Hassan Arfaeinia

Work place: Department of Computer Engineering, Liyan Institute of Education, Bushehr, Iran

E-mail: harfaeinia@gmail.com


Research Interests: Machine Learning


Hassan Arfaeinia obtained her B.E in Computer Science from Rafsanjan University, Kerman, Iran in 2009. HE received her M.S in Computer Engineering from the Amirkabir University of Technology, Tehran, Iran in 2011 and PhD in Computer Engineering from Islamic Azad University, North Tehran Branch, Iran in 2016. His main paper interests consist of Software Development, Machine Learning, Statistical Modeling and Computer Programming.

Author Articles
A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique

By Noshin Hagshenas Musa Mojarad Hassan Arfaeinia

DOI: https://doi.org/10.5815/ijisa.2022.03.02, Pub. Date: 8 Jun. 2022

Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.

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An Intelligent Ensemble Classification Method For Spam Diagnosis in Social Networks

By Ali Ahraminezhad Musa Mojarad Hassan Arfaeinia

DOI: https://doi.org/10.5815/ijisa.2022.01.02, Pub. Date: 8 Feb. 2022

In recent years, the destructive behavior of social networks spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have extracted the behavioral characteristics of spam and obtained good results based on machine learning algorithms to identify them. However, most of these studies use a single classification technique that often works differently for different spam data. In this paper, an intelligent ensemble classification method for social networks spam detection is introduced. The proposed heterogeneous ensemble learning framework is based on stack generalization and uses an evolutionary algorithm to improve the modeling process and reduce complexity. In particular, particle swarm optimization has been used as an evolutionary algorithm to optimize model parameters to reduce model complexity. These parameters include a subset of effective features and a subset of the most appropriate single classification techniques. The SPAM E-mail dataset used in this article contains the correct and effective features in spam prediction. Experimental results show that the proposed algorithm effectively improves the detection rate of spam and performs better than the methods used.

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ILSHR Rumor Spreading Model by Combining SIHR and ILSR Models in Complex Networks

By Adel Angali Musa Mojarad Hassan Arfaeinia

DOI: https://doi.org/10.5815/ijisa.2021.06.05, Pub. Date: 8 Dec. 2021

Rumor is an important form of social interaction. However, spreading harmful rumors can have a significant negative impact on social welfare. Therefore, it is important to examine rumor models. Rumors are often defined as unconfirmed details or descriptions of public things, events, or issues that are made and promoted through various tools. In this paper, the Ignorant-Lurker-Spreader-Hibernator-Removal (ILSHR) rumor spreading model has been developed by combining the ILSR and SIHR epidemic models. In addition to the characteristics of the lurker group of ILSR, this model also considers the characteristics of the hibernator group of the SIHR model. Due to the complexity of the complex network structure, the state transition function for each node is defined based on their degree to make the proposed model more efficient. Numerical simulations have been performed to compare the ILSHR rumor spreading model with other similar models on the Sina Weibo dataset. The results show more effective ILSHR performance with 95.83% accuracy than CSRT and SIR-IM models.

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Modelling Electricity Consumption Forecasting Using the Markov Process and Hybrid Features Selection

By Hadis Dalkani Musa Mojarad Hassan Arfaeinia

DOI: https://doi.org/10.5815/ijisa.2021.05.02, Pub. Date: 8 Oct. 2021

Given the problem of electrical energy storage, it is critical to predict the amount of load required in order to have a reliable and stable power distribution network. Predicting electricity consumption of subscribers and analyzing their consumption behavior under the influence of various factors and time variables is important. Given the large volume of subscriber consumption data and the effective factors, it is only possible to analyze the data using new information technology tools such as data mining. In this paper, feature selection, clustering and Markov process techniques are used to model and predict the power consumption data of subscribers. First, the selection of a subset of effective features is based on the combined PCA approach and the Firefly algorithm. Subscribers are then clustered based on the features selected by the K-means. Finally, subscriber behavior patterns are modeled to predict consumption using the Markov process on high-risk clusters. This study is simulated based on the data of electricity subscribers in Bushehr-Iran Power Distribution Company. The simulation results show the superiority of the proposed model over other similar algorithms such as LASSO-QRNN and HyFIS. The accuracy of power consumption prediction in the proposed method is about 1% compared to LASSO-QRNN and about 0.5% compared to HyFIS.

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