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Showing 4 results for Hashemi
Dr. Ghasem Sadeghi Bajestani, Msc Abbas Monzavi, Prof. Seyed Mohamad Reza Hashemi Golpaygani,
Volume 13, Issue 3 (12-2016)
Volume 13, Issue 3 (12-2016)
Abstract
The most important method for behavior recognition of recurrent maps is to plot bifurcation diagram. In conventional method used for plotting bifurcation diagram, a couple of time series for different values of model parameter have been generated and these points have been plotted with due respect to it after transient state. It does not have enough accuracy necessary for period detection and essential for discrimination between long periodic behaviors from chaotic behaviors; on the other hand because of being 2-dimensinal, it will not be possible to investigate the effect if the initial condition is in the basin of attraction.
In this research, a new bifurcation diagram is presented which is called: Qualitative Bifurcation Diagram (QBD). QBD provides accurate determination of periodicity. Results of our algorithm implementation on logistic map, represents its ability on determining long periods and period windows. Bifurcation diagram of logistic map does not obey mosaic tiling patterns (patterns that are created by arrangement not interaction) as a disciplinein addition to having the dynamic order. Some benefits of QBD are: long period discrimination, period window detection, computation time reduction, period presentation instead of amplitude show. In the following we have an analytical survey to Lyapunov exponent – as a usual measurement tool for chaotic behavior – and important notes are expressed. Finally, Recurrent Quantification Analysis (RQA) and QBD are compared.
Mina Abbaspour Orangi, Seyyed Alireza Hashemi Golpayegani,
Volume 18, Issue 2 (10-2021)
Volume 18, Issue 2 (10-2021)
Abstract
Trust is one of the most important cornerstones in social networks' discussions. mostly the way that users of these networks trust each other are considered identical, while these users can have different approaches and considerations in trusting others. Meanwhile, users can impress each other and change their trusting patterns in other users. As a result, the mechanism and manner of impressing opinion trust behavior and conditions of behavioral modes changing have a place of importance to be considered. The question is that, how we can consider different behavior of users and their impression in trusting others? In the first step, the main purpose of this paper is to spotlight social networks' different user behavior in trusting other users. For this purpose, the three most important behavioral modes in users trust are considered. In each of these modes, behavioral and functional characteristics of users are the basis of calculating trust, which is based on mental beliefs of them. These modes are named as optimistic, moderate, and pessimistic trusting modes. In optimistic mode, we suppose that users think positively and consider low level of activities and signs in trusting others. Here, negative interactions have little impact on users mind. In moderate mode, we suppose that users are not as optimistic as mode A and consider all the interactions and signs when they want to trust others. Here, any negative action can destroy the trust of users and has a greater impact on users. Finally, in pessimistic mode, we suppose that users are pessimistic and hardly trust someone. In this mode, the interactions that happened more recently have more value than those happened in the past.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
Hamid Darabian, Sattar Hashemi, Sajad Homayoon, Karamollah Bagherifard,
Volume 18, Issue 3 (12-2021)
Volume 18, Issue 3 (12-2021)
Abstract
Nowadays, crypto-ransomware is considered as one of the most threats in cybersecurity. Crypto ransomware removes data access by encrypting valuable data and requests a ransom payment to allow data decryption. The number of Crypto ransomware variants has increased rapidly every year, and ransomware needs to be distinguished from the goodware types and other types of ransomware to protect users' machines from ransomware-based attacks. Most published works considered System File and process behavior to identify ransomware which depend on how quickly and accurately system logs can be obtained and mined to detect abnormalities. Due to the severity of irreparable damage of ransomware attacks, timely detection of ransomware is of great importance. This paper focuses on the early detection of ransomware samples by analyzing behavioral logs of programs executing on the operating system before the malicious program destroy all the files. Sequential Pattern Mining is utilized to find Maximal Sequential Patterns of activities within different ransomware families as candidate features for classification. First, we prepare our test environment to execute and collect activity logs of 572 TeslaCrypt samples, 535 Cerber ransomware, and 517 Locky ransomware samples. Our testbed has the capability to be used in other projects where the automatic execution of malware samples is essential. Then, we extracted valuable features from the output of the Sequence Mining technique to train a classification algorithm for detecting ransomware samples. 99% accuracy in detecting ransomware instances from benign samples and 96.5% accuracy in detecting family of a given ransomware sample proves the usefulness and practicality of our proposed methods in detecting ransomware samples.
