Efficiency Evaluation of Scientific Research Platform Management based Improved Back Propagation Neural Network

Recently, numerous factors are required with the differentiated and innovative technique-based scientific research management. However, the innovative model is required to solve the typical software development with high effort and restricted capacity to adjust to changing needs. Hence, to evaluate the efficacy of scientific research platform, the Improved Back Propagation Neural Network (IBPNN) is developed and non-linear correlations were examined. Initially, innovation method based on platform modeling is used to systematically identify prospective consumers for engagement in platform evolution. Through strategic learning and monitoring, the client acquires data related to strategic improvement preparation and management judgements. An attractive core platform offer and an abundance of auxiliary services are made available by platform adoption. Control measures then enable the appropriate procedural incorporation of another services and applications into the platform model. In order facilitate accurate decision-making, this research recommended that weights be optimized by providing accurate predictions. According to the results, it illustrates that with a 98.45% efficiency and a 20.25% energy consumption decrease, the suggested IBPNN system outperformed the existing models such as Ecological Institutional Norms (EIN), Original Web Platform (OWP), and Dynamic Performance Management (DPM).