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Machine Learning Algorithms Applied to Corrupted Data Recovery: A Comprehensive Survey

Year: 2026

Type: preprint

Abstract: This preprint presents a narrative survey and conceptual synthesis of machine learning approaches applied to corrupted data recovery, including supervised, unsupervised, generative, transformer-based, and reinforcement learning methods.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20353907

Author: Conta, Erika

Language: English

Topic: Digital and Cyber Forensics

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7162209954

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