mohammadali javadzadeh

  The brain-computer interface system provides a communication path between the brain and the computer and has recently received increasing attention. One of the most common paradigms of brain-computer interface systems is motor imagery. The braincomputer interface system based on motor imagery using electroencephalogram signals uses a large number of channels when receiving signals. Channels not related to the intended task cause unwanted interference and increase the noise level. In this paper, we present two optimal channel selection methods to improve common spatial pattern (CSP)-related features for classification of motor imagery tasks. Since the brain activities of motor imagery are located in a specific area of the brain, how to choose the right channels is important to improve the performance of the brain-computer interface. In this paper, analysis of variance (ANOVA) feature selection and sequential forward feature selection (SFFS) combined with common spatial pattern (CSP) are used to select optimal electrode channels. The results show that the accuracy of KNN, SVM and LDA classifications when using ANOVA+CSP method is 74, 72 and 71% respectively, when using SFFS+CSP method is 74, 73 and 68% respectively, when using CSP alone, 65, 62 and 60%, respectively, when not using the methods of selecting optimal channels and common spatial pattern, it is 58, 64 and 57%, respectively; Therefore, the combination of optimal channel selection methods and common spatial pattern has increased the accuracy of the classifiers.

Due to the ever-increasing amount of data, the amount of textual data is also growing at a high speed. Extracting information from these textual data is one of the necessities of today's information-based world. Text classification is one of the methods of obtaining information from this massive data. In this research, using a standard dataset of Persian news, which included five features in more than 86 thousand news, we investigated the performance of the logistic regression algorithm in the classification of Persian text and also compared it with other similar works. Considering the steps of creating a text category, we have explained the method used in the vectorization section and also stated the importance of the pre-processing section, especially the method used in tagging and converting sub-tags to main ones. In the final evaluation, by changing the algorithm's parameters and modifying the news tags, we reached the desired result of 95% in the accuracy criterion for the text classification of the Persian news dataset.

Two factors of the rapid growth of various sciences in recent years and the growth and spread of the global Internet network have caused several thousand articles in various fields to be published on the Internet every day, and become available to everyone. This problem creates a challenge for the specialists of different sciences, because they have to look for their interests and scientific field in a large number of published articles so that they can always remain an expert. In this research, for the first time, we intend to introduce a recommender system that suggests suitable articles to Persian language users according to their interests. The purpose of this research is to provide basic research for this category of issues in Persian language, to be a starting point for other researchers. In this study, we want to propose articles based on the semantic similarity of the title, abstract and keywords of the article (with appropriate weighting) with the user's academic records. After the implementation and placement of the model as a pilot on the internet platform. A group of undergraduate students evaluated the model's computer software, and the overall accuracy of the system was evaluated at 79%.

The growing number of malware is one of the major threats in the field of cyber and its detection has always been associated with challenges. Windows-based malicious executable files perform malicious activity at the target operating system level or any other application by manipulating features in their header and obscuring their behavior. Detecting suspicious specimens from a large volume of input samples as well as discovering new and unknown malware is one of the topics that is always researched by researchers. In this study, a combined method has been proposed to determine the level of maliciousness of suspicious executable files. Kashif's proposed method consists of two static modules for extracting executable file header properties, and two behavioral modules for extracting signature-generating properties and a thoughtful behavioral model based on machine learning methods. The purpose of this study is to identify suspicious Windows executable files from the large volume of files and determine their maliciousness level. This method detects malware based on the maliciousness probability of being assigned to each file. Experiments showed a malignancy percentage of six types of malware for PE header detector module, in the range of 62.7 to 70%, Yara-based detector module, in the range of 70.8 to 78.2%, Behavioral signature-based detector module, 98% and ML-based detector module by using Random forest learning algorithm has been 99% accuracy. The experimental results also showed that Kashef detected 94% of the protected malware with a 2% improvement compared to the results of 10 similar products. And with 98% detection of unprotected malware, there is a 5% improvement compared to the results of 10 similar products.

Earthquake is one of the eminent dangers that threaten the lives of people in Tehran. Therefore, the urban management administration should make the best decisions for prevention and safeguard measures after the occurrence of this natural event. Due to the psychological effects of the crisis time in the managers' decision-making, necessarily a mechanism independent of the crisis effects should be designed that analyzes the urban situation and provides crisis management proposals. Tehran consists of 22 zones that are located on 13 faults. Hence in this paper we design and present an expert system which uses process pattern and takes important physical and urban concepts indices, as well as the factors that are identified by the Center for Vibration Detection as input, and offers the necessary plan to take appropriate measures relating to each of the 22 zones’ safety centers in Tehran. This plan offers the best actions for each region in the event of an earthquake, according to the indicators of the region. The purpose of this paper is making crisis management smart in order to minimize damage in the event of an earthquake. Finally, to evaluate and compare the proposed method with Mercalli and Richter methods, the occurrence of a hypothetical earthquake in Ozgol area of region number 1 of Tehran was assumed, and a program preview implemented. The results show that the method presented in this article is more precise than Richter and Mercalli methods in proposing the provisional measures for the mentioned earthquake, due to the effectiveness of various factors in this method.

Researchers have always been interested in graph nodes clustering based on content or structure. But less attention has been paid to clustering based on both structure and content. But a content-structural clustering is needed in information networks like social networks. In this paper, the ICS-Cluster algorithm is proposed which takes into consideration both the structure and content aspects of the nodes. The purpose of this approach is to gain a coherent internal structure (structural aspect) and homogeneous attribute values (content aspect) in the graph. In this approach firstly the graph is converted into a content-structural graph which edges’ weight show similarity between the connected nodes. Incremental clustering is done based on edges’ weight in this process the edges with the most weight is considered as clusters then the weight of connected edge to the cluster is aggregated and they’ll be one edge, the process is repeated until the algorithm reaches the number of clusters that indicated by the user. ICS-Cluster algorithm number of cluster is indicated by the user. Comparing ICS-Cluster with other content structural algorithm based on six criteria for measuring cluster quality shows that ICS-Cluster has good performance. These criteria contain structural criteria (Modularity, Error Link, and Density), content criterion (Average Similarity), content-structural criterion (CS-Measure) and the run time.

Most applications of sensor nodes are in hazardous areas, inaccessible or hostile environments. Therefore, the need for security in these networks is essential. Trust methods are powerful tools for diagnosing unexpected behavior of nodes (malicious nodes or failure nodes). In this paper, we have  proposed TBSI trust model whose main features are low computational overhead, low energy consumption and confronting attacks in WSNs. This model is simulated and evaluated by NS-2 simulator and its behavior has been evaluated based on the results of these simulations. Examining practical results shows that energy consumption, routing overhead, and the time of death of nodes are reduced and the rate of packet delivery to the base station is increased. These desirable outcomes prove that using the method of trust to achieve a secure network is a good solution to solve security issues in wireless sensor networks.

Considering the rapid advances in high quality mobile devices, knowledge transfer over mobile network has become a growing field of research. The major problem in knowledge transfer system in mobile environment is to deliver the needed knowledge to the user for decision making in the right time and right context. The problem of knowledge transfer in the "right time" has been studied previously, but less attention has been paid to the problem of knowledge transfer considering the "right context". In this paper, the problem of knowledge transfer in the mobile environment, according to the context information is discussed. An architecture is proposed for knowledge management system in a mobile network based on GPS to predict the road traffic congestion and to guide derivers. The anticipation of traffic condition helps traffic managers to avoid the negative consequences of high traffic hours.