@article { author = {Salehi, Sajjad and Taghiyareh, Fattaneh}, title = {Opinion Formation Modeling By Agents With Internal Tendency}, journal = {International Journal of Web Research}, volume = {2}, number = {1}, pages = {1-11}, year = {2019}, publisher = {University of Science and Culture}, issn = {2645-4335}, eissn = {2645-4343}, doi = {10.22133/ijwr.2019.177130.1018}, abstract = {Several factors such as engagement with peer groups, government policies, personal attitudes can affect people’s opinion about a specific subject. Most of scholars in this area focus on the interaction of individuals in social network and overlook other factors. In this paper, an opinion formation model is presented in which the internal tendencies of individuals are considered as an intrinsic property. In this model, people revise their opinion based on their neighbors’ opinion, trust/distrust between them and their own internal tendency. By internal tendency we mean a set of internal factors which may affect the decision of individuals. Simulation results show that this model is able to predict individuals’ opinion which might present their preferences to different products in social network when parameters of the model are identified and assigned. As this model can predict people’s opinion in the market, it can be used in definition of a marketing or production strategy.}, keywords = {Opinion Formation,Agent Based Modeling (ABM),Social Networks,Social Market,Internal Tendency}, url = {https://ijwr.usc.ac.ir/article_100374.html}, eprint = {https://ijwr.usc.ac.ir/article_100374_5d029b3686e8ab5e347208cb49951691.pdf} } @article { author = {Nazari, Zahra and Kamandi, Ali and Shabankhah, Mahmood}, title = {A QoS Aware Multi-Cloud Service Composition Algorithm}, journal = {International Journal of Web Research}, volume = {2}, number = {1}, pages = {12-25}, year = {2019}, publisher = {University of Science and Culture}, issn = {2645-4335}, eissn = {2645-4343}, doi = {10.22133/ijwr.2019.177957.1020}, abstract = {Devices that are connected on the internet and are exchanging data with internet brokers to receive requested services are a significant part of internet users. In order to manage and account well to IoT requests maximum processing power, speed in data transfer, and proper combining services in minimum time is needed. Since there is a large number of IoT devices which have a large scale, we have to use the abilities and services of cloud environment in order to solve its problems. So, service composition in a cloud environment is paid attention recently. We want to suggest an algorithm with the approach in this research, of improving factors propounded in the service composition problem like the number of clouds involved in service, number of services examined before responding to users’ requests SP and load balance between clouds. In this paper, the factor, similarity measure, is introduced and used to find the best cloud and composition plan in each phase which in addition to improving QoS metrics propounded in previous papers, it caused improving QoS metric of load balancing between clouds, prevention of formation of a bottleneck in clouds entrance. These changes, besides the proper load balancing, have avoided the clouds stop working suddenly and satisfied the users by presenting the services faster.}, keywords = {Service composition,multi-cloud environment,Internet of Things,Load Balancing,Quality of service}, url = {https://ijwr.usc.ac.ir/article_100378.html}, eprint = {https://ijwr.usc.ac.ir/article_100378_8a9b67c982cc214e0e79f4e217244318.pdf} } @article { author = {Lesani, Milad and Naderan, Marjan and Alavi, Seyed Enayatallah}, title = {Fuzzy Ontology with ANFIS Neural Network for Semantic Sensor Networks in Smart Homes based on Internet of Things}, journal = {International Journal of Web Research}, volume = {2}, number = {1}, pages = {26-38}, year = {2019}, publisher = {University of Science and Culture}, issn = {2645-4335}, eissn = {2645-4343}, doi = {10.22133/ijwr.2019.192759.1034}, abstract = {In this paper, a fuzzy ontology for Semantic Sensor Networks (SSN) is proposed for smart homes in two phases. In the first phase, using the WordNet ontology, the location and type of an object is identified with the aid of a graphical interface. This object and its synonyms are added to the list of the known objects set. Succeeding, the relation of the object with other groups is assessed based on a similarity measure in addition to using the fuzzy ontology. In the second phase, sensors with erroneous information are identified and pruned by finding a relationship between some specific factors. To this end, temperature, moisture and light are considered and the Adaptive Neuro-Fuzzy Inference System (ANFIS) is incorporated. The proposed method is implemented using some parts of the Wikipedia database and the WordNet dictionary. The first phase of the proposed method is tested with several sample requests and the system shows favorable results on finding the original group (and other related groups) of the request. For training the neural network in the second phase, the Intel lab Dataset is used. Results of this phase show that the neural network can predict the temperature and moisture factors with low error, while the light factor has more error in prediction}, keywords = {Internet of Things,Semantic Sensor Network (SSN),Fuzzy Ontology,Neural Network,Smart Home}, url = {https://ijwr.usc.ac.ir/article_100379.html}, eprint = {https://ijwr.usc.ac.ir/article_100379_a8680e7796a60f1201b480da28c5b0ec.pdf} } @article { author = {Abbasimehr, Hossein and Tarokh, MohammadJafar}, title = {A Novel Analytical Framework Combining the Concepts of Credibility and Aspect based Opinion Mining}, journal = {International Journal of Web