University of QomJournal of Engineering Management and Soft Computing3116-015812220260401Designing a multi-objective mathematical model for optimizing a shop floor flow production system, considering the number of human activities using the Gray Wolf metaheuristic algorithm118429310.22091/jemsc.2026.12210.1253ENMohammadAkhshabiCooresponding Author, Department of Industrial Engineering, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran0000-0003-3119-1105JavadArabMaster of Science in Industrial Engineering, Tonekabon Branch, Islamic Azad University, Tonekabon, IranJournal Article20250126The flow shop production system has attracted a lot of attention in research, and many researchers have conducted research to optimize flow shop production systems. The important point is to correctly assign activities to each machine in order to minimize the time to complete all activities. In the present study, a model for optimizing the number of human activities in a flow shop production system is presented, which is based on a two-objective model. The result of solving the model shows that the mathematical model has a very good ability to solve the problem in small dimensions. The gap between the results of the deterministic solution and the metaheuristic model has been reported to be zero percent. However, considering that in small and medium problems, the gap between the mathematical model and the metaheuristic model was negligible, the results presented are reliable by relying on the results calculated according to the metaheuristic model.https://jemsc.qom.ac.ir/article_4293_bb13dd7e69b5fa17f496f919d7dfc69d.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Extracting and ranking for the threats of the radio access network layer of the 5th generation mobile network base on risk analysis1955429410.22091/jemsc.2026.11966.1237ENMohammadRaghebInvited Professor of Telecommunication Engineering at Qom University of Technology, Qom, Iran, https://orcid.org/0000-0002-5982-54400000-0002-5982-5440MohammadrezaKeshavarziAssociated professor, ICT Research Institute, Iran Telecommunication Research Center (ITRC), Tehran, Iran, https://orcid.org/0000-0001-9852-54510000-0001-9852-5451BahmanMadadiPHD Student, Imam Husein university, Tehran, Iran, https://orcid.org/0000-0002-7130-88250000-0002-7130-8825Journal Article20250112The fifth generation of mobile networks (5G) offers new and advanced services such as virtual/augmented reality (AR/VR), high-quality video streaming, remote surgery, Internet of Things (IoT), and smart cars with stringent requirements. In this article, we review the 5G architecture and then focus on the Radio Access Network (RAN) architecture, and collect the threats, vulnerabilities, and security solutions provided by researchers and academic authorities in this field. In this article, we also rank RAN threats in the 5G network. To rank threats, we assign a score to each threat by introducing a risk criterion, and finally, we analyze the risk by assessing the impact/severity and probability of success of the threats on the 5G network, so that we can provide a good ranking for the threats. The results of our studies and analyses in this article inform 5G network operators which threats should be prioritized. The results of this paper will guide to secure the 5G network and deploy security solutions.https://jemsc.qom.ac.ir/article_4294_b2820abb35169e1ffb8cc33f38d2618b.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Forecasting Stock Market Volatility: A Wavelet-Enhanced Hybrid GARCH-Deep Learning Approach5674429510.22091/jemsc.2026.13545.1291ENMasoumehMolaeiSchool of Computer Engineering, Iran University of Science and Technology, Iran, Tehran.0009-0003-7674-6001HamidMoradi-KamaliSchool of Computer Engineering, Iran University of Science and Technology, Iran, Tehran0009-0009-2370-2164RezaEntezari-MalekiSchool of Computer Engineering, Iran University of Science and Technology, Iran, Tehran.0000-0003-3356-661XJournal Article20250804Accurate volatility forecasting is vital for effective decision-making in financial markets, yet remains a complex challenge due to the noisy, non-stationary nature of financial time series and the diversity of volatility definitions. This study proposes a novel hybrid framework that combines statistical, signal processing, and machine learning techniques to enhance volatility prediction. The approach begins with wavelet transformations to extract multi-scale features from raw financial data, effectively