مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119A Hybrid Deep Learning Model Based on BLSTM-GRU for Enhanced Call Volume Forecasting in Call CentersA Hybrid Deep Learning Model Based on BLSTM-GRU for Enhanced Call Volume Forecasting in Call Centers18219510.66224/jist.52928.14.55.182enArmaghanNikfarDepartment of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran SasanHarifiDepartment of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iranhttps://orcid.org/0000-0002-6788-8222JavadMohammadzadehDepartment of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iranhttps://orcid.org/0000-0003-1889-0294202625This research aims to develop a forecasting model for call center call volume by incorporating characteristics unique to each country and call center environment. Using historical call data, calendar events, and other relevant input variables, the goal is to create a framework that is capable of accurately simulating and predicting the volume of incoming calls over specified time periods. For this purpose, we employ advanced recurrent neural network architectures, including MLP, RNN, LSTM, and a hybrid BLSTM-GRU model, to analyze temporal patterns in call data. The BLSTM-GRU model, in particular, uses two-way and gating processing mechanisms to more effectively capture complex temporal dependencies. The dataset consists of daily call volume over a 4-year period along with text features formatted in Excel for ease of training and evaluation. Furthermore, the forecasting framework integrates a comparison mechanism that compares the predicted call volume against the actual values to assess the accuracy and robustness of the model. Experimental results show that the BLSTM-GRU model outperforms standard architectures, achieving a forecasting accuracy of 94.12%, whereas the optimized single LSTM baseline reaches 81.35%. To increase operational efficiency, the framework also generates shift schedules that comply with call center standards and prevent call queues by recommending the required number of agents. The superior performance of the proposed BLSTM-GRU model highlights its potential application in various contact centers around the world and provides a scalable solution for intelligent contact center management.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119Neural Risk-Adaptive Access Control: A Behavior-Context Hybrid Framework for Adaptive Authorization in Dynamic EnvironmentsNeural Risk-Adaptive Access Control: A Behavior-Context Hybrid Framework for Adaptive Authorization in Dynamic Environments19621310.66224/jist.53783.14.55.196enDonyaPiriaeiDepartment of Computer Engineering, Bo. C., Islamic Azad University, Borujerd, IranAfshinRezakhaniDepartment of Computer Engineering, Faculty of Engineering, Ayatollah Boroujerdi University, Borujerd, IranHamidHaj Seyyed JavadiDepartment of Computer Engineering, Shahed University, Tehran, Iran0000-0003-0082-036XLeilaRikhtechi Department of Computer Engineering, Bo. C., Islamic Azad University, Borujerd, Iran202668We present a risk-adaptive access control framework that integrates dynamic behavioral modeling with Attribute-Based Access Control (ABAC) to enable real-time decision-making in volatile and high-risk environments. The proposed system introduces two novel components: the Attention-based Temporal Unit (ATU) for capturing sequential access behaviors, and the Contextual Scoring Module (CSM) for quantifying contextual and environmental signals. Risk is estimated via a multi-dimensional function that combines behavioral patterns, device and location attributes, and real-time threat indicators. An adaptive decision threshold, continuously refined based on false-accept and false-reject outcomes, ensures a calibrated balance between security and operational usability. Computed risk scores are seamlessly embedded into ABAC policies to support fine-grained enforcement. Evaluation on four domain-relevant datasets—Login Data Set for Risk-Based Authentication (RBA), CERT Insider Threat Test Dataset, CIC-IDS2017, and RT-IoT2022—used to emulate access control scenarios, shows F1-scores up to 0.95 and precision up to 0.94, outperforming strong baselines by up to 12%. The results demonstrate robustness across authentication, insider threat, intrusion, and IoT domains, supporting the framework’s applicability in real-time, Risk-Adaptive access systems for wireless communication and IoT environments, particularly in emerging 5G/6G contexts. The modular architecture also separates behavioral, contextual, and environmental evidence so that each source can contribute to the final authorization decision without requiring a single static risk signal.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119Fusion-Based Deep Learning Framework Detection Using an Optimized for Cardiovascular Disease Squeeze Net ModelFusion-Based Deep Learning Framework Detection Using an Optimized for Cardiovascular Disease Squeeze Net Model21422410.66224/jist.49428.14.55.214enM Shanmuga SundariDepartment of Computer Science and Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India0000-0001-5755-474X202527The electrocardiogram (ECG), which measures the electrical activity of the heart, is frequently used to identify heart conditions because it is simple and non-invasive. The primary focus of the analysis is on the fusion of action impulse patterns produced by the heart’s specialized cardiac tissues. It is possible to detect any irregularities in the heart by closely examining the electrical