Designing a Self-Assessment System Based on the Sermon of Hammam and Analysing the Interrelations Between Its Various Sections
This article presents and designs a confidential self-assessment system based on the "Sermon of Hammam" (Khutbah al-Hammām), aimed at measuring individuals' alignment with the characteristics of the God-conscious (muttaqin) as described in the sermon. The system utilizes a comprehensive questionnaire composed of statements focused on moral, spiritual, and behavioral traits, allowing users to evaluate themselves based on religious and faith-based criteria. Users rate themselves on statements related to behaviors such as honesty, humility, avoidance of extravagance, and night worship using a scale from 1 (never) to 5 (always). The system’s methodology consists of three main stages, the first one is data collection and assessment. A questionnaire made up of specific statements—such as avoiding excess and speaking kindly—is developed, and participants provide quantitative responses. Each person’s score is calculated by summing the points from the questionnaire items. The second one is results analysis. Each individual’s total score is compared against the maximum possible score (200), and the percentage of alignment with the sermon’s standards is calculated. The results are presented to the user as a natural number between 0 and 100. The lowest-scoring behaviors or traits—those needing improvement—are highlighted and accompanied by related sayings from different resources to encourage reflection and provide guidance. The third one is providing improvement suggestions. If low scores are identified, suggestions for improvement or deeper reflection on the mentioned traits are offered to the user. This process supports the enhancement of users' moral and spiritual excellence. Finally, by collecting responses and calculating the correlation coefficient, the strongest relationship was found between questions related to worship and those related to piety. This project is developed with the aim of encouraging individuals to improve their spiritual traits and offers practical methods for personal development. It can serve as a valuable tool for the community.
An interpretive structural model of factors affecting the phenomenon of investors' mental anchoring in the capital market
The purpose of this research was to design an interpretive structural model of the factors affecting the phenomenon of investors' mental anchoring in the capital market. This research is a survey in terms of its developmental purpose, a survey in terms of its data collection method, and a cross-sectional study in terms of its time. The model was presented based on a mixed method with a quantitative and qualitative approach. The statistical population included academic and capital market experts, of whom 15 were interviewed as a statistical sample until the theoretical saturation of the interview. In the qualitative stage, the results of the semi-structured interviews were analyzed using the content analysis method. The content analysis of the interviews with the experts led to the identification of 18 factors affecting the phenomenon of investors' mental anchoring, which were: initial experiences, information and analysis, the influence of the media, psychological factors, economic factors, social factors, market transparency, access to information, laws and regulations, investment strategies, risk management, market analysis, investment experience, economic situation, social influence, profitability, investor behavior trends, and self-confidence. The results of fitting the interpretive structural model of these factors showed that these factors can be categorized into 6 levels in terms of influence and impact. Psychological factors, investment strategies, profitability, and self-confidence have been identified as the most influential factors, while economic factors, social factors, laws and regulations, and economic situation have been identified as the most influential factors on investors' mental anchor.
Evaluating The Efficiency of Iranian Listed Companies Using a Combined Method of Data Envelopment Analysis and Machine Learning
This paper examines the efficiency of Iranian listed companies using data envelopment analysis and machine learning algorithms. First, the efficiency scores of 130 listed companies in the period from 2007 to 2013 were calculated using data envelopment analysis. Input variables included total debt and RETRSIK and output variables included annual stock return and liquidity. Then, the machine learning algorithm was used to identify and evaluate critical variables in predicting company performance. The results showed that the XGBoost algorithm had superior prediction performance. This study shows that the combination of data envelopment analysis and machine learning methods can be an effective tool for analyzing and predicting the efficiency of listed companies and help investors and managers make decisions. This research emphasizes the role of modern data analysis methods in improving economic and financial decision-making.
Identifying and evaluating the challenges of the central bank's comprehensive supervision using multi-criteria decision-making methods
Identifying and evaluating the challenges of comprehensive supervision by the central bank refers to processes in which the country's central bank seeks to identify the problems and weaknesses present in the supervisory and financial system, as well as to assess the impact of these challenges on the performance and stability of the banking and financial system. Overall, identifying and assessing supervisory challenges is of significant importance for the central bank, as it aids in maintaining financial stability and preventing crises, ultimately safeguarding the interests of depositors and enhancing the credibility of the nation's financial system. The objective of this research is to identify and evaluate the challenges of comprehensive supervision by the central bank. In this study, 10 experts in the fields of banking and economics identified 15 key challenges and subsequently responded to a questionnaire designed for this purpose. The results of the analyses conducted using the DEMATEL method indicated that among the identified challenges, the lack of transparency in the central bank's operations and legal constraints for more precise supervision were recognized as the most influential challenges. These challenges play a crucial role in the quality of supervision and the effectiveness of the central bank, and addressing them could contribute significantly to improving the performance and credibility of the country's banking system. The findings of this research may provide valuable insights for policymakers in designing appropriate strategies to strengthen supervision within the financial system.
