List of Articles Modeling

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  1 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
  Hamideh Sadat Cheraghchi Ali Zakerolhossieni
  10.7508/jist.2016.02.002
  The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social More

  The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript profile
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  2 - A Hybrid Cuckoo Search for Direct Blockmodeling
  Saeed NasehiMoghaddam mehdi ghazanfari babak teimourpour
  10.7508/jist.2017.18.001
  As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within posit More

  As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field. Manuscript profile
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  3 - ANFIS Modeling to Forecast Maintenance Cost of Associative Information Technology Services
  Reza Ehtesham Rasi Leila  Moradi
  10.7508/jist.2017.20.007
  Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for quantifying Information Technology (IT) Generated Services perceptible by business users. In addition to forecasting, IT cost related to system maintenance can help managers for future and constructive deci More

  Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for quantifying Information Technology (IT) Generated Services perceptible by business users. In addition to forecasting, IT cost related to system maintenance can help managers for future and constructive decision. This model has been applied by previous large volume of data from IT cost factors, generated services, and associative cost for building pattern, tuning and training this model well. First of all, the model was fully developed, stabilized, and passed through intensive training with large volume of data collected in an organization. It can be possible to feed a specific time period of data into the model to determine the quantity of services and their related maintenance cost. ANFIS forecasting maintenance cost of measured service availability totally provided with first quantifying services in a specific time period. Having an operational mechanism for measuring and quantifying information technology services tangible by users for estimating their costs is contributed to practical accurate investment. Some components have been considered and measured in the field of system maintenance. The main objective of this study was identifying and determining the amount of investment for maintenance of entire generated services by consideration of their relations to tangible cost factors and also intangible cost connected to service lost. Manuscript profile
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  4 - Modeling the Inter-arrival Time of Packets in Network Traffic and Anomaly Detection Using the Zipf’s Law
  Ali Naghash Asadi Mohammad  Abdollahi Azgomi
  10.7508/jist.2018.02.003
  In this paper, a new method based on the Zipf’s law for modeling the features of the network traffic is proposed. The Zipf's law is an empirical law that provides the relationship between the frequency and rank of each category in the data set. Some data sets may follow More

  In this paper, a new method based on the Zipf’s law for modeling the features of the network traffic is proposed. The Zipf's law is an empirical law that provides the relationship between the frequency and rank of each category in the data set. Some data sets may follow from the Zipf’s law, but we show that each data set can be converted to the data set following from the Zipf’s law by changing the definition of categories. We use this law to model the inter-arrival time of packets in the normal network traffic and then we show that this model can be used to simulate the inter-arrival time of packets. The advantage of this law is that it can provide high similarity using less information. Furthermore, the Zipf’s law can model different features of the network traffic that may not follow from the mathematical distributions. The simple approach of this law can provide accuracy and lower limitations in comparison to existing methods. The Zipf's law can be also used as a criterion for anomaly detection. For this purpose, the TCP_Flood and UDP_Flood attacks are added to the inter-arrival time of packets and they are detected with high detection rate. We show that the Zipf’s law can create an accurate model of the feature to classify the feature values and obtain the rank of its categories, and this model can be used to simulate the feature values and detect anomalies. The evaluation results of the proposed method on MAWI and NUST traffic collections are presented in this paper. Manuscript profile
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  5 - Using Discrete Hidden Markov Model for Modelling and Forecasting the Tourism Demand in Isfahan
  Khatereh Ghasvarian Jahromi Vida Ghasvarian Jahromi
  10.7508/jist.2018.02.007
  Tourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the More

  Tourism has been increasingly gaining acceptance as a driving force to enhance the economic growth because it brings the per capita income, employment and foreign currency earnings. Since tourism affects other industries, in many countries, tourism is considered in the economic outlook. The perishable nature of most sections dependent on the tourism has turned the prediction of tourism demand an important issue for future success. The present study, for the first time, uses the Discrete Hidden Markov Model (DHMM) to predict the tourism demand. DHMM is the discrete form of the well-known HMM approach with the capability of parametric modeling the random processes. MATLAB Software is applied to simulate and implement the proposed method. The statistic reports of Iranian and foreign tourists visiting Isfahan gained by Iran Cultural Heritage, Handicrafts, and Tourism Organization (ICHHTO)-Isfahan Tourism used for simulation of the model. To evaluate the proposed method, the prediction results are compared to the results from Artificial Neural Network, Grey model and Persistence method on the same data. Three errors indexes, MAPE (%), RMSE, and MAE, are also applied to have a better comparison between them. The results reveal that compared to three other methods, DHMM performs better in predicting tourism demand for the next year, both for Iranian and foreign tourists. Manuscript profile
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  6 - An SRN Based Approach for Performance Evaluation of Network Layer in Mobile Ad hoc Networks
  meisam Yadollahzadeh tabari Ali A Pouyan
  10.7508/jist.2018.04.006
  The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing More