addressing non-stationarity. These features are then evaluated using multiple volatility estimators to determine their predictive relevance. The framework integrates GARCH models, wavelet-derived inputs, and deep learning architectures, with Particle Swarm Optimization (PSO) employed for optimal parameter tuning. Leveraging S&P 500 data from 2000 to 2024 and incorporating multi-source inputs, the model achieves a more holistic representation of market dynamics. Empirical results demonstrate that the hybrid method significantly reduces prediction errors and consistently outperforms baseline models and established benchmarks. To validate its practical utility, we developed trading strategies based on the predicted volatility. Backtesting results show substantial performance gains.https://jemsc.qom.ac.ir/article_4295_716a631a2a62194057eded67f3a86508.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Solving Multi-objective Optimization Problems Using the Society Deciling Process Algorithm7594429610.22091/jemsc.2026.13697.1297ENAylarPoorahadFaculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: aylar.p.2000@gmail.com0009-0007-5366-9030EinollahPiraCorresponding author, Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: pira@azaruniv.ac.ir0000-0001-9010-6113MohammadKhodizadeh-NahariFaculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: m.khodizadeh@azaruniv.ac.ir0009-0007-7416-3100SajadEsfandyariDepartment of Computer Engineering, Malayer University, Malayer, Iran. Email: sajad.a1367@gmail.com0000-0002-3971-7215Journal Article20250822Advancements in technology and the emergence of multi-objective optimization problems across various scientific domains have spurred research and development of novel metaheuristic algorithms to address these challenges. Although these methods have largely succeeded in approaching the Pareto-optimal front, the optimization process has not been fully realized. This paper introduces a multi-objective version of the Social Division Process (SDP) algorithm, termed MOSDP, aimed at improving the quality of Pareto front solutions. The MOSDP algorithm employs a memory structure as an archive to store non-dominated solutions. Additionally, it utilizes a non-dominated sorting mechanism based on crowding distance to establish a hierarchical social division structure and guide the evolutionary process in the multi-objective problem space. The performance of MOSDP is evaluated using 18 well-known multi-objective test functions, UF, and IMOP, and compared with the Multi-Objective City Councils Evolution (MOCCE), Multi-Objective Ant Lion Optimization (MOALO), Multi-Objective Slime Mould Algorithm (MOSMA), and Multi-Objective Artificial Hummingbird Algorithm (MOAHA). The results of the Friedman average rank test demonstrate the superiority of MOSDP over the aforementioned algorithms in terms of Inverted Generational Distance (IGD), Generational Distance (GD), and Maximum Spread (MS) metrics.https://jemsc.qom.ac.ir/article_4296_d1d17d37e9d635ff0087e5a05325e4a4.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Designing a smart model for granting banking facilities based on big data based on macroeconomic variables, sanctions and economic shocks95108429710.22091/jemsc.2026.14066.1307ENMasoumehVakiliDepartment of Information Technology Management, SR.C., Islamic Azad University, Tehran, IranSeyed AhmadShayanniaDepartment of Industrial Management, Fi.C., Islamic Azad University, Firoozkooh, IranMaryamRahmatyDepartment of Management, Cha.C., Islamic Azad University, Chalus, IranRezaRadfarDepartment of Industrial Management, SR.C., Islamic Azad University, Tehran, IranJournal Article20251005The granting of bank facilities and the targeting and identification of suitable customers by banks is a serious concern. The non-repayment of granted loans by customers can severely damage the profitability of banks, leading them to move towards proprietorship and economic activity, which ultimately results in increased inflation and many other economic problems. Based on the problem mentioned, the aim of the present research is to design an intelligent model for granting bank facilities based on big data, considering macroeconomic variables, sanctions, and economic shocks. To design this model, 6 macroeconomic variables, shocks, and sanctions were included in the model. The model was evaluated using four machine learning algorithms: multiple regression, support vector machine, decision tree, and random forest, based on customer data from the country's banks. Subsequently, economic growth, unemployment rate, and Gini coefficient have relatively less influence, estimated to affect loan repayment or the number of unpaid loans by 9%, 10%, and 11% respectively.https://jemsc.qom.ac.ir/article_4297_9b2d343b446f1e4b5079b409e5997f7c.