مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143713492025525Outage Performance of Cooperative Underlay Cognitive Radio Relay Based NOMA Networks with Energy Harvesting Capability Outage Performance of Cooperative Underlay Cognitive Radio Relay Based NOMA Networks with Energy Harvesting Capability 11110.61186/jist.44610.13.49.1enmaryamnajimiDepartment of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran 0000-0001-8835-30512023115In this work, Non-orthogonal multiple access (NOMA) technology is considered in cognitive radio (CR) networks in which the secondary users can only access the utilized spectrum of the primary user such that the primary user can tolerate the interference created by the secondary network. On the other words, the combination of CR and NOMA (CR-NOMA) is a novel concept to enhance the spectrum efficiency and the reliability of the network communication. The relaying technology with capability of energy harvesting is also considered which can improve the outage performance. In this scheme, the proper relay harvests energy from the secondary transmitter while it transmits the data of the secondary transmitter to the corresponding receiver. With this regard, the network throughput is improved in outage behavior and imperfect successive interference cancellation (SIC) condition at two users. Hence, the proposed problem is maximizing the performance of the network by proper selection of the relay for data transmission, setting the transmission power of the selected relay and optimal power allocation coefficients to each user with constraints on the outage probability and the interference in the primary user communication. For solving the problem, an iterative low complexity algorithm is proposed using the convex optimization scheme and Karush–Kuhn–Tucker conditions to select the best relay for transmission and users’ power allocation coefficients and also set the transmission power of the selected relay.  Simulation results verify the effectiveness of the proposed algorithm  for increasing almost 30 percent of the network performance in comparison to the bench mark algorithms in different conditions.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143713492025525Numerical Study of a Switchable Polarization for Reflect-array Unit-cell for Satellite CommunicationsNumerical Study of a Switchable Polarization for Reflect-array Unit-cell for Satellite Communications122310.61186/jist.46276.13.49.12enMohammadMansouriniaFaculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran0009-0004-7153-5085Ramezan AliSadeghzadehFaculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran0000-0002-9156-1342202442The purpose of this paper is to design and simulate a unit cell that is wideband and multi-polarized for a reflect-array antenna. Bandwidth of structure is greater than 70% of X and Ku bands for satellite applications that reached from two printed dipoles and asymmetric arrow head shape in structure for multi electric and magnetic resonances and used thicker substrate for enhancement of BW by stack up of substrate with different layers. Depending on the antenna usage, the proposed structure can provide dual or triple polarization switching through the use of one or two control bits. Among the innovations of this structure compared to other activities, it can be said that the switching capability to support multiple polarizations occurs in a common wide bandwidth, or in other words, each of the switching modes is not in different single frequency or a bandwidth of frequencies. Comparing the switching modes of the proposed design with other existing control structures, the feature of maintaining the polarization of the feeding antenna or converting it to orthogonal polarization in different bits distinguishes the proposed structure. The proposed design has a new geometrical structure and considering that it can have the main polarization of the feeding antenna, its orthogonal polarization and even circular polarization in any switching mode, it has a relatively simple geometry, which reduces the complexity of the construction mechanism.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143713492025525A Turkish Dataset and BERTurk-Contrastive Model for Semantic Textual SimilarityA Turkish Dataset and BERTurk-Contrastive Model for Semantic Textual Similarity243210.61186/jist.48127.13.49.24enSomaiyehDehghanDepartment of Computer Engineering, Yildiz Technical University, Istanbul, Turkey0000-0002-5011-5821Mehmet FatihAmasyaliDepartment of Computer Engineering, Yildiz Technical University, Istanbul, Turkey0000-0002-0404-59732024927Semantic Textual Similarity (STS) is an important NLP task that measures the degree of semantic equivalence between two texts, even if the sentence pairs contain different words. While extensively studied in English, STS has received limited attention in Turkish. This study introduces BERTurk-contrastive, a novel BERT-based model leveraging contrastive learning to enhance the STS task in Turkish. Our model aims to learn representations by bringing similar sentences closer together in the embedding space while pushing dissimilar ones farther apart. To support this task, we release SICK-tr, a new STS dataset in Turkish, created by translating the English SICK dataset. We evaluate our model on STSb-tr and SICK-tr, achieving a significant improvement of 5.92 points over previous models. These results establish BERTurk-contrastive as a robust solution for STS in Turkish and provide a new benchmark for future research.