  The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing high level performance metrics. Also there is no theoretical background for mentioned simulators, too. In this research, we propose a framework for performance evaluation of mobile ad hoc networks. The presented framework points to the network layer of MANETs using SRN (Stochastic Reward Nets) modeling tool as variation of generalized stochastic Petri net (GSPN). Based on decomposition technique it encompasses two separate models: one for analysis of data flowing process and the other for modeling routing process ; supposing AODV as a routing protocol that is worked out. To verify the presented model, an equivalence-based method is applied. The proposed SRN model has been quantified by deriving two performances metrics as Packet Delivery Ratio (PDR) and End-to-end Delay. Both metrics are also compared to the value obtained from NS-2 simulator versus different number of nodes and four packet generation rates. The results show the obtained values from presented SRN model well matched to the values generated from NS-2 simulator with a considerable lesser execution time. Manuscript profile
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  7 - An Agent Based Model for Developing Air Traffic Management Software
  Mahdi Yosefzadeh Seyed Reza Kamel Tabbakh Seyed Javad  Mahdavi Chabok Maryam khairabadi
  10.52547/jist.15635.10.37.28
  20.1001.1.23221437.2022.10.37.3.3
  The Air Traffic Management system is a complex issue that faces factors such as Aircraft Crash Prevention, air traffic controllers pressure, unpredictable weather conditions, flight emergency situations, airplane hijacking, and the need for autonomy on the fly. agent-ba More

  The Air Traffic Management system is a complex issue that faces factors such as Aircraft Crash Prevention, air traffic controllers pressure, unpredictable weather conditions, flight emergency situations, airplane hijacking, and the need for autonomy on the fly. agent-based software engineering is a new aspect in software engineering that can provide autonomy. agent-based systems have some properties such: cooperation of agents with each other in order to meet their goals, autonomy in function, learning and Reliability that can be used for air traffic management systems. In this paper, we first study the agent-based software engineering and its methodologies, and then design a agent-based software model for air traffic management. The proposed model has five modules .this model is designed for aircraft ,air traffic control and navigations aids factors based on the Belief-Desire-Intention (BDI) architecture. The agent-based system was designed using the agent-tool under the multi-agent system engineering (MaSE) methodology, which was eventually developed by the agent-ATC toolkit. In this model, we consider agents for special occasions such as emergency flights’ and hijacking airplanes in airport air traffic management areas which is why the accuracy of the work increased. It also made the flight’s sequence arrangement in take-off and landing faster, which indicates a relative improvement in the parameters of the air traffic management Manuscript profile
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  8 - A Customized Web Spider for Why-QA Pairs Corpus Preparation
  Manvi  Breja
  10.52547/jist.15700.11.41.41
  20.1001.1.23221437.2023.11.41.2.7
  Considering the growth of researches on improving the performance of non-factoid question answering system, there is a need of an open-domain non-factoid dataset. There are some datasets available for non-factoid and even how-type questions but no appropriate dataset av More

  Considering the growth of researches on improving the performance of non-factoid question answering system, there is a need of an open-domain non-factoid dataset. There are some datasets available for non-factoid and even how-type questions but no appropriate dataset available which comprises only open-domain why-type questions that can cover all range of questions format. Why-questions play a significant role and are usually asked in every domain. They are more complex and difficult to get automatically answered by the system as why-questions seek reasoning for the task involved. They are prevalent and asked in curiosity by real users and thus their answering depends on the users’ need, knowledge, context and their experience. The paper develops a customized web crawler for gathering a set of why-questions from five popular question answering websites viz. Answers.com, Yahoo! Answers, Suzan Verberne’s open-source dataset, Quora and Ask.com available on Web irrespective of any domain. Along with the questions, their category, document title and appropriate answer candidates are also maintained in the dataset. With this, distribution of why-questions according to their type and category are illustrated. To the best of our knowledge, it is the first large enough dataset of 2000 open-domain why-questions with their relevant answers that will further help in stimulating researches focusing to improve the performance of non-factoid type why-QAS. Manuscript profile
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  9 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
  Alireza Mansouri Fattaneh Taghiyareh
  10.52547/jist.9.33.1
  20.1001.1.23221437.2021.9.33.2.7
  People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marke More

  People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links. Manuscript profile
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  10 - Analytical Model to Create Proxy Server Sessions in Multimedia Networks
  Mehdi Khazaei
  10.52547/jist.9.36.10
  20.1001.1.23221437.2021.9.36.10.1
  One of the most popular and widely applied protocols on multimedia networks is the Session Initiation Protocol (SIP) to create, modify, and terminate the sessions. SIP is the platform of Next Generation Networks (NGN). In this way, SIP should be able to respond to the n More

  One of the most popular and widely applied protocols on multimedia networks is the Session Initiation Protocol (SIP) to create, modify, and terminate the sessions. SIP is the platform of Next Generation Networks (NGN). In this way, SIP should be able to respond to the needs of such a largely-used network. One of the major problems in SIP networks is overload. This challenge creates a sharp drop in quality of service for NGN users. In this regard, many studies have been conducted on the effectiveness of this protocol, especially under overload. A new analytical model is developed that prioritizes the SIP message processing. An analytical approach is proposed based on the Mean Value Analysis (MVA) algorithm in queue theory. Considering some appropriate assumptions customizing MVA as to implement this proposed model and to cope with the limitations of the MVA is highly essential. The output of the analytical model is compared with the standard SIP model obtained from the simulator and the results confirm that prioritizing original messages would enhance the SIP performance at different load conditions. Prioritization of original messages is advantageous, and outperforms the normal SIP. Nevertheless, prioritizing the repeated messages not only has no advantage, but also its performance is less than the normal SIP. Manuscript profile