مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-1437124720241111Elymus Repens Optimization (ERO); A Novel Agricultural-Inspired AlgorithmElymus Repens Optimization (ERO); A Novel Agricultural-Inspired Algorithm17018210.61186/jist.41748.12.47.170enMahdiTouraniFaculty of Engineering, University of Birjand0000000301721101202341Optimization plays a crucial role in enhancing productivity within the industry. Employing this technique can lead to a reduction in system costs. There exist various efficient methods for optimization, each with its own set of advantages and disadvantages. Meanwhile, meta-heuristic algorithms offer a viable solution for achieving the optimal working point. These algorithms draw inspiration from nature, physical relationships, and other sources. The distinguishing factors between these methods lie in the accuracy of the final optimal solution and the speed of algorithm execution. The superior algorithm provides both precise and rapid optimal solutions. This paper introduces a novel agricultural-inspired algorithm named Elymus Repens Optimization (ERO). This optimization algorithm operates based on the behavioral patterns of Elymus Repens under cultivation conditions. Elymus repens is inclined to move to areas with more suitable conditions. In ERO, exploration and exploitation are carried out through Rhizome Optimization Operator and Stolon Optimization Operators. These two supplementary activities are used to explore the problem space. The potent combination of these operators, as presented in this paper, resolves the challenges encountered in previous research related to speed and accuracy in optimization issues. After the introduction and simulation of ERO, it is compared with popular search algorithms such as Gravitational Search Algorithm (GSA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The solution of 23 benchmark functions demonstrates that the proposed algorithm is highly efficient in terms of accuracy and speed.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-1437124720241111Enhancing IoT Security: A Comparative Analysis of Hybrid Hyperparameter Optimization for Deep Learning-Based Intrusion Detection SystemsEnhancing IoT Security: A Comparative Analysis of Hybrid Hyperparameter Optimization for Deep Learning-Based Intrusion Detection Systems18319610.61186/jist.46793.12.47.183enHeshamtAsadi1.Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. 0009-0000-8619-8158Mahmood Alborzi1.Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. 0000-0001-6619-992XHessam Zandhessami1.Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. 0000-0002-8815-915X2024523Rapidly expanding domains such as the Internet of Things require sophisticated approaches to securing interconnected devices against cyber threats. The following study intends to fill in a crucial gap in the state of effective intrusion detection systems for the Internet of Things based on a comparison and analysis of various hyperparameter optimization approaches to improve existing and future detection systems. In other words, our main goal was to investigate and compare various hyperparameter optimization strategies to find and assess the most effective way to improve the performance of deep learning -based IDS. Our methodology was comprised of the following comparative optimization analysis used to compare a hybrid optimization approach against stand-alone implementation of Harmony Search and Bayesian Optimization. The analysis was done quantitatively based on IDS trained and tested on simulated Internet of Things network data, and IDS performance was evaluated by the following metrics : accuracy, precision, recall, and F1 score. The comparison of results showed that the hybrid optimization demonstrated the best performance indicators in terms of accuracy at 99.74%, precision at 99.7%, recall at 99.72%, and F1 score at 99.71%. The results of the study confirm the efficiency of implementing multiple optimization approaches and reveal the potential effectiveness of such combination for effective hyperparameter optimization of deep learning -based IDS in the Internet of Things environment.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-1437124720241111A Survey of Intrusion Detection Systems Based On Deep Learning for IoT DataA Survey of Intrusion Detection Systems Based On Deep Learning for IoT Data19720710.61186/jist.44521.12.47.197enMehrnazMoudiDepartment of Computer Engineering, University of Torbat Heydariehhttps://orcid.org/0000-0002-9081-5347ArefehSoleimaniDepartment of Computer Engineering, University of Torbat Heydariehhttps://orcid.org/0009000159388607AmirHossein Hojjati niaDepartment of Computer Engineering, University of Torbat Heydarieh000900078947530X20231025Today, the scope of using the Internet of Things is growing by taking science and technology as the first place in human life, and as these networks get bigger, more data are exchanged. It performs high-speed data exchanges on the Internet and in a pre-defined network. The more the Internet of Things penetrates into people's lives, the more important data it transmits. This causes attackers to draw attention to these data, and Internet of Things network devices that have limited resources are exposed to attacks. With the complexity of hardware and software for the ease of human's use, naturally more intelligent attacks will happen, which is the reason of presenting many methods in this field. For this reason, in this article, we are going to discuss the most important methods used in intrusion detection systems based on deep learning and machine that can identify these interruptions. In this article, we have compared 46 articles from 2020 to 2024 based on the type of dataset used, the type of classification (binary or multi-class) and the accuracy rates obtained from each method, and we have been able to see a comprehensive overview for researchers who intend to work in IoT data security. According to the obtained results, if the proposed method is implemented in binary form, it can achieve better accuracy than multi-class.