Mathematical Models in Artificial Intelligence: Optimizing Algorithms for Big Data Analysis in IT Systems

Abstract

IT systems demand for big data analysis provide efficient optimal optimization of algorithms in order to scale, accuracy, etc. In this research, four key algorithms: Gradient Boosting, K-Means Clustering, Principal Component Analysis (PCA) and Markov Decision Processes (MDP) are applied in artificial intelligence to improve the data processing efficiency with a potential application. Execution time, predictive 'accuracy' and computational efficiency are used to evaluate these algorithms. Results of the experiments also imply that Gradient Boosting has outperformed traditional machine learning models in terms of predictive tasks with an accuracy of 92.5%. PC (Principal component) reduced dataset dimensionality by 45%, roughly, without significant loss of information, while Kmeans Clustering reduced data categorization time by 38 %. Moreover, MDP also enhanced the sequential decision making efficiency by 30%, compared to conventional reinforcement learning models. The advantages of using hybrid AI techniques to improve data analytics in IT infrastructure are detailed, and put into perspective with the related work. The result of this research shows that optimization techniques employed in conjunction with machine learning greatly help real-time data processing and predictive procedures. It is true that future research about quantum computing, federated learning and distributed AI frameworks could enhance big data efficiency for IT systems even more.