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Analysis of Neural Networks Efficiency for the Restoration of Damaged Files

Student: Lupanov Vladislav

Supervisor: Sergey Alexandrovich Slastnikov

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2018

This work is devoted to study of machine learning state-of-the-art models applied to restoration of damaged files problem. The main tasks of this work are to analyze the efficiency of last neural network models solving the problem mentioned above and to compare results with previous works that use both approaches: neural networks and statistical. We use data consisting of the most popular file types collected from the Internet and manually randomly added noise to that data to test our models. This work consists of analytical, theoretical and practical parts. To begin with, some recent file type detection and analysis methods are considered. Furthermore, data preprocessing and used models are described. Finally, the comparison of results of this work and previous models applied to our data are shown.

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