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-1437124720241111Improving Opinion Mining Through Automatic Prompt ConstructionImproving Opinion Mining Through Automatic Prompt Construction21622710.61186/jist.46273.12.47.216enArashYousefi JordehiDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan, Iran0000-0001-8136-2246Mahsa Hosseini Khasheh HeyranDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan, Iran0000-0002-5295-2789SaeedAhmadniaDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan, Iran0000-0002-3034-6083Seyed AbolghassemMirroshandelDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Guilan, Iran0000-0001-8853-9112OwenRambowDepartment of Linguistics, Stony Brook University, Stony Brook, NY, USA0000-0003-2054-039X202442Opinion mining is a fundamental task in natural language processing. This paper focuses on extracting opinion structures: triplets representing an opinion, a part of text involving an opinion role, and a relation between opinion and role. We utilize the T5 generative transformer for this purpose. It also adopts a multi-task learning approach inspired by successful previous studies to enhance performance. Nevertheless, the success of generative models heavily relies on the prompts provided in the input, as prompts customize the task at hand. To eliminate the need for human-based prompt design and improve performance, we propose Automatic Prompt Construction, which involves fine-tuning. Our proposed method is fully compatible with multi-task learning, as we did so in our investigations. We run a comprehensive set of experiments on Multi-Perspective Question Answering (MPQA) 2.0, a commonly utilized benchmark dataset in this domain. We observe a considerable performance boost by combining automatic prompt construction with multi-task learning. Besides, we develop a new method that re-uses a model from one problem setting to improve another model in another setting as a Transfer Learning application. Our results on the MPQA represent a new state-of-the-art and provide clear directions for future work.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-1437124720241111Economic Impacts and Global Successes through the Internet of Everything (IoE) in the World CountriesEconomic Impacts and Global Successes through the Internet of Everything (IoE) in the World Countries22824010.61186/jist.44181.12.47.228enSeyed OmidAzarkasbK.N. Toosi University of Tecnology Tehran, Iran0009-0003-3322-2216Seyed HosseinKhastehK.N. Toosi University of Tecnology Tehran, Iran0000-0003-2227-45072023926The rapid evolution of information and communication technology (ICT) in recent decades has triggered profound transformations across the global economic landscape. A key driver of this transformation is the Internet of Everything (IoE), which integrates objects, data, people, and processes to create interconnected ecosystems that generate unprecedented value. The rise of IoE has not only revolutionized technological innovation but has also played a critical role in reshaping global economies by fostering competitiveness and unlocking new economic opportunities. This article examines the economic impacts and technological breakthroughs driven by IoE in six selected countries—spanning developed, developing, and neighboring economies. By analyzing their experiences, we highlight how these nations have utilized IoE to achieve sustainable growth, strengthen market positions, and accelerate their technological advancement. Countries venturing into the realm of IoE benefit from two key aspects. Firstly, they gain new value from technological innovation, and secondly, they secure competitive advantages and market shares against nations that have yet to invest and adapt to the IoE market. Studying pioneering and trailblazing countries in the realm of this technology, unveiling their patterns, visions, and key achievements, not only provides clear insights, identifies needs, and fosters advancements, but also critically examines and analyzes the subject matter. The findings offer essential insights for policymakers, business leaders, and innovators, providing a roadmap for leveraging IoE to maximize economic benefits and drive digital transformation on a global scale.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-1437124720241111GOA-ISR: A Grasshopper Optimization Algorithm for Improved Image Super-ResolutionGOA-ISR: A Grasshopper Optimization Algorithm for Improved Image Super-Resolution24124910.61186/jist.44920.12.47.241enBaharGhaderiDepartment of Electrical Engineering ,Faculty of Engineering Shiraz Branch ,Islamic Azad University, Shiraz ,Iran 0009-0003-2094-5634HamidAzadDepartment of Electrical Engineering ,Faculty of Engineering Shiraz Branch ,Islamic Azad University, Shiraz ,Iran 0000-0003-1246-4956HamedAgahiDepartment of Electrical Engineering ,Faculty of Engineering Shiraz Branch ,Islamic Azad University, Shiraz ,Iran 0000-0002-9407-00982023123The image super-resolution (ISR) process provides a high-resolution (HR) image from an input low-resolution (LR) image. This process is an important and challenging issue in computer vision and image processing. Various methods are used for ISR, that learning-based methods are one of the most widely used methods in this field. In this approach, a set of training images is used in various learning based ISR methods to reconstruct the input LR image. To this end, appropriate reconstruction weights for the image must be computed. In general, the least-squares estimation (LSE) approach is used for obtaining optimal reconstruction weights. The accuracy of SR depends on the effectiveness of minimizing the LSE problem. Therefore, it is still a challenge to obtain more accurate reconstruction weights for better SR processing. In this study, a Grasshopper Optimization Algorithm (GOA)-based ISR method (GOA-ISR) is proposed in order to minimize the LSE problem more effectively. A new formulation for the upper bound and the lower bound is introduced to make the search process of the GOA algorithm suitable for ISR. The simulation results on DIVerse 2K (DIV2K) dataset, URBAN100, BSD100, Set 14 and Set 5 datasets affirm the advantage of the proposed GOA-ISR approach in comparison with some other basic Neighbor Embedding (NE), Sparse Coding (SC), Adaptive Sparse, Iterative Kernel Correction (IKC), Second-order Attention Network (SAN), Sparse Neighbor Embedding and Grey Wolf Optimizer (GWO) methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The results of the experiments show the superiority of the proposed method comparing to the best compared method (DWSR) increases 8.613 % PSNR.

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