LITERATURE SURVEY ON AUTOMATED STORAGE CLEANER
Authors/Creators
- 1. Assistant Professor, School of C & IT, REVA University, Bangalore, Karnataka, India
- 2. Student, School of C & IT, REVA University, Bangalore, Karnataka, India
Description
With the increase in digital devices' use, the need for storage has also increased. Files and data get accumulated in the device which will slow down the performance of the device. To deal with this issue automated storage cleaner app has been introduced and hence this paper deals with the automated storage cleaners. This paper represents the app that helps to delete unnecessary data and files. This app helps to delete unnecessary files and data by using algorithms and some tools to scan duplicate and temporary files and then helps the user to delete them. The app offers some features, one of which includes the ability to perform regular scans and cleanups, to optimize the device’s storage usage, and to backup important files before deletion. As there exists a lot of storage cleaner apps, when choosing the storage cleaner app, it is important to consider the features that are offered, app’s reliability and user reviews. It is also important to be cautious before allowing the app to access the data and files, and to read the app’s privacy policy before using it.
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LITERATURE SURVEY ON AUTOMATED STORAGE CLEANER -Formatted Paper.pdf
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Additional details
References
- 1. Thomee, B., Huiskes, M. J., Bakker, E. M., & Lew, M. S. (2013, July). An evaluation of content-based duplicate image detection methods for web search. In 2013 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
- 2. Li, Y. (2021, May). A fast algorithm for near-duplicate image detection. In 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) (pp. 360-363). IEEE.
- 3. Landge, A., Mane, P. (2016). Near duplicate image matching techniques. ICICES, 7.
- 4. Kim, C. (2003). Content-based image copy detection. Signal Processing: Image Communication, 18(3), 169-184.