signal generated with each heartbeat. Since the application of data mining technology in healthcare has greatly increased knowledge, data mining is becoming a more and more popular option in the medical field. This study uses a hybridized algorithm for pattern mining in order to categorize heartbeats. First, the input data from the ECG signal is pre- processed using a median filter, and then the features are extracted. Both statistical and medical aspects are included in these extracted features. Activity pattern mining using the Optimization Search Algorithm (OSA), a hybridized optimization algorithm, is the next step. Lastly, the rule matrix produced by the mining of activity patterns is used to categorize heartbeats. This research proposes a deep learning-based framework for the classification of cardiovascular diseases using electrocardiogram (ECG) signals. A hybrid optimization technique, integrating the Taylor series and Ebola Optimization Search Algorithm (OSA), termed OSA-SN, is introduced to enhance the performance of a lightweight convolutional neural network model, SqueezeNet. The ECG signals undergo preprocessing and feature extraction, followed by sequential pattern mining using the proposed optimization strategy. The optimized model achieves superior performance metrics, with a precision of 0.93, sensitivity of 0.94, and specificity of 0.91. These results demonstrate the potential of the OSA-SN-enhanced SqueezeNet model in accurate, real-time heartbeat classification for clinical applications.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119Soft and Safe Network Slicing with Hierarchical Multi-Agent Reinforcement LearningSoft and Safe Network Slicing with Hierarchical Multi-Agent Reinforcement Learning22523710.66224/jist.51727.14.55.225enZeinabSasanComputer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranSiavashKhorsandiComputer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran0000-0002-2802-59422025106Network slicing has emerged as a key enabler for next-generation mobile networks, allowing multiple virtual network slices to coexist over shared physical infrastructure. Soft network slicing can improve resource utilization by enabling controlled inter-slice resource sharing; however, increased resource sharing may impose a higher burden on donor slices and consequently increase slice dissatisfaction. Achieving efficient resource utilization while controlling slice dissatisfaction and maintaining feasible resource allocation remains challenging under dynamic and heterogeneous traffic demands. In this paper, we propose a Hierarchical Multi-Agent Reinforcement Learning (HMARL) framework for online soft and safe network slicing. The proposed architecture operates at two decision levels: slice-level agents make local admission decisions, while a higher-level meta-controller coordinates inter-slice resource sharing through adaptive borrowing limits. A deterministic safety layer validates the proposed actions before resource allocation and ensures compliance with resource, continuity, and borrowing constraints. The proposed framework is evaluated against learning-based, non-sharing, random, greedy, and heuristic resource-allocation approaches. The training results show that HMARL reaches a stable operating regime with high normalized resource utilization and a low request rejection ratio. Across different scenario classes, HMARL achieves a favorable trade-off between resource utilization and slice dissatisfaction, resulting in competitive overall objective values relative to the considered alternatives. These results indicate that hierarchical coordination combined with deterministic feasibility enforcement provides an effective approach for adaptive resource management in soft network slicing environments.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119Assessing the Usability of a Comprehensive Research Management System: A Mixed-Use Study in IranAssessing the Usability of a Comprehensive Research Management System: A Mixed-Use Study in Iran23824410.66224/jist.53112.14.55.238enMehdiBeyramijamDepartment of Health in Emergency and Disaster, School of Public Health, University of Medical Sciences, Hamadan, Iran.0000-0002-1373-7808Mohammad SadeghSohrabiOccupational Health and Safety Research Center, Institute of Health Sciences and Technology, Hamadan University of Medical Sciences, Hamadan, Iran.000000023543578XMohammadBabamiriDepartment of Ergonomics, School of Public Health, Research Center for Health Sciences, Institute of Health Sciences and Technology, Hamadan University of Medical Sciences, Hamadan, Iran.0000-0002-0824-8567ArezoFarhadiDepartment of Medical Library and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. 