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Proposing a Data Governance Model for Fraud Detection in Executive Agencies Based on Federated Learning in a Cloud Computing Environment109140429810.22091/jemsc.2026.14081.1309ENMasoomehMojtabaeePh.D. Student Department of Information Technology Management, Ki.C., Islamic Azad University , Kish, Iran, https://orcid.org/0009-0009-4675-360X0009-0009-4675-360XSeyed JavadIranbanfardAssociate Prof., Department of Management, Shi.C., Islamic Azad University, Shiraz, Iran, https://orcid.org/0000-0003-2067-43710000-0003-2067-4371SaraNajafzadehAssistant Prof., Department of computer, YI.C., Islamic Azad University, Tehran, Iran, https://orcid.org/0000-0002-9415-66090000-0002-9415-6609MostafaKolahdooziAssociate Prof., Department of Information Technology Management, SR.C. Islamic Azad University, Tehran, Iran, https://orcid.org/0009-0003-6839-42880009-0003-6839-4288Journal Article20251004With the growing volume of data interactions and increasing complexity of oversight in executive agencies, the need for innovative approaches to fraud detection and data governance has become more pressing than ever. Given that data is stored in a distributed manner, cloud computing emerges as an effective solution. However, security concerns hinder direct data exchange between organizations. To address this challenge, the present study proposes a decentralized, data-driven governance model based on federated learning and cloud infrastructure. In this model, each organization preprocesses its data at the edge and extracts fraud-related features. These results are then transmitted to a central server, where deep learning techniques are used to predict new inter-organizational fraud patterns. This approach preserves confidentiality and enables collaborative analysis without requiring data aggregation. Experimental results show that the proposed method reduces computational complexity by 60% and achieves a fraud detection accuracy of 97.6%, demonstrating its high effectiveness in multi-organizational and distributed environments.https://jemsc.qom.ac.ir/article_4298_ff80c9606fb7fc3a3c056fea14883f50.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Clustering and Ranking of Provinces in Terms of Investment Security Based on Multi-Criteria Multi-Period Decision-Making141163429910.22091/jemsc.2026.14237.1314ENAliGanjiMSc., Faculty of Industrial Engineering, Department of Industrial Engineering, SR.C., Islamic Azad University, Science and Research Branch, Tehran, Iran
Email: Aliganji201@gmail.com / https://orcid.org/0009-0007-9873-99870009-0007-9873-9987AliJahanCorresponding Author, Associate Prof., Faculty of Industrial Engineering, Department of Industrial Engineering, Se.C., Islamic Azad University, Semnan, Iran
Email: Alijahan@iau.ir / Iranalijahan@yahoo.com / https://orcid.org/0000-0001-6347-16760000-0001-6347-1676Journal Article20251021Investment security is a critical component of economic development in countries, as its enhancement fosters investor confidence and encourages participation in productive sectors and financial markets. Given the regional disparities across the country, assessing investment security at the provincial level is essential. This study aims to rank and cluster Iranian provinces in terms of investment security over the period 2021–2023, using a multi-criteria, multi-period decision-making approach. For this purpose, two techniques—MP-TOPSIS and MULTI-MOORA—were employed to evaluate and rank the provinces, and the results were compared. Subsequently, the k-means clustering method was applied to group provinces into homogeneous clusters. The data were obtained from 12 policy reports published by the Iranian Parliament Research Center, and the weighting of criteria was performed using three methods: MEREC, Shannon entropy, and CRITIC. The findings reveal that Semnan, Golestan, and Hormozgan provinces exhibit the highest levels of investment security, while Tehran, Sistan and Baluchestan, and Kohgiluyeh and Boyer-Ahmad rank lowest. Additionally, the correlation between the two ranking methods was estimated at 87%, indicating a high degree of consistency and validating the proposed model. The results suggest that macroeconomic stability, administrative transparency, and robust legal frameworks are the most influential factors in determining investment security. Region-specific policy recommendations based on these analyses can significantly improve the investment climate across the countryhttps://jemsc.qom.ac.ir/article_4299_28c08bb28e09abcf0a43cda784e0a1c9.