Empirical Compression Features of Mobile Computing and Data Applications Using Deep Neural Networks

Abstract

Due to the enormous data sizes involved in mobile computing and multimedia data transfer, it is possible that more data traffic may be generated, necessitating the use of data compression. As a result, this paper investigates how mobile computing data are compressed under all transmission scenarios. The suggested approach integrates deep neural networks (DNN) at high weighting functionalities for compression modes. The proposed method employs appropriate data loading and precise compression ratios for successful data compression. The accuracy of multimedia data that must be conveyed to various users is higher even though compression ratios are higher. The same data are transferred at significantly higher compression ratios, which save time while also minimizing data mistakes that may occur at the receiver. The DNN process also includes a visible parameter for handling high data-weight situations. The visible parameter optimizes the data results, allowing simulation tools to readily observe the compressed data. A comparison case study was created for five different scenarios in order to confirm the results, and it shows that the suggested strategy is significantly more effective than existing methods in roughly 63 percent of the cases.