Nasim Tohidi, Seyed Mohammad Hossein Hasheminejad,
Volume 19, Issue 2 (9-2022)
Volume 19, Issue 2 (9-2022)
Abstract
One of the most important research areas in natural language processing is Question Answering Systems (QASs). Existing search engines, with Google at the top, have many remarkable capabilities. However, there is a basic limitation; search engines do not have deduction capability which a QAS is expected to have. In this perspective, a search engine may be viewed as a semi-mechanized QAS. Upgrading a search engine such to a QAS is a task whose complexity is hard to exaggerate. To achieve success, new concepts and ideas are needed to address difficult problems which arise when knowledge has to be dealt with in an environment of imprecision, uncertainty and partial truth.
QASs are search engines that have the ability to provide a brief and accurate answer to each question in natural language for instance, the question that a search engine answers with a set of documents, a QAS answers with a paragraph, sentence or etc. In this paper, a solution is proposed to optimize the performance and speed of web-based QASs for answering English questions.
As evolutionary algorithms are suitable for issues with large search space, in this approach we have used an evolutionary algorithm to optimize QASs. In this regard, we have chosen APSO which is a simplified version of PSO. The proposed method consists of five main stages: question analysis, pre-process, retrieval, extraction and ranking. We have tried to provide a method that would be more accurate in choosing the most probable answer from the documents that have been retrieved by the standard search engine and at the same time, be faster than similar methods. In ranking process, various attributes can be extracted from the text that are used in APSO. For this purpose, in addition to selecting a sentence from the text and examining its attributes, different cut parts of the sentence are selected each time by changing the beginning and end points of the cut part. The attributes which have been used in this study are: 1. Number of unigrams similar to the question words, 2. Number of bigrams similar to the question words, 3. Number of unigrams similar to the question words in the cut part, 4. Number of bigrams similar to the question words in the cut part, 5. Number of synonyms with the question words and 6. Number of synonyms with the question words in the cut part. The fitness function is the weighted sum of these attributes.
Top-1 accuracy and MRR are the most valid metrics for measuring the performance of QASs. The proposed method has achieved the accuracy (top-1 accuracy) of 0.527 with respect to the standard dataset and the MRR of it, is 0.711. Both of these results are improved compared to most similar systems. In addition, the time taken to answer the input question in the proposed method, has been significantly reduced compared to similar methods. In general, the accuracy and MRR in this paper have progressed and the system needs less time to find the answer, in comparison with existing QASs.
QASs are search engines that have the ability to provide a brief and accurate answer to each question in natural language for instance, the question that a search engine answers with a set of documents, a QAS answers with a paragraph, sentence or etc. In this paper, a solution is proposed to optimize the performance and speed of web-based QASs for answering English questions.
As evolutionary algorithms are suitable for issues with large search space, in this approach we have used an evolutionary algorithm to optimize QASs. In this regard, we have chosen APSO which is a simplified version of PSO. The proposed method consists of five main stages: question analysis, pre-process, retrieval, extraction and ranking. We have tried to provide a method that would be more accurate in choosing the most probable answer from the documents that have been retrieved by the standard search engine and at the same time, be faster than similar methods. In ranking process, various attributes can be extracted from the text that are used in APSO. For this purpose, in addition to selecting a sentence from the text and examining its attributes, different cut parts of the sentence are selected each time by changing the beginning and end points of the cut part. The attributes which have been used in this study are: 1. Number of unigrams similar to the question words, 2. Number of bigrams similar to the question words, 3. Number of unigrams similar to the question words in the cut part, 4. Number of bigrams similar to the question words in the cut part, 5. Number of synonyms with the question words and 6. Number of synonyms with the question words in the cut part. The fitness function is the weighted sum of these attributes.
Top-1 accuracy and MRR are the most valid metrics for measuring the performance of QASs. The proposed method has achieved the accuracy (top-1 accuracy) of 0.527 with respect to the standard dataset and the MRR of it, is 0.711. Both of these results are improved compared to most similar systems. In addition, the time taken to answer the input question in the proposed method, has been significantly reduced compared to similar methods. In general, the accuracy and MRR in this paper have progressed and the system needs less time to find the answer, in comparison with existing QASs.
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