Deep Learning Prediction and Optimization for Management of Cloud-Based Services in Enterprises

Abstract

Cloud-based services in enterprises refer to the use of remote servers hosted on the internet to store, manage, and process data, rather than relying on local servers or personal computers. Enterprise Cooperation Projects (SECPs) play a vital role in bridging the gap between academic theory and practical industry applications, fostering meaningful collaborations between educational institutions and businesses. This paper proposes a novel approach to enhance the prediction and optimization of SECPs using the Particle SeaHorse Optimization Decision Tree (PSODT) algorithm. The PSODT algorithm combines the adaptive optimization capabilities of Particle Swarm Optimization (PSO) with the decision-making power of Decision Trees to effectively classify project outcomes and predict performance indicators. Through a series of experiments and analyses, we evaluate the algorithm’s classification accuracy, precision, recall, and computational efficiency. The results demonstrate that the PSODT algorithm achieves high accuracy in predicting project outcomes, with precision, recall, and F1 score values consistently exceeding 96%. Specifically, the accuracy ranges from 96.8% to 97.5%, precision from 96.8% to 97.8%, recall from 96.5% to 97.5%, and F1 score from 96.8% to 97.5%. Furthermore, the algorithm balances predictive accuracy with computational efficiency, with computational times ranging from 120 to 150 seconds across experiments.