  We live in a period where bio-informatics is rapidly expanding, a significant
quantity of genomic data has been produced as a result of the advancement of
high-throughput genome sequencing technology, raising concerns about the costs
associated with data storage and transmission. The question of how to properly
compress data from genomic sequences is still open. Previously many researcher
proposed many compression method on this topic DNA Compression without machine
learning and with machine learning approach. Extending a previous research, we
propose a new architecture like modified DeepDNA and we have propose a new
methodology be deploying a double base-ed strategy for compression of DNA
sequences. And validated the results by experimenting on three sizes of
datasets are 100, 243, 356. The experimental outcomes highlight our improved
approach's superiority over existing approaches for analyzing the human
mitochondrial genome data, such as DeepDNA.
