An Artificial Neural Network Based Framework for IT Project Risk Management

  • Tariq Abdullah, Malaya Nayak


The development of information technology software projects are subjected to higher rates of failures. The control of risks in information technology projects will produce higher investment returns. In order to analyze as well as regulate various project threats, the establishment for a smart risk assessment framework is beneficial. It improves the traditional project management systems as well as associated protocols that are available today by offering an autonomous process for verifying predicted outcomes. This allows for the contrast that poses solutions according to each system with relevant questions and empirical data related to threats defined within major information technology projects. Current methods provide us with an assessment process to determine the likelihood for the outcomes of information technology project's targets even before the project has been initiated. In this paper we used a method to the implementation of a risk assessment paradigm for information technology  projects using the Artificial Neural Networks (ANN) strategy. An example of an effective means of addressing such problems is given through the use of ANN as a quantitative method (e.g., the correlation / likelihood assessments). In other terms, we demonstrate how pre and post likelihood predictions through ANNs will eventually be extracted using empirical data. The method suggested in this paper were verified by computer modeling for the reliability of framework. In addition, this paper showed how project management and risk mitigation parameters, like time and costs, effect the progression and outcomes of an information technology project development. ANNs were selected as these models imparts statistical evaluation that reveal hidden relationships to the input data. The major benefits of using ANNs are versatility in regards to interpretation including resilience on the sample dataset. It was found that this tool was able to produce appropriate measures by exploiting both pre project and post project data.

How to Cite
Malaya Nayak, T. A. (2020). An Artificial Neural Network Based Framework for IT Project Risk Management. International Journal of Advanced Science and Technology, 29(3), 4876- 4883. Retrieved from