A proposed coding algorithm with AI-assisted restoration capability

This paper introduces an innovative encoding method based on the concept of "distancecodes" , which allows characters to appear randomly in different locations during the decoding process. Codeword-based algorithms decode and present their characters sequentially, which can hinder accurate reconstruction in the event of incomplete text decoding, as there is no intelligent or non-intelligent model available for continuing the text reconstruction.In contrast, our proposed algorithm allows for the non-sequential and scattered appearance of characters throughout the data during the decoding process. This enables AI algorithms to restore undecoded characters using contextual information such as decoded characters, adjacent complete and incomplete words, and the specified length of words. This innovative approach significantly enhances decoding efficiency, achieving rates ranging from 50\% to 90\% across diverse texts.Further evaluation of the algorithm's performance with advanced language models—including GPT-4, GPT-Turbo, Mistral Large 2, Claude 3.5 Sonnet, and Llama 3-1-70—demonstrates restoration accuracy and completeness ranging from 90\% to 100\%. Additionally, our experiments on image data also yielded more successful results in image recovery than traditional codeword-based algorithms. These findings highlight the potential of integrating AI-driven models into coding practices, paving the way for improved data handling and recovery strategies.