Open Access

ARTICLE

Content Based Automated File Organization Using Machine Learning Approaches

by Syed Ali Raza^1,2, Sagheer Abbas¹, Taher M. Ghazal^3,4, Muhammad Adnan Khan^5,6, Munir Ahmad¹, Hussam Al Hamadi^7,*

1 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
2 Department of Computer Science, GC University Lahore, Pakistan
3 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
4 School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, UAE
5 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore, 54000, Pakistan
6 Department of Software, Pattern Recognition and Machine Learning Lab, Gachon University, Seongnam, 13120, Gyeonggido, Korea
7 Cyber-Physical Systems, Khalifa University, Abu Dhabi, 127788, UAE

* Corresponding Author: Hussam Al Hamadi. Email:

Computers, Materials & Continua 2022, 73(1), 1927-1942. https://doi.org/10.32604/cmc.2022.029400

Received 02 March 2022; Accepted 07 April 2022; Issue published 18 May 2022

Abstract

In the world of big data, it's quite a task to organize different files based on their similarities. Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user. Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities. Then, the browsing process can be performed quickly and easily. This research aims to design a system to automatically organize files based on their similarities in terms of content. The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system. The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files. The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content. The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.

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Keywords

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