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143713492025525Review on architecture and challenges on smart citiesReview on architecture and challenges on smart cities334910.61186/jist.48360.13.49.33enMehdiAzadimotlaghDepartment of Computer Engineering of Jam, Persian Gulf University, Jam, IranNarges JafariDepartment of Computer Engineering, Amirkabir University of Technology, Tehran, IranRezaSharafdiniDepartment of Mathematics, Persian Gulf University, Bushehr, Iran0000-0002-3171-220920241025Due to rapid urbanization, a balance between resources and urban growth is required. For the achievement of this equilibrium, the use of information technologies is essential. Smart cities are the answer to this requirement, as a result, they improve various aspects of urban life and address related challenges and/or mitigate them. Modern technologies, including a wide range of Internet of Things (IoT) sensors, are used in smart cities for collecting and analyzing data on different aspects of urban life to enhance their inhabitants' lives. Smart cities improve the sustainability and efficiency of urban dynamics. Today, smart cities can enhance services and citizens' lives in various fields such as governance, education, healthcare, transportation, and energy. Smart city applications require collaboration among individuals from various disciplines, including engineering, architecture, urban design, and economics, to plan, design, implement, and deploy a smart solution for a specific task. Therefore, a proper understanding of the applications and architecture of smart cities and the challenges they face is crucial. In this paper, we will provide background information about the applications of smart cities, describe the architecture of applications in smart cities, present security and privacy challenges to examine robustness and flexibility in smart city applications, and examine new trends in this field.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143713492025525A Novel Hybrid Convolutional-Attention Recurrent Network (HCARN) for Enhanced Cybersecurity Threat DetectionA Novel Hybrid Convolutional-Attention Recurrent Network (HCARN) for Enhanced Cybersecurity Threat Detection506210.61186/jist.48536.13.49.50enArchanaLaddhadFaculty of Computer Science, Oriental University, Indore – Madhya Pradesh, India 0009-0009-3362-7778 Gurveen VaseerFaculty of Computer Science, Oriental University, Indore – Madhya Pradesh, India 0000-0002-9198-445X2024119Cybersecurity solutions are critical for the protection of networks against constantly evolving threats. Traditional intrusion detection systems (IDS) struggle to adapt to the rapidly varying attack patterns, encouraging the exploration of advanced techniques such as deep learning. This study introduces a novel framework utilizing a Hybrid Convolutional-Attention Recurrent Network (HCARN) for identifying cybersecurity threat. Utilizing the CSE-CIC-IDS2018 dataset, the data preparation process includes data cleanup, feature extraction, and Information Gain-based feature choice. The HCARN architecture, integrates convolutional layers, attention mechanisms, and recurrent layers, is employed for categorization.  Convolutional layers effectively capture spatial features in the dataset, attention mechanisms highlight critical features, and recurrent layers model temporal dependencies. This allows HCARN to process and analyze complex patterns in network traffic, leading to more accurate threat diagnosis. The proposed model proves significant efficacy in distinguishing between major, moderate, and minor threats, attaining high accuracy and robustness in threat recognition. The incorporation of attention mechanisms allows the model to emphasize on critical features, while the recurrent layers pay attention to temporal dependencies in the dataset. The HCARN architecture determines classification accuracy, achieving 94.7% in K-fold validation, 95.4% in model training, and 92.3% in model testing while classifying major, moderate, minor threats satisfactorily, confirming its effectiveness in cybersecurity threat detection. This novel attempt underscores the potential of hybrid deep learning models in enhancing cybersecurity defenses against sophisticated attacks, paving the way for adaptive security systems.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143713492025525Enhancing IoT Device Behavior Prediction through Machine Learning ModelsEnhancing IoT Device Behavior Prediction through Machine Learning Models637610.61186/jist.47570.13.49.63enShubhamMinhassAmity Institute of Information Technology, Amity university, Noida, Uttar Pradesh, India RituChauhanArtificial Intelligence and IoT Lab, Center for Computational Biology and Bioinformatics, Amity university, Noida, Uttar Pradesh, IndiaHarleenKaurDepartment of Computer Science and Engineering, Jamia Hamdard, Delhi, India 202486There is an urgent need for precise and trustworthy models to forecast device behavior and evaluate vulnerabilities as a result of the Internet of Things' (IoT) explosive growth. By assessing the effectiveness of several machine learning algorithms logistic regression, decision trees, random forests, Naïve Bayes, and KNN on two popular IoT devices Alexa and Google Home Mini this study seeks to enhance IoT device behavior forecasting. Our results show that Naïve Bayes and random forest models are more accurate and efficient than other algorithms at predicting device behavior. These findings demonstrate how important algorithm selection is for maximizing the performance of IoT systems. The study also emphasizes the usefulness of precise device behavior prediction for practical uses such as industrial control systems, home automation, and medical monitoring. For example, accurate forecasts can improve decision-making in crucial situations, facilitate more seamless automation, and stop system failures. In addition to adding to the expanding corpus of research on IoT data analysis, this study establishes the foundation for the creation of increasingly sophisticated machine learning models that can manage the intricate and ever-changing nature of IoT ecosystems. Future studies should concentrate on increasing the dataset's diversity to encompass a wider range of IoT environments and devices and enhancing the model's adaptability to changing IoT environments.

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