Classification of Massive Data Sets Using a Revolutionary Grey Wolf Optimization Algorithm and a Deep Learning Model in a Cloud-Based Setting

The field of big data analytics has attracted a considerable amount of attention in the realm of academic research due to the fact that it is incredibly useful in a wide variety of real-time applications. This is the reason why the subject has received so much attention. The relatively recent development of machine learning and deep learning models has resulted in an improvement in performance. This improvement has been brought about as a result of the development of these models. The application of these models to the study of massive datasets has become much simpler as a result of these models, which has led to an improvement in performance outcomes. When considering the complexity of large data and the processing requirements that it imposes, it is beneficial to employ feature selection strategies that make use of metaheuristic optimization algorithms. This is due to the fact that big data is distinguished by the vast quantity of information that it contains. There is a huge advantage in the fact that these algorithms are able to successfully uncover the best potential set of features, which ultimately results in improved classification performance. In this particular piece of literature, a new approach that is known as the GWOA-DBN model is presented as a solution to the problem. In order to construct this model and the benefits that come along with it, the Grey Wolf Optimization algorithm and the optimal deep belief network have been coupled. The objective of the Apache Spark environment is to find a solution to the problem of classifying enormous volumes of data. This is the goal of the environment, which is the ultimate objective of the environment. The GWOA-DBN technique, which involves the construction of a feature selection method, is built on the base of the Grey Wolf Optimization Algorithm (GWOA), which acts as the cornerstone for the methodology. In the process of utilizing this strategy, the goal is to determine which subset of traits is the most ideal. The DBN-based classification model is also applied in order to appropriately categorize the large amounts of data into the precise categories that are necessary. This is done in order to fulfill the requirements. We aim to get the best possible results. The efficient processing of enormous data sets is another goal of using the Apache Spark platform. In order to provide the most optimal outcomes, this is carried out. This takes place in addition to the purpose that was specified earlier. A number of tests were carried out in an effort to improve the efficiency of the GWOA-DBN method on the whole. As a result of the results of these studies, it was proved that this method is more effective than other approaches.