Digital transformation in banking has significantly improved efficiency, including in the critical Letter of Credit (LC) examination area. Despite these advancements, LC examination remains complex, labor-intensive, and error-prone, leading to operational risks and inefficiencies. Integrating...

Supplier quality evaluation in industrial sectors is well-recognized due to its direct impact on quality assurance and improvement. This task is challenging due to the need to process extensive qualitative and quantitative data, multi-dimensional attributes, and numerous suppliers. Traditional...

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities,...

The transition towards energy-efficient practices in the food supply chain (FSC) is essential for addressing the dual imperatives of sustainability and cost-effectiveness. As consumers become increasingly aware of the environmental impact of their food choices, their willingness to support...

Accurately detecting anomalous points in time-series data is critical, as false positives can mislead business stakeholders, waste valuable resources, and diminish the overall impact of the detection system. While various statistical and machine learning techniques are employed to flag potential...

Interest in predicting sports match outcomes has grown significantly, driven by advancements in machine learning techniques and widespread adoption. However, the utilization of these predictive models in enhancing tactical team performance remains relatively limited. We propose a methodology...

The increasing use of intelligent data processing and its capacity to handle vast data sets enhance efficiency and effectiveness in production management. Consequently, machine learning models have become essential for decision-making in this domain. Previous literature reviews have not...

Cyber threat detection is a critical challenge in cybersecurity, with numerous existing solutions relying on rule-based systems, supervised learning models, and entropy-based anomaly detection. However, rule-based methods are often limited by their dependence on predefined signatures, making...

The academic world is becoming increasingly interested in the applications of Artificial Intelligence technology in education. A systematic review examines AI applications in education, focusing on their effectiveness, challenges, and implications. A comprehensive analysis of studies published...

The evolution of Industry 5.0, along with its predecessor Industry 4.0, has significantly boosted the adoption of predictive maintenance through integrating Internet of Things (IoT) sensors and real-time big data analysis, enabling the identification and prevention of equipment failures. This...