International Journal of Computer (IJC)

Leadership and Mentorship Models in Software Quality Management

2025-05-08T16:00:41+00:00

This paper explores the synergy between leadership and mentorship models in driving improvements in software quality. Through a literature review, the study identifies that transformational and situational leadership—when combined with proactive mentorship practices—significantly enhance the effectiveness of quality assurance systems in IT projects. The structured Six Sigma methodology supports this process by reducing defects, optimizing development workflows, and enabling continuous improvement. The findings underscore that integrating managerial practices with the DMAIC framework serves as an effective means of cultivating a corporate culture centered on innovation and quality. Such integration is particularly vital for boosting competitiveness in the software development industry. The article offers valuable insights for both academic researchers and IT management professionals, as well as software quality experts aiming to embed modern leadership and mentorship theories into strategic management models to improve development and quality control outcomes. The paper’s analytical approach not only contrasts different leadership frameworks but also identifies optimal paths for implementing mentorship practices, thereby contributing to the advancement of management processes in the context of digital transformation.

Analyzing the Performance of ECLAT Algorithm for Large Datasets by Comparing K-means and Gaussian Mixture Model

2025-01-31T12:53:50+00:00

Frequent Itemset Mining (FIM) is a technique that transforms historical data into useful information by identifying beneficial patterns. The ECLAT method uses depth-first search to intersect the transaction ID sets with the corresponding k^thitem sets in order to calculate the support items. While searching for the best-selling products, ECLAT uses a lot of memory and processing time due to the enormous number of transaction ID sets. To overcome these problems, the clustering method combines with the ECLAT algorithm to retrieve the support items. Description elements 100,000 to 400,000 were used to retrieve the support items of the most popular selling goods. For the K-means clustering approach, the optimal value of k is 8 clusters according to the 0.59 silhouette value. For the Gaussian Mixture Model, the ideal value of k is 14 clusters based on a 0.59 silhouette score value between 100,000 and 400,000 data items. After clustering the same product items, the ECLAT algorithm retrieves the support items by applying a minimum support value of 0.00001 in this investigation. According to the experimental results, the Gaussian Mixture Model not only offers more flexibility for clustering the same items but also reduces the memory usage and execution times. The outcomes of this investigation indicate that the Gaussian Mixture Model provides more efficient enhancement of the performance of the ECLAT algorithm than the K-means algorithm.

A Method for Identifying and Assessing Phishing Attacks in Communication Messages

2025-04-28T20:48:28+00:00

Phishing attacks have become a significant threat in online communication platforms. These attacks exploit human vulnerabilities by using deceptive messages to steal sensitive information or distribute malicious content. This paper presents a comprehensive phishing detection system, leveraging machine learning and multi-layered analysis of URLs, files, and message content. The proposed system integrates URL analysis, file analysis, and text analysis services to identify potential threats effectively. Experimental results demonstrate the efficacy of the approach, achieving high accuracy in detecting phishing attempts. This research contributes to the field of cybersecurity by providing a robust framework for identifying and mitigating phishing risks in real-time communication.