Enhanced Multi-Criteria DNA Sequence Analysis Using Neutrosophic Logic and Deep Learning: An Integrated Approach for Comparison and Classification

This paper presents a comprehensive approach for analyzing DNA sequences and attempts to find a practical solution to the problems of comparing and classifying genomic data, which have become increasingly complex and noisy. The approach we propose combines neutrosophic logic which primarily deals with data confusion and uncertainty with modern machine learning techniques to achieve more realistic and flexible analysis. Traditionally, traditional methods such as multiple sequence alignment (MSA) algorithms or the NeedlemanWinch algorithm become extremely cumbersome when faced with large or unclear data. Performance degrades, and results are sometimes inaccurate. Therefore, we propose a system we call the "Advanced DNA Comparator," which converts DNA sequences to digital representations using Word2Vec (similar to how humans understand words), uses neutrosophic logic as a tool for understanding ambiguity, and then applies classification to a random forest algorithm. The results we achieved were not only promising, but also truly powerful: 99.83% accuracy, 100% accurate classification, and a ROC-AUC of 99.92%. This simply means that the system was able to handle sequences and detect similarities even when the world was a little luttered.This framework could be a real turning point, because it is not only accurate but also easy to interpret and expand, making it a powerful tool for applications such as personalized medicine or early disease prediction.