A Multi-Factor Monitoring Fault Tolerance Model Based on a CNN Algorithm for Data Recovery

Abstract

A novel CNN-based Data Restoration System is proposed to address the challenges of data recovery in various applications. Data loss or corruption can result from factors like hardware failures, file system errors, or accidental deletion. Traditional data recovery methods often struggle with complex data structures or large-scale datasets. To overcome these challenges, convolution neural networks (CNNs) are leveraged for data restoration. The system utilizes a deep learning framework that takes advantage of CNNs’ spatial understanding to recover lost or corrupted data. By training the CNN on a large dataset of intact data samples, it learns to identify essential patterns and features crucial for successful recovery. The system employs a multi-stage approach. Initially, the CNN model is trained with labeled examples of intact and corrupted data, allowing the network to learn the underlying relationships between input data and the corresponding restored output. During the restoration phase, the trained CNN is applied to corrupted or lost data, extracting relevant features and using its learned knowledge to estimate the original data. By utilizing spatial information from the CNN’s convolutional layers, the system effectively restores data with high accuracy and efficiency. Experimental results on diverse datasets demonstrate that this CNN-based Data Restoration System outperforms traditional methods in both recovery accuracy and speed, highlighting its potential for real-world applications.