Web is created and published by many different types of providers. Unlike books and journals, most of this information is not subject to editing or peer review by experts. This lack of quality control and the explosion of web ...

... Blog Context Mohammad Javad Kargar Islamic Azad University, Maybod Branch Department of Computer Engineering Maybod,Iran showkaran@hotmail.com ... The presented framework was tested on Persian (Farsi) Weblogs along with a Pesrian interface. ...

The World Wide Web (WWW) has become one of the fastest growing electronic information sources. Recent years have seen a transformation in the type of content available on the web. Various IT reports show a rapid rise in the number of web pages in collaborative Web and social networking. The nature of the social media is user-granted content in front of one-sided traditional Web. As the availability of such content increases, the task of identifying high-quality content in sites based on user contributions social media sites becomes increasingly important. This paper attempts to state information quality (IQ) frameworks in traditional Web application and social networking. For this, last IQ framework in Blog social networks, developed by our research group, is clearly compared with different IQ frameworks in the Web. The results show different dimensions and criteria for evaluating IQ in social networks.

... In the deterministic part of Maurer's model a statement is valid if and only if it is either contained in A View or if it can be derived from A View by applications of the following two inference rules: :, YX ∀ XA Aut , , 1,, XA Trust , YX Cert , ├ yA Aut , (1) ... 2 V = { YA Aut , , 1,, YA Trust ,

The WWW has become one of the fastest growing electronic information sources. In the Web, people are engaging in interaction with more and more diverse information than ever before, so that the problem of information quality is more significant in the Web than any other information system, especially considering the rate of growth in the number of documents. Yet, despite a decade of active research, Information quality lacks comprehensive methodology for its assessment and improvement and a few researches have presented practical way for measuring IQ criteria. This paper attempts to address some of the issues involved in information quality assessment by classifying and discussing existing researches of information quality frameworks. While many of pervious works have concentrated on a dimension of information quality assessment, in our classification has been considered three dimensions: criteria, models and validation methods of the models and criteria. This classification prepares a bed for developing practical and at the same time valid information quality model.

The World Wide Web (WWW) has become one of the fastest growing electronic information sources. Meanwhile new facilities for producing web pages such as Blogs make this issue more significant because Blogs have simple content management tools enabling non-experts to build easily updatable web diaries or online journals. On the other hand despite a decade of active research, information quality lacks comprehensive methodology for its assessment and improvement. Especially there is not any framework for measuring information quality on the Blogs yet. This paper establishes a survey on Iranian Blog as our case study presents results of the survey, prioritizes information quality criteria by allocating priority coefficient to all the criteria as an important prerequisite for measuring information quality in the Blogs. Also results of the research by gap analysis shows there is a solid consensus between Bloggers and visitors about the priorities of information quality criteria in the Blogs.