# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 1 Introduction  
The whole field of genomic research has grown over the last several decades, mostly in solving DNA sequences. Because of this fast growth, the need for better methods of classification has become quite urgent. The conventional methods are lacking in both precision and efficiency, which has led to the exploration of machine learning as a strong tool for interpreting genetic data. The current study deals with the development of new deep learning- and machine learning-based algorithms for the classification of DNA sequences originating from dogs, chimpanzees, and humans. The characterization of DNA sequences is one of the most important tasks in genomics, whereby the grouping of sequences by their properties enables scientists to identify genetic variations associated with diseasesand provides functional knowledge of genetic entities. The result could be to reveal highly hidden genetic information and its potential roles in biological processes, something for which machine learning and deep learning have become necessary in, to tackle challenges that traditional methods cannot handle [1]. DNA sequence classification plays an important role in the identification of genomic regulatory regions, serving as a key building block in understanding gene expression and regulation. Additionally, these methods play an important role in identifying pathogenic mutations that participate in genetic disorders and medically relevant data. Machine learning, hence, has extended the capability of genomic data classification, which goes to a deeper understanding of their biological functionality. Feature extraction and representation have been powerfully contributed to by machine learning and deep learning methods; this provides an insight into the relationship between nucleotides with much more detail. This has consequently yielded better accuracy and speed in the classification of DNA sequences, as several recent developments in the area have shown. Such progress not only builds on the current capabilities but opens new avenues to explore, extending the possible scope of discovery in genomics [2]. DNA sequence classification is a challenging task due to the complexity of genomic data, which conventional approaches often fail to handle. While conventional, rule-based algorithms have served well in many contexts, they lack the ability to learn the fine-grained patterns of modern genomic data. This challenge is further compounded because next-generation sequencing technologies can generate voluminous amounts of data. Conventional approaches are far too slow and incapable of dealing with such enormous amounts of data, often needing consideration for genetic variation among humans, chimpanzees, and dogs. The advent of new technologies has, therefore, been increasingly felt, and among these, one of the most promising is machine learning. An ability to distinguish complex patterns in DNA sequences, thanks to machine learning, is what accelerates classification and enhances performance in predictions [3]. Despite its potential, one of the significant challenges that face the application of machine learning to DNA sequence classification is its adaptability in the dynamic and ever-changing nature of genomic landscapes. The full potential of machine learning in genomics can only be realized by developing research methodologies that effectively address these challenges. By doing so, machine learning models can be made more adaptive and able to handle the sophisticated requirements of genomic analysis. The paper proposes new machine learning models for the classification of genomic sequences, discussing their efficiency in analyzing the complex genetic activity of different species. This current research opens the door to more sophisticated ways of analyzing genetic coding, therefore enabling a more precise and more holistic interpretation of genomic data. The field of genomic studies has experienced phenomenal growth in recent decades and, until recently, mostly with respect to DNA sequencing.