# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 7 Conclusions  
This paper classified human DNA sequences with deep learning, focusing on identifying the best combination of features, architectures, and hyperparameter tuning that enhances performance. It was also established that hybrid architecture, especially the LSTM + CNN model, does well in classifying DNA sequences compared to other traditional machine learning algorithms such as logistic regression, naive Bayes, and random forest. It reached an accuracy of 82.99% with the LSTM + CNN hybrid model, which takes advantage of LSTM for long-range dependencies and CNN for local pattern identification. This result underlines the fact that deep learning techniques are effective at extracting complex dependencies and patterns from genomic data, making it a worthwhile approach in human genomics research.  
Feature representation, architectural optimization, and hyperparameter tuning were discussed further as sub-research questions. This study highlighted how preprocessing techniques like one-hot encoding and DNA embedding are crucial for the translation of raw DNA sequences into formats usable in deep learning. While the research emphasized model performance, it does indicate that future work will involve a systematic comparison of encoding methods for feature engineering. Additionally, the hybrid model LSTM + CNN outperformed standalone models such as CNN and GRU, further establishing the supremacy of combining sequential and spatial data learning. Although some form of hyperparameter tuning has not been explored, it provides indications that fine-tuning parameters such as learning rate, batch size, and depth of the network could be conducted for further model generalization and robustness. The work gives the assurance of a promise that deep learning can bring advancement to DNA sequence classification and creates grounds for subsequent exploration in the domain.  
Future Research and Limitations  
An important direction to explore is robustness against attacks in genomic applications. Recent studies [29; 30] demonstrate how minor perturbations in DNA input data significantly degrade prediction performance. Adversarial training and model hardening techniques can render DNA classification systems safer and more reliable, especially for clinical and forensic applications. We plan to accomplish this in future research using perturbation testing and robust optimization methods.  
Figure 13: Consolidated results of DNA sequence classification models.  
**Author Contributions:** Conceptualization, E.T.; Methodology, E.M.; Writing--review & editing. Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The R20000 Research fund is from the School of Computing, and another R20000 is from ROI Research Output incentive.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.