Journal of Artificial Intelligence, Virtual Reality, and Human-Centered Computing
Optimized Neural Network Architecture for High-Accuracy Data Classification
Abstract
Mohd Nadeem
Accurate data classification plays a critical role in modern intelligent systems, particularly in areas such as healthcare, finance, cybersecurity, and software engineering, where reliable decision-making depends on precise data analysis. Traditional machine learning algorithms often encounter challenges when dealing with complex, high dimensional, and large-scale datasets, resulting in reduced predictive accuracy and limited adaptability. Neural networks have demonstrated strong potential in addressing these challenges; however, their performance heavily depends on architectural design, parameter configuration, and training strategies. Therefore, developing optimized neural network architecture is essential to improve classification accuracy and computational efficiency. This study proposes an optimized neural network architecture designed to enhance high-accuracy data classification across diverse datasets. The proposed framework focuses on systematic optimization of the neural network structure, including the selection of appropriate hidden layers, neuron configurations, activation functions, and learning parameters. In addition, preprocessing techniques such as data normalization, feature scaling, and dimensionality handling are incorporated to improve the learning capability of the model. Regularization mechanisms and dropout techniques are also applied to reduce overfitting and improve generalization performance. The proposed architecture is evaluated using benchmark datasets commonly used in classification research. The performance of the optimized model is compared with conventional machine learning approaches and baseline neural network models. Evaluation metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic–area under the curve (ROC-AUC) are employed to assess classification effectiveness and robustness. Experimental results demonstrate that the optimized neural network architecture significantly improves classification performance and learning stability. The model achieves higher predictive accuracy and better generalization compared to traditional models, while maintaining efficient computational performance. The findings indicate that architectural optimization and proper training strategies play a vital role in improving neural network-based classification systems. The proposed framework provides an effective and scalable approach for high-accuracy data classification. It can be applied to various real-world applications involving complex datasets, and it offers a promising direction for future research in intelligent data-driven decision-making systems.