۱.
This paper investigates the application of Process Mining (PM) techniques to redesign and optimize logistics processes within an Iranian bank. The primary aim is to identify inefficiencies, bottlenecks, and process deviations using real-world event log data and to provide data-driven recommendations for process improvement. Data comprising 35,642 event reports related to 16,490 logistics process workflows were extracted from the bank's automation and correspondence systems over six months in 2022. Disco 2.14 was used for data analysis. Results revealed that only 3.6% of product demands conformed to the predefined process model, indicating high process variability and improvement potential. Analyses also showed the average process duration was 5.7 days, exceeding the bank's internal benchmark (three to five days), and the process fulfillment ratio was 83.3%, falling short of the desired target of 95%. Key inefficiencies identified included excessive waiting times for unfulfilled demands (averaging 315.7 days) and bottlenecks in the "Registering the purchase invoice" and "Registering the warehouse receipt" activities. Drawing on these findings, suggestions were proposed to optimize the procurement process, automate manual efforts, and improve alignment with the defined process model. This study contributes to the existing knowledge by providing an empirical case study of PM application in a specific context within the banking industry. The findings underscore the importance of monitoring and managing process conformance, as well as addressing excessive waiting times to improve customer satisfaction and operational efficiency. Limitations of this study include reliance on data from a single bank and a focus on logistics processes. Future research could focus on investigating root causes of process deviations, using PM for predictive analysis, and evaluating the impact of process improvements on key performance indicators.
۲.
The cement industry plays a crucial role in construction and infrastructure development, carrying significant economic implications. While demand in developed countries has declined due to environmental concerns, Iran remains self-sufficient and a leading exporter in the Middle East. The Fourth Industrial Revolution has introduced the Internet of Things (IoT) as a transformative technology, delivering economic, environmental, and social benefits. Although historically resistant to change, the cement industry has recently begun adopting IoT technologies, demonstrating notable progress in recent years. This study aims to identify and prioritize IoT applications within the sustainable cement industry in Iran. The research began by identifying IoT applications through a review of leading industry practices and academic literature. A sustainability-based framework was developed to evaluate these applications across economic, environmental, and social dimensions. The Best-Worst Method (BWM) was used to weight sustainability indicators, and the VIKOR method was applied to assess the relative attractiveness of each application. Capability indicators were also evaluated. A capability–attractiveness matrix was constructed to score and prioritize the applications accordingly. The study identified 13 relevant IoT applications for the cement industry. A set of 17 attractiveness indicators (grouped into economic, social, and environmental dimensions) and eight capability indicators (based on IoT architecture layers) were used in the evaluation. The applications were assessed using a capability–attractiveness matrix, and “Gas Monitoring” and “Temperature Measurement and Monitoring” were found to have the highest priority, indicating strong feasibility and strategic value for sustainable implementation.
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This article examines blockchain research in accounting, auditing, and corporate governance (AAG), a field that has experienced rapid growth but remains fragmented. Using Latent Dirichlet Allocation (LDA) topic modeling, the study analyzes 486 Scopus-indexed abstracts published between 2017 and 2025 to uncover key themes and research trends. The analysis identifies eleven thematic clusters: Blockchain Research Landscape & Governance, Financial Reporting & Digital Security, Blockchain Applications in Diverse Domains, Auditing Practices & Taxation, Bitcoin & Emerging Digital Financial Tools, Corporate Governance & Compliance, Smart Contracts & Crypto Audits, Digital Transformation in Finance, ESG & Corporate Strategy, Supply Chain Transparency, and Adoption of Audit Technologies by Firms. A strategic thematic map further classifies these into motor, basic, niche, and emerging themes, providing the first data-driven overview of blockchain research in AAG. The results highlight well-developed areas such as supply chain transparency, alongside blind spots in standard-setting, assurance of smart contracts, and ESG integration. The study advances understanding by offering a structured framework that supports future research, regulatory development, and professional practice.
۴.
Internal Financial Control (IFC) is a critical component of corporate governance, ensuring the accuracy, reliability, and compliance of financial reporting. Traditional IFC systems rely on manual audits, centralized databases, and rule-based checks, which are often inefficient, prone to human error, and vulnerable to fraud. The integration of Blockchain Technology and Machine Learning (ML) has introduced transformative improvements in Internal Financial Control (IFC) systems. This paper explores how Blockchain and machine learning (ML) technologies can strengthen internal financial controls (IFC). By addressing limitations in traditional systems, these technologies introduce transparency, automation, and predictive capability, fostering enhanced compliance and reduced risk. The integration of these technologies offers a paradigm shift for governance, risk management, and auditing practices, enhances fraud detection and regulatory compliance, while addressing challenges such as scalability and data privacy. Through a synthesis of academic literature and industry case studies, Blockchain ensures immutable transaction records, while ML enables predictive anomaly detection. Blockchain and ML are transforming internal financial control by enhancing security, automation, and predictive capabilities. There are still challenges in overcoming scalability, interpretability, Hybrid Blockchain-ML frameworks, and regulatory challenges for widespread adoption.
۵.
Enterprise networks, as the backbone of modern information systems, are increasingly exposed to sophisticated and rapidly evolving cyber threats. Traditional Intrusion Detection Systems (IDS), based on static attack signatures, often fail to detect novel or complex intrusions, resulting in high false alarm rates. This study proposes an intelligent IDS that leverages Machine Learning and Deep Learning techniques to significantly improve detection accuracy and reduce alert noise. The system is capable of classifying attacks by severity and provides an intuitive interface to support efficient threat monitoring. Beyond technical performance, the solution addresses managerial objectives by lowering maintenance costs, enhancing service quality, accelerating incident response, and ensuring high availability with straightforward deployment. The proposed model offers a scalable and resilient IDS tailored for enterprise environments, contributing both practical and strategic value in the fight against increasingly sophisticated cyberattacks.
۶.
Deep learning models have become indispensable across scientific and business domains, offering new approaches to problem-solving but requiring substantial technical expertise for their implementation. This article presents StudySupport, an open-source visual system for configuring and training machine learning models via a graphical interface rather than traditional coding. The system enables users to manage the entire pipeline - from data preprocessing and model construction to optimization and performance evaluation - while maintaining flexibility for advanced customization. By lowering the technical entry barrier, the StudySupport system facilitates the adoption of machine learning in IT management and organizational decision-making. The proposed framework supports faster integration of data-driven methods into enterprise information systems, reduces implementation costs, and empowers managers, analysts, and educators to leverage artificial intelligence in digital transformation processes. The study contributes to the field of information technology management by bridging the gap between advanced machine learning techniques and their practical application in business, education, and decision-support systems.
