Azerbaijan Journal of High Performance Computing

Journal Information
ISSN / EISSN: 26166127 / 26174383
Total articles ≅ 79

Latest articles in this journal

Lida Naderlou, Non-Profit Higher Education Institutions Roozbeh Zanjan Branch, Zahra Tayyebi Qasabeh, Payame Noor University of Guilan
Azerbaijan Journal of High Performance Computing, Volume 5, pp 72-86;

Science and technology are proliferating, and complex networks have become a necessity in our daily life, so separating people from complex networks built on the fundamental needs of human life is almost impossible. This research presented a multi-layer dynamic social networks model to discover influential groups based on a developing frog-leaping algorithm and C-means clustering. We collected the data in the first step. Then, we conducted data cleansing and normalization to identify influential individuals and groups using the optimal data by forming a decision matrix. Hence, we used the matrix to identify and cluster (based on phase clustering) and determined each group’s importance. The frog-leaping algorithm was used to improve the identification of influence parameters, which led to improvement in node’s importance, to discover influential individuals and groups in social networks, In the measurement and simulation of clustering section, the proposed method was contrasted against the K-means method, and its equilibrium value in cluster selection resulted from 5. The proposed method presented a more genuine improvement compared to the other methods. However, measuring precision indicators for the proposed method had a 3.3 improvement compared to similar methods and a 3.8 improvement compared to the M-ALCD primary method.
Mohammad Azimnezhad, Science and Research Islamic Azad University, Mohammad Manthouri, Mohammad Teshnehlab, Shahed University, K.N. Toosi university
Azerbaijan Journal of High Performance Computing, Volume 5, pp 143-164;

This paper proposes a vaccination approach based on robust control for the SEIR (susceptible plus exposed plus infectious plus recovered populations) model of epidemic diseases. First, a classic sliding mode controller is investigated based on the SEIR model. Next, fuzzy logic is utilized to better approximate the uncertainties in the SEIR system using the sliding mode controller. Therefore, the proposed controller is a fuzzy sliding mode controller, which, compared to the sliding mode controller, provides an appropriate estimation of systems' actual parameters and removes the chattering phenomenon from the control signal. The stability of the controlled system is guaranteed using the Lyapunov theory simulations in which the classical sliding mode and the proposed controllers are compared, Using data from previous articles. Simulation results show that the proposed controller eliminates the susceptible subpopulation, incubated disease, and infectious diseases, eradicating the disease. Comparison with other methods reveals the better efficiency of the proposed method.
Armin Rabieifard, Non-Profit Higher Education Institutions Sardarjangal Branch, Lida Naderlou, Zahra Tayyebi Qasabeh, Payame Noor university of Guilan
Azerbaijan Journal of High Performance Computing, Volume 5, pp 94-111;

Today, energy consumption is important in calculating the heating and cooling loads of residential, industrial, and other units. In order to calculate, design, and select the heating-cooling system, a suitable method of consumption and cost analysis is needed to prepare the required data for air conditioning motors and design an intelligent system. In this research, a method for balancing the temperature of an intelligent building in the context of the Internet of Things is presented based on a combination of network cutting and clustering techniques. In order to achieve the optimization of the algorithm in this method, it is necessary to convert heterogeneous data into homogeneous data, which was done by introducing a complex network and appropriate clustering techniques. In this method, information was collected by the IoT, and a graph matrix of these data was generated, then recorded by an artificial intelligence method and a combination of three methods of hierarchical clustering, Gaussian mixture, and K-means for comparison with the preliminary results. Finally, due to the reliability of the K-means method and the use of majority voting for weights, the K-means method reached 0.4 and was selected as the clustering method. The main part of the proposed method is based on different classifications in Appropriate criteria that were evaluated. Acceptable results were recorded so that with the minimum value of 88% and the highest value of about 100, the results of the proposed method can be confirmed. All hypotheses of the method can be declared possible and acceptable.
Araz Aliev, Azerbaijan State Oil and Industry University, Yunis Gahramanli, Samir Aliyev, Institute of Mathematics and Mechanics of Ministry of Science and Education of the Republic of Azerbaijan
Azerbaijan Journal of High Performance Computing, Volume 5, pp 87-93;

This paper described the opportunity to use artificial neural networks to predict the chemical reaction result under given conditions. Applied three layers neural network for prediction of the mass content of alkaline trained using the results of the chemical reactions. As inputs were used values of the chemical quantities before the reaction and output values of the chemical quantities after the reaction. HPC technologies and multi-worker technology were used for accurate results.
Aliaa Kadhim Gabbar Alwaeli, Islamic Aazd University, Karrar Ezzulddin Kareem Al-Hamami
Azerbaijan Journal of High Performance Computing, Volume 5, pp 131-142;

Utilizing virtualization technology, a cloud computing service provides on-demand access to computer resources & services through the internet. There are new ways to control the functioning of cloud resources since they are constructed in diverse places. More than one algorithm Possibly included in these plans. One of the most important components of a high-performing cloud computing system is the scheduling mechanism. Fault tolerance & load balancing methods are also included in the scheduling system, not only task scheduling strategies. Fault handling Possibly accomplished via using scheduling systems. We analyze & contrast a few different scheduling algorithms to see what they have going for them in terms of benefits & drawbacks.
North Tehran Branch Azad University, Faezeh Gholamrezaie, Arash Hosseini, Nigar Ismayilova, Shahed University, Azerbaijan State Oil and Industry University
Azerbaijan Journal of High Performance Computing, Volume 5, pp 57-71;

