International Journal Information and Communication Technology Research
Volume:16 Issue: 2, Spring 2024

 In intelligent transport system (ITS) networks, it is very useful to reduce the transmission energy consumption. Device-to-device (D2D) communication is a key technology that can improve the performance of the ITS networks. In this paper, we investigate a D2D-enabled vehicular network and study a power allocation strategy to maximize the spectral efficiency of the network. Also, for safety-critical vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) applications, it is presented a reliable connection during the V2V and V2I links. The performance analysis of the investigated network shows that the studied method can maximize the total spectral efficiency of the network subject to satisfy some constraints when the channel state information (CSI) is perfectly known or not.

The optimal routing will only be possible if the link weights are calculated correctly. Also, the weight of the links should be different based on the type of destination nodes and the traffic flow of each path in the network and should be updated in real time. In this study, using multi-criteria decision-making techniques, the criteria related to each type of destination node and the importance of that criterion according to the type of the destination node, are considered. This research is applied based on the purpose. Library studies have been used to extract criteria related to each type of the destination node. Also, the field method has been used to calculate the weight of each criterion. The simulation of the proposed method showed that this method has reduced the average weight of links in routing by about half compared to the method that considered only the "distance" criterion.

As an emerging technology that combines both digital and physical realms, access to information technology has expanded (IoT) the Internet of Things. The Internet of Things, as it becomes more pervasive, will overshadow human life as much as possible. Some of the major challenges associated with the development of this phenomenon have been the issue of security, which is needed in all its layers and even specifically in individual layers. According to the structure and applications of the Internet of Things, as well as the threats and challenges in cyberspace, we first examine security needs and then, by examining some methods of securing the Internet of Things, we propose a method according to the approaches discussed.

 Web application (app) exploration is a crucial part of various analysis and testing techniques. However, the current methods are not able to properly explore the state space of web apps. As a result, techniques must be developed to guide the exploration in order to get acceptable functionality coverage for web apps. Reinforcement Learning (RL) is a machine learning method in which the best way to do a task is learned through trial and error, with the help of positive or negative rewards, instead of direct supervision. Deep RL is a recent expansion of RL that makes use of neural networks’ learning capabilities. This feature makes Deep RL suitable for exploring the complex state space of web apps. However, current methods provide fundamental RL. In this research, we offer DeepEx, a Deep RL-based exploration strategy for systematically exploring web apps. Empirically evaluated on seven open-source web apps, DeepEx demonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the stateof-the-art RL-based method. Additionally, it showed a 19% increase in structural diversity. These results confirm the superiority of Deep RL over traditional RL methods in web app exploration.

In recent years, the performance of deep neural networks in improving the image retrieval process has been remarkable. Utilizing deep neural networks; however, leads to poor results in retrieving images with missing regions. The operators’ dysfunctions, who consider the relationship between the image pixels, statistically extract incomplete information from an image, which in turn reduces the number of image features and or leads to features' inaccurate identification. An attempt has been made to eliminate the problem of missing image information through image inpainting techniques; therefore, a content-based image retrieval method is proposed for images with missing regions. In this method, through image inpainting the crucial missing information is reconstructed. The image dataset is being queried to find similar samples. For this purpose, a two-stage inpainting framework based on encoder-decoder is used in the image retrieval system. Also, the features of each image are extracted from the integration and concatenating of content and semantic features. Through using handcraft features such as color and texture image content information is extracted from the Resnet-50 deep neural network. Finally, similar images are retrieved based on the minimum Euclidean distance. The performance of the image retrieval model with missing regions is evaluated with the average precision criterion on the Paris 6K datasets. The best retrieval results are 60.11%, 50.14%, and 42.43% for retrieving the top one, five, and ten samples after reconstructing the image with the most missing regions with a destruction frequency of 6 Hz, respectively.

With the start of the Coronavirus epidemic, remote education systems were widely accepted in all universities around the world. The University of Tehran, the top university in Iran, had an electronic education system since 2010, which provided services in this field to the academic community. With the spread of the pandemic, these services became widespread in all departments of the University of Tehran. Compared to traditional learning, E-Learning brings challenges for Users, the main challenge of which is Users’ Satisfaction. In this paper, by examining the developed model based on the TAM model and the factor analysis of this model, the level of satisfaction of student users with the elearning management system of the University of Tehran during the Corona era is carefully evaluated. The exploitation of this model with the analytical method of partial least squares in the construction of structural equations is based on the information collected from the questionnaire that has been provided to the users of the e-learning system of the University of Tehran. The results obtained from this study show that the Technology Acceptance Model (TAM) describes well the factors affecting the level of user satisfaction with the E-learning management system. The positive effect of the visual beauty and the quality of the information in this system has improved the technical quality of the service; as a result, the level of user satisfaction in using this system has increased.