Performance Analysis of Capital Development Funds in Lorestan Province by COFOG Divisions and Groups Using Data Envelopment Analysis
Government investment in development projects is a primary mechanism for creating new production and service capacities. However, these projects often face challenges, including budget deficits and incomplete funding allocation. Given the competition between current expenditures and development credits for public funds, employing efficient management mechanisms for development projects is crucial. Optimization methods and efficiency analysis models, such as Data Envelopment Analysis (DEA), can be valuable tools. DEA is a non-parametric method used to evaluate the efficiency of homogeneous decision-making units (DMUs) based on multiple inputs and outputs. This study examines the performance of capital investment credits in Lorestan Province over six years, categorized by COFOG divisions and groups, using the Fair DEA (KA) model. The results show consistent rankings for 2021, 2020, and 2018, while other years exhibit differences. A comparative analysis with other provinces using the CCR and CSW models reveals that Lorestan’s efficiency was below the national average in 2017 and 2018 but improved from 2019 to 2021, surpassing the national average.
Development and Evaluation of a Hybrid Model for Predicting the Maturity of Intellectual Capital Based on Artificial Neural Network and Genetic and Firefly Algorithms
In today’s rapidly evolving innovation landscape, managing intellectual assets and assessing their maturity plays a crucial role in gaining competitive advantage, creating value, and achieving organizational success. Therefore, this research aims to present a predictive model for the maturity of intellectual capital in knowledge-based firms located in industrial parks. This model employs a hybrid approach that integrates Multi-Layer Perceptron (MLP) artificial neural networks with genetic algorithms and firefly algorithms. The research is applied-developmental in nature, descriptive-modeling in methodology, and mixed-methods (qualitative and quantitative) in data type. The qualitative sample consisted of 12 experts and specialists selected through snowball sampling, while the quantitative segment included 212 knowledge-based companies chosen using stratified random sampling. Data collection tools included a review of scientific literature, specialized interviews, and standardized questionnaires. After assessing the validity and reliability of the questionnaires, the data were analyzed using the Delphi method, confirmatory factor analysis, MLP neural networks, and their combinations with genetic algorithms and firefly algorithms. SPSS, PLS, and Python software were utilized for this purpose. The results indicated that all models examined were capable of predicting the maturity level of intellectual capital. However, the hybrid model combining neural networks with the firefly algorithm demonstrated significantly better and more accurate performance in predicting intellectual capital maturity levels, achieving evaluation metrics of 95.35% accuracy, 94.35% precision, 95.35% recall, 94.41% F1-score, and an AUC of 0.996.
On Graph-Based Cryptography in Dynamical Systems
For the purpose of data storage and transmission, constrained sets are required. Thus, to convert sequences from a full shift space to a sofic shift, certain constraints must be applied. One method to address this issue is the use of finite-type codes. An (X, n)-finite-type code can be used to transform sequences from a full n-shift into sequences from the shift space X. If X is a sofic space with entropy at least equal to the logarithm of n, then an (X, n)-finite-type code exists.
Identifying and Ranking Performance Evaluation Criteria of Retail Warehouses Using the Fuzzy Best-Worst Method
Efficient management of retail warehouses is associated with multiple challenges, and identifying key criteria for evaluating the performance of these warehouses is of particular importance. The primary research question in this study is: which criteria are more important for evaluating the performance of retail warehouses? In this study, performance-related criteria were initially identified and extracted through a comprehensive review of the literature and analysis of previous research. Subsequently, these criteria were evaluated using the fuzzy Delphi method and expert opinions in the field. In the next step, the validated criteria were ranked and prioritized using the fuzzy Best-Worst Method (FBWM) to determine the relative importance of each criterion. The results of the study indicated that inventory accuracy and transfer time are of high importance and have a direct impact on warehouse productivity and cost reduction. Additionally, labor productivity, picking time, stockout cost, and transfer quality were among other important factors, highlighting the role of human efficiency in improving warehouse performance. Criteria such as transportation cost, safety, and compliance with standards were considered less important compared to others. These findings are consistent with many previous studies and emphasize that accurate inventory management and optimization of warehouse processes are essential for enhancing efficiency. These results can assist managers in making better decisions aimed at improving warehouse performance and increasing customer satisfaction.
About the Journal
About Us
The Decision Science and Intelligent Systems is a leading scientific journal dedicated to advancing the fields of decision science and intelligence systems by the help of operations research (OR), data envelopment analysis (DEA), and mathematical modeling. We publish original research and review articles that contribute to the theoretical and practical understanding of these disciplines.
Our Mission
Our mission is to provide a platform for researchers and practitioners to disseminate their cutting-edge findings and foster collaboration within the scientific community. We aim to promote innovation, rigor, and the application of scientific methodologies to solve real-world problems.
Scope of Coverage
The journal covers a wide range of topics, including:
- Decision analysis and optimization
- Machine learning and artificial intelligence
- Operations management and logistics
- Data envelopment analysis and performance evaluation
- Mathematical modeling and simulation
- Applications in various domains, such as healthcare, finance, and sustainability
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The journal is targeted at researchers, academics, industry professionals, and policymakers interested in the latest advances and applications of decision science and intelligence systems.