Renewable energy is one of the most critical issues of continuously increasing electricity consumption which is becoming a desirable alternative to traditional methods of electricity generation such as coal or fossil fuels. This study aimed to develop, evaluate, and compare the performance of Linear multiple regression (MLR), support vector regression (SVR), Bagging and random forest (R.F.), and decision tree (CART) models in predicting wind speed in Southeastern Iran. The data used in this research is related to the statistics of 10 minutes of wind speed in 10-meter, 30-meter, and 40-meter wind turbines, the standard deviation of wind speed, air temperature, humidity, and amount of the Sun's radiation. The bagging and random forest model with an RMSE error of 0.0086 perform better than others in this dataset, while the MLR model with an RMSE error of 0.0407 has the worst.
Zahra Tayyebi Qasabeh, Payame Noor University of Guilan, Seyyed Sajjad Mousavi, Pol Talshan Azad University
Azerbaijan Journal of High Performance Computing, Volume 5, pp 33-51;

The blockchain is a revolutionary technology transforming how assets are managed digitally and securely on a distributed network. Blockchain decentralized technology can solve distrust problems of the traditional centralized network and enhance the privacy and security of data. It provides a distinct way of storing and sharing data through blocks chained together. The blockchain is highly appraised and endorsed for its decentralized infrastructure and peer-to-peer nature. However, much research about the blockchain is shielded by Bitcoin. But blockchain could be applied to a variety of fields far beyond Bitcoin. Blockchain has shown its potential for transforming the traditional industry with its essential characteristics: decentralization, persistency, anonymity, and audibility. Undoubtedly, blockchain technology can significantly change the global business environment and lead to a paradigm shift in the functioning of the business world. However, to unlock the tremendous potential, various challenges in the adoption and viability of blockchain technology must be addressed before we can see the legal, economic, and technical viability of this technology in the operation of various business applications. In this study, the fundamental concepts of blockchain are discussed at the beginning, and the way it works and its architecture is mentioned, and since all technologies face challenges, this technology is no exception and has challenges based on the works related to the challenges It is mentioned.
Elviz Ismayilov, Azerbaijan State Oil and Industry University
Azerbaijan Journal of High Performance Computing, Volume 5, pp 52-56;

Cloud technologies are currently one of the fastest growing directions in the IT field. This architecture uses virtualization technology in several computing paradigms (distributed systems, grid and service computing, etc.). It is possible to reach the goal using the unlimited possibilities of the Internet. It should be noted that most companies have transferred their resources and capabilities to cloud technology. According to Check Point Software Technologies Ltd 2020 statistics, 39% of enterprises said that security is important in cloud technology, and 52% said that public and hybrid cloud technologies have become more critical in the direction of security over the past two years. Enterprises are concerned about personal data storage and using special software enabled by cloud technologies. They are considering these points. This paper also discusses the various benefits of the cloud with its challenges and applications
Faezeh Gholamrezaie, Azar Feyziyev, Azerbaijan State Oil and Industry University
Azerbaijan Journal of High Performance Computing, Volume 5, pp 3-32;

The effect of dynamic and interactive events on the function of the elements that make up the computing system manager causes the time required to run the user program to increase or the operation of these elements to change. These changes either increase the execution time of the scientific program or make the system incapable of executing the program. Computational processes on the migration process and vector algebras try to analyze and enable the Flushing process migration mechanism in support of distributed Exascale systems despite dynamic and interactive events. This paper investigates the Flushing process migration management mechanism in distributed Exascale systems, the effects of dynamic and interactive occurrences in the computational system, and the impact of dynamic and interactive events on the system.
Hadis Oftadeh, Islamic Azad University, Mohammad Manthouri, Shahed University
Azerbaijan Journal of High Performance Computing, Volume 5, pp 112-130;

Correct diagnosis of diseases is the main problem in medicine. Artificial intelligence and learning methods have been developed to solve problems in many fields, such as biology and medical sciences. Correct diagnosis before treatment is the most challenging and the first step in achieving proper cures. The primary objective of this paper is to introduce an intelligent system that develops beyond the deep neural network. It can diagnose and distinguish between Hepatitis types B and C by using a set of general tests for liver health. The deep network used in this research is the Deep Boltzmann Machine (DBM). Learning components within Restricted Boltzmann Machine (RBM) lead to intended results. The RBMs extract features to be used in an efficient classification process. An RBM is robust computing and well-suited to extract high-level features and diagnose hepatitis B and C. The method was tested on general items in laboratory tests that check the liver’s health. The DBM could predict hepatitis type B and C with an accuracy between 90.1% and 92.04%. Predictive accuracy was obtained with10-fold cross-validation. Compared with other methods, simulation results on DBM architecture reveal the proposed method’s efficiency in diagnosing Hepatitis B and C. What made this approach successful was a deep learning network in addition to discovering communication and extracting knowledge from the data.
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