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Multi objective -Multi-level Scheduling in Cloud Manufacturing: A Hybrid Approach Integrating Mathematical Modeling and Machine Learning164183430010.22091/jemsc.2026.14343.1319ENMohammadrezaRazdarDepartment of Industrial Engineering, Qa.C., Islamic Azad University, Qazvin, IranMohammad AminAdibiDepartment of Industrial Engineering, Qa.C., Islamic Azad University, Qazvin, Iran0000-0002-4489-1330HassanHalehDepartment of Industrial Engineering, Golpayegan Isfahan University of Technology, Golpayegan, Iran0000-0001-9872-3116Journal Article20251029This research investigates multi-objective, multi-level scheduling within a cloud manufacturing environment, employing metaheuristic algorithms. Initially, a comprehensive literature review was conducted, followed by the development of a multi-objective mathematical model integrated with a machine learning (ML) model. The model’s validity was first assessed by solving it for small-scale instances. Given that the exact method was only feasible up to the tenth instance, metaheuristic algorithms were utilized for solving the model in larger dimensions. The results demonstrated the model’s solvability in large-scale scenarios using the NSGAII algorithm. Subsequently, the model was solved considering risk input values, revealing that among the temporal parameters, transportation time and setup time exhibit the most significant impact on overall time. Among the cost-related parameters, preparation cost has the greatest effect on time, with transportation cost being the next most significant. Crucially, activity time emerges as the most impactful cost parameter, with transportation cost following as the second most influential.https://jemsc.qom.ac.ir/article_4300_f48bde2485f2d010ec35b58e985b2f5b.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Maximizing the flow of used goods by designing a reverse logistics network using meta-heuristic methods184203430110.22091/jemsc.2026.14496.1324ENGholam HassanShirdelDepartment of Computer Sciences, University of Qom, Qom, IRAN.0000-0003-2759-4606AliFadhilDepartment of Mathematics, University of Qom, Qom, IRAN.0000-0001-7341-2258Journal Article20251112In supply chains, the goal is usually to meet demand at the lowest cost. But there are cases where either the transportation costs are insignificant or, such as in critical situations, the supply of more goods has a much higher priority than the costs. In such cases, instead of minimizing the cost, we should maximize the transfer flow values. In this case, the supply chain network minimization problem (minimum cost flow) becomes a type of flow maximization problem (maximum flow). In this paper, we have addressed a type of flow maximization problem in supply chains. First, we have defined and modeled it, then, considering its complex structure, we have obtained a suitable approximate solution for it by using a meta-heuristic method.https://jemsc.qom.ac.ir/article_4301_1b201147bcf3c89ecf52fea1ba997dff.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Dual-branch hybrid-feature learning for imbalance-aware paediatric colonoscopy decision support with interpretable multi-scale networks204224430210.22091/jemsc.2026.14688.1327ENKhosroRezaeeDepartment of Biomedical Engineering, Meybod University, Meybod, Iran0000-0001-6763-6626Journal Article20251127Colorectal neoplasia, while rare, is high-risk in children. As such, colonoscopic detection of paediatric polyps is of clinical importance. Current deep learning–based computer-aided systems are trained on adult data and do not provide solutions for class imbalance, cross-centre generalisation or interpretability in paediatrics. We introduce an imbalance-aware dual-branch hybrid-features (IDHF) framework for polyp versus non-polyp classification. IDHF, based on a ResNet-50 backbone, is equipped with complementary texture branch to augment deep semantic features, adaptive gating for late fusion and optimisation of a class-weighted loss function with false-negative penalty and branch-agreement regularisation. The proposed model is trained on CP-CHILD-A (8,000 images, 1,000 polyp/7,000 non-polyp) and tested without fine-tuning on CP-CHILD-B (1,500 images, 400 polyp/1,100 non-polyp) on another platform. ResNet-50+IDHF achieves 99.6% accuracy, 100.0% sensitivity, 99.5% specificity, 96.8% precision and a 98.4% F1-score on CP-CHILD-A. ResNet-50+IDHF achieves 99.5% accuracy, 99.2% sensitivity, 99.6% specificity, 99.0% precision and a 99.1% F1-score on CP-CHILD-B, supporting a robust and interpretable solution for computer-aided paediatric polyp detection.https://jemsc.qom.ac.ir/article_4302_59b297a90d6f3229de6845085ada6eeb.