0000-0002-3458-69062026215Usability is a key dimension of the quality of research information systems. This study aimed to examine the usability of the “Pazhuheshyar” research affairs management system and explore users’ experiences and challenges when interacting with it across several medical universities in Iran. A convergent parallel mixed‑methods design was employed. In the quantitative phase, 169 users (76 men and 93 women) completed a questionnaire evaluating the usability of web‑based software, and independent t‑tests were used to examine differences by gender and user role. In the qualitative phase, 16 semi‑structured interviews were conducted using purposive sampling and analyzed through content analysis. The mean Relative Usability score was 33.19%, indicating a low to moderate level of usability. The lowest subscale score was related to Ease of Use (23.82%), while the highest score was observed for Content/Output Quality (42.40%). Women reported higher usability scores than men across most domains, although no significant differences were found for System Terminology and Information and System Capabilities. Similarly, officers and managers reported higher scores than researchers on most scales, with no significant differences in System Terminology and Information, Personalization, and Error Management. Qualitative analysis identified five main categories: complex navigation pathways and similar menu options; system errors and data storage problems; insufficient training and user support; strengths related to documentation and national system integration; and suggested improvements such as simplifying processes, redesigning search and reporting functions, providing personalized dashboards, and developing a mobile version. Despite strengths in documentation and integration, limitations in ease of use and learnability appear to hinder optimal efficiency and user satisfaction. Implementing user‑centered workflow redesign, improving error handling and feedback mechanisms, providing structured training, and continuously monitoring user experience may enhance system adoption, user satisfaction, and productivity.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119A Hybrid Entity-Centric Approach to Persian Pronoun Resolution with BERT EnhancementA Hybrid Entity-Centric Approach to Persian Pronoun Resolution with BERT Enhancement24526710.66224/jist.49358.14.55.245enHassanHaji MohammadiDepartment of Computer Engineering, NT.C., Islamic Azad University, Tehran, IranAlirezaTalebpourDepartment of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran 0000-0003-2538-3928Ahmad Mahmoudi-AznavehCyberspace Research Institute, Shahid Beheshti University, Tehran, IranSamanehYazdaniDepartment of Computer Engineering, NT.C., Islamic Azad University, Tehran, Iran2025131Pronoun resolution, a critical aspect of coreference resolution in natural language processing, is essential for accurate text understanding and generation. This paper introduces a hybrid Persian pronoun resolution model that leverages rule-based and machine-learning approaches. Seven high-precision rule-based modules, tailored explicitly for Persian linguistic nuances, are combined with a random forest classifier enhanced with BERT contextual embeddings and cluster-level features. This approach effectively addresses the challenges of pronoun resolution, surpassing the performance of existing Persian systems on two corpora: the newly developed Mehr corpus (400 documents) and the established RCDAT corpus. Integrating BERT embeddings and cluster-level features significantly contributes to the model's superior performance, achieving a 4.68 F1-score improvement on Mehr and a 4.01 F1-score improvement on RCDAT. The proposed hybrid model demonstrates a practical and interpretable solution, contributing to Persian natural language processing research and applications.

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مرکز منطقه ای اطلاع رسانی علوم و فناوریJournal of Information Systems and Telecommunication (JIST) 2322-143714552026119Hybrid Embedding and GPT-5 Instruction-Augmented Framework for Arabic Hate Speech DetectionHybrid Embedding and GPT-5 Instruction-Augmented Framework for Arabic Hate Speech Detection26827610.66224/jist.51935.14.55.268en Anas Khudhur Abbas al jubooriComputer Engineering, Department Pardis Alborz, University of Tehran, Tehran, IranHeshaamFailiSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran , Tehran, Iran20251028Hate speech is defined as language that creates hatred, hostility, or violence towards people or groups based on their religion, ethnicity, gender, and/or nationality. As social media continues to grow and expand, the influx of hate speech and abusive language on these platforms is on the rise and has become a significant threat to online safety and social cohesion. Despite improvements in the detection of hate speech with automation technologies, there is little research conducted on the detection of hate speech in Arabic, especially regarding varying dialects. Current approaches often fail to identify language patterns or contextual nuances that are specific to different dialects. Moreover, little research has been conducted focused solely on instruction-based learning in this field. We presented a methodology for detecting abusive language and hate speech in Arabic utilizing the L-HSAB database. Through the combination of AraBERT-v2 and Multilingual MPNet, hybrid embeddings were created that have both language-specific characteristics and cross-linguistic conceptual characteristics. UMAP and HDBSCAN were applied for both dimension reduction and cluster analysis, and iterative refinement of clustering was facilitated by the utilization of Silhouette and Coherence scores. Based on the identified resulting clusters generated, GPT-5 was used to produce linguistically grounded instructional prompts, which were then assessed by both LLM-as-a-Judge using prompt generation and by human validators. Finally, the identified instructions were incorporated into further fine-tuning of the models. Experimental results indicate that the proposed method yields 90.5% accuracy for the purpose of detecting hate speech in dialects of Arabic.

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