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.

A model study of teaching method reform of computer laboratory course integrating internet of things technology

“…Comparison experiments of TFREC algorithm with YOLOv3 [ 34 ], YOLOv5 [ 35 ], and AdaBoost [ 36 ], algorithm yielded a table of detection rates as shown in Table 6 , and a graph of comparison of detection performance as shown in Fig 6 .…”

“…Comparison experiments of TFREC algorithm with YOLOv3 [34], YOLOv5 [35], and Ada-Boost [36], algorithm yielded a table of detection rates as shown in Table 6, and a graph of comparison of detection performance as shown in Fig 6 . To validate the correctness of the above analysis, the following hypothesis tests are done, based on the definition of H0 when there is no significant difference between the TFREC algorithm and the comparison algorithm, H1 when one algorithm outperforms the other and the difference is significant. The hypothesis experiments were carried out 100 times as shown in Table 7.…”

AI based Dynamic Prediction Model for Mobile Health Application System

“…Several intelligent tools have been developed based on machine learning and data-driven methodologies to address these issues. These methods have all included connecting many data sources to create a collective understanding for future research and predictive analysis [9,10]. Several research have demonstrated that the severity of heart diseases may be automatically diagnosed using various machine learning approaches, such as combining numerous classification algorithms and augmentation algorithms to create reliable automated prediction systems.…”