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Intelligent Robust Adaptive Inverse Dynamical Controller for Nonlinear Dynamics of Vehicle Systems225241430310.22091/jemsc.2026.14801.1331ENAlirezaBorjaliDepartment of Electrical Engineering, University of Qom, Qom, IRAN. Aborjali1370@gmail.comRezaGhasemiCorresponding Author Department of Electrical Engineering, University of Qom, Qom, IRAN. r.ghasemi@qom.ac.ir, reghasemi@gmail.com0000-0002-0503-6139Muhammad AminRezaeiDepartment of Electrical Engineering, University of Qom, Qom, IRAN. Marezaei1380@gmail.comJournal Article20251208Compared to the other research that focuses on the intelligent identification of nonlinear systems, the deliberated methodology evolved the inverse intelligent process as a universal controller for a class of nonlinear systems and this method has achieved a remarkably low root mean square (RMS) and tracking error rate. In the vehicle systems, the tracking of the predefined desired path is challenging to achieve, especially in presence of disturbances. The planned AIDS procedure consists of an online inverse model identifier updated using the back-propagation (BP) algorithm. In this approach, an offline identification phase provides the initial network weights. A Multilayer Perceptron (MLP) is then employed as a nonlinear controller, trained to represent the system's inverse dynamics and applied to the vehicle model. The convergence of the noisy states to the nominal ones, the robustness of the recommended designing system, and the reduction of noisy phenomena effect in the system's state are all crucial advantages of the planned AIDC. Simulation results demonstrate the promising performance of the proposed methodology.https://jemsc.qom.ac.ir/article_4303_bd8ad8eca259480230098557256d5a94.pdfUniversity of QomJournal of Engineering Management and Soft Computing3116-015812220260401Scenario-Based Robust Decision-Making in Health Tourism Service Supply Chains242259430410.22091/jemsc.2026.14971.1334ENAmirhosseinSaffariniaDepartment of Management and Accounting, College of Farabi, University of Tehran, Iran0009-0005-9523-7354Mohammad RezaFathiAssociate Professor, Department of Management and Accounting, College of Farabi, University of Tehran, Iran0000-0002-7973-9814Mohammad JavadPahlevanzadehDepartment of Management and Accounting, College of Farabi, University of Tehran, Iran0000-0003-4203-2644SamanehRaeesi NafchiAssistant Professor of Industrial Management, Shiraz University of Technology-Lamerd Higher Education Center, Fars, Iran0000-0003-3412-037XJournal Article20251223Health tourism is a dynamic sector of Iran’s economy with strong potential in the religious city of Qom. This study develops plausible future scenarios for the health tourism service supply chain and identifies suitable strategies using Robustness Analysis (RA). A mixed qualitative–quantitative design is employed. First, key influencing factors are identified through expert interviews and a literature review. Next, alternative scenarios are constructed and evaluated using a weighted decision-matrix that simultaneously accounts for system complexity and environmental uncertainty in a participatory decision-making setting. The analysis yields seven distinct scenarios derived from eight key factors, ranging from an ideal future characterized by sustainable development and attraction of religious markets to a pessimistic future marked by sanctions-driven stagnation and multiple crises. The RA results indicate that vertical integration and market development perform best under favorable conditions, whereas retrenchment and heterogeneous diversification are more effective under crisis conditions. The proposed weighted RA decision matrix provides a practical decision-support tool for policymakers and healthcare managers in Qom to select resilient strategies across diverse environmental futures.https://jemsc.qom.ac.ir/article_4304_7429dc90d446bec16d15627ec42ab675.pdf