In the context of rapid digital transformation, information technology (IT) projects have become a fundamental component of organizational development strategies. However, the successful implementation of such projects is often challenged by various technical risks and issues that may arise at different stages of the project lifecycle. The purpose of this study is to systematically analyze the sources of technical risks in IT project management, evaluate their impact on key project performance indicators, and identify effective mechanisms for their mitigation. The research is based on a system-oriented approach combined with comparative analysis and risk structuring methods. The findings reveal that the primary sources of technical risks include inadequate requirements definition, suboptimal architectural decisions, integration inconsistencies, and insufficient testing processes. Furthermore, the study demonstrates that the application of modern approaches such as DevOps practices, continuous integration and delivery (CI/CD), and automated testing significantly contributes to minimizing technical risks and improving project outcomes. The results of this research provide both theoretical insights and practical recommendations for enhancing decision-making processes and ensuring more effective management of IT projects.
Shukufa Rustamova, Isa Muradov· Caspian journal of applied m...· 0 citations
As a result of the digitalization of education systems, the collection of large amounts of academic and behavioral data has expanded the possibilities of predicting students’ academic performance. Early identification of the risk of academic failure or dropout is of great importance in terms of optimizing decision-making mechanisms in higher education institutions. In this research, an adaptive artificial intelligence-based model is proposed for predicting students’ academic outcomes. The model is built on the basis of various features demographic indicators, attendance, current assessment results, and behavioral indicators. In the framework of the study, supervised machine learning algorithms such as Random Forest, Support Vector Machine, and Gradient Boosting were comparatively analyzed and an ensemble approach was applied. Experimental results showed that the proposed adaptive model demonstrates higher prediction accuracy compared to individual models and achieves results higher than 91% on test data. Feature importance analysis showed that attendance and midterm grades collected during the semester are the main predictive factors. The results of the study are of practical importance for the development of early intervention strategies and the optimization of academic management in higher education institutions.
Isa Muradov, Sona Safaraliyeva· Caspian journal of applied m...· 0 citations