Research}, volume = {2}, number = {1}, pages = {39-50}, year = {2019}, publisher = {University of Science and Culture}, issn = {2645-4335}, eissn = {2645-4343}, doi = {10.22133/ijwr.2019.187666.1028}, abstract = {With the emergence of Web 2.0, user generated content in the form of online product reviews has proliferated. Although product reviews contain valuable information, they vary greatly in terms of quality and credibility. This study presents an opinion mining framework - Cred-OPMiner (Credibility-Specific-Opinion Miner) - by combining the concepts of credibility and aspect based opinion mining. Cred-OPMiner performs three main tasks. The first task is to group reviewers based on the credibility dimensions. The second critical task is aspect extraction in which aspects of a given product are identified using a novel hybrid and domain independent algorithm. The final task is the sentiment prediction task where the sentiment on each aspect is computed. The key novelty is utilizing source credibility concepts for online reviewer clustering. Source credibility dimensions including trustworthiness and expertise are quantified using reviewers’ data. In addition, a new aspect extraction technique is developed and incorporated in the Cred-OPMiner. Cred-OPMiner was tested using data crawled from epinions.com. It groups reviewers and then performs aspect based opinion mining by differentiating among opinions of various reviewer groups.}, keywords = {Online Review,Aspect based Opinion Mining,Trust Network,Reviewer Credibility}, url = {https://ijwr.usc.ac.ir/article_100380.html}, eprint = {https://ijwr.usc.ac.ir/article_100380_98cee4b506705d7cdeab2cd1e088d7b1.pdf} } @article { author = {Tafakori, Homa and Karbasi, Soheila and Yaghoubi, Mehdi}, title = {Identifying Abnormal Behavior of Users in Recommender Systems}, journal = {International Journal of Web Research}, volume = {2}, number = {1}, pages = {51-64}, year = {2019}, publisher = {University of Science and Culture}, issn = {2645-4335}, eissn = {2645-4343}, doi = {10.22133/ijwr.2019.197769.1038}, abstract = {  Abstract— Nowadays, we deal with a large volume of information that we may have wrong choices without appropriate guidance. To this end, recommender systems are proposed which are a type of information filtering system that acts as a filter and displays information that is useful and close to the user's interests. They reduce the volume of the retrieved information and help users to select relevant products from millions of choices available on the internet. However, since these systems use explicitly and implicitly collected information about the user's interests for different items to predict the user's favorite items, the adversaries due to their openness nature might attack them. Therefore, identifying them is essential to improve the quality of the recommendations. For this purpose, in this paper, a method based on two criteria of a maximum number of users with the equal length and the degree of novelty of their profiles is presented and finally, the DBSCAN clustering algorithm is used to distinguish genuine users from fake users. In order to improve the DBSCAN algorithm, we proposed a new method to determine the values of Eps and MinPts automatically. The results of the proposed method are compared with a new comparative study on shilling detection methods for trustworthy recommendations, which shows that the proposed method independent of the type of attack can identify fake users in most cases with accuracy close to 1.}, keywords = {Recommender systems,Shilling Attack,Abnormal Behavior,Novelty Degree Of User’s Profile}, url = {https://ijwr.usc.ac.ir/article_100383.html}, eprint = {https://ijwr.usc.ac.ir/article_100383_ef1a36f8fea297f06e6e9b533475407b.pdf} } @article { author = {kargar, Javad and Hajiloo, Fatemeh}, title = {An Analysis on Characteristics of Negative Association Rules}, journal = {International Journal of Web Research}, volume = {2}, number = {1}, pages = {65-74}, year = {2019}, publisher = {University of Science and Culture}, issn = {2645-4335}, eissn = {2645-4343}, doi = {10.22133/ijwr.2020.217670.1051}, abstract = {Association rules are one of the data and web mining techniques which aim to discover the frequent patterns among itemsets in a transactional database. Frequent patterns and correlation between itemsets in datasets and databases are extracted by these interesting rules. The association rules are positive or negative, and each has its own specific characteristics and definitions. The mentioned algorithms of the discovery of association rules are always facing challenges, including the extraction of only positive rules, while negative rules in databases are also important for a manager’s decision making. Also, the threshold level for support and confidence criteria is always manual with trial and error by the user and the proper place or the characteristics of datasets is not clear for these rules. This research analyses the behavior of the negative association rules based on trial and error. After analyzing the available algorithms, the most efficient algorithm is implemented and then the negative rules are extracted. This test repeats on several standard datasets to evaluate the behavior of the negative rules. The analyses of the achieved outputs reveal that some of the interesting patterns are detected by the negative rules, while the positive rules could not detect such helpful rules. This study emphasizes that extracting only positive rules for covering association rules is not enough.}, keywords = {Negative Association Rules,Data mining,Frequent Itemsets,Association Rule Mining}, url = {https://ijwr.usc.ac.ir/article_103844.html}, eprint = {https://ijwr.usc.ac.ir/article_103844_ffe77b6249de36de76edf2f3a37a66de.pdf} }