An efficient Draco lizard optimized stacked Bi-LSTM framework for risk mitigation and resource allocation in project management systems

Modern project management demands the mitigation of risk factors in conjunction with optimal resource utilization. Traditional approaches to risk mitigation, based on subjective assessment and supportive static tools, cannot operate effectively in a project environment filled with dynamics. Advanced analytics, artificial intelligence, and deep learning drive data-based decision-making and are able to bring forth predictive insights leading to adaptive management strategies. This paper proposed advanced deep learning approaches to improve risk reduction and optimization of resource allocation in software project management systems. First, the raw historical dataset, collected from the sources that are freely available, is gathered and preprocessed. The dataset collected contains a lot of irrelevance, which causes prediction error and computation complexity. In this regard, the pre-processing, such as one-hot encoding and normalization of data, is done, which reduces issues of data redundancy. After pre-processing, a stacked bidirectional long short-term network S-BiLSTM is proposed for the joint prediction of risks and enhancement of decision power for resource scheduling. Further, optimal resource allocation is carried out by the employment of the Draco lizard optimization DLO technique based on different parameters comprising project complexity, integration complexity, schedule pressure, team size, team experience, technology risk, and risk management maturity. The proposed framework is analyzed through a simulation on the Python platform. Various factors, such as accuracy, workload distribution analysis, resource optimization efficiency , convergence analysis, training time, and resource utilization rate, are analyzed and compared with conventional approaches to demonstrate the proposed approach’s efficiency. The proposed approach achieves a predictive accuracy of 99.2%, significantly improving the performance of the PMS in project risk identification.