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@article{204552,
        author = {K Ansha and Brintha C},
        title = {Deep Learning-Driven Cardiometric Analysis on Chest X-Ray Images for Early Cardiac Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {3019-3027},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204552},
        abstract = {The proliferation of advanced cyber-attacks has been triggered by the active construction of digital infrastructure, whereby the efficient intrusion detection systems are the determinant of providing the network security. The paper under consideration offers a mixed method of machine learning-based cyber-attacks prediction with the help of GANs to enhance the detectability. The real world of the normal and evil network traffic is rendered with the help of the UGR16 information. Preprocessing of data and feature extraction arrangements are installed in such a manner that quality of data and the complexity are increased. The machine learning schemes used in the classification include: Random Forest, Support Vector Machine and Logistic Regression, and GAN is employed to generate false attack samples to overcome the issue of imbalanced classes. The system accuracy of the proposed system is 95% which represents the effectiveness of the system to detect the normal and attack traffic. The findings of the experiments demonstrate the improvement of the precision, recall and F1-score, i.e. the larger the detection ability the smaller the false positives. The model also has good generalization behavior besides stable training behavior and validation. In general, the problem of generative and predictive strategies is an effective and scaled solution to the current issues of cybersecurity.},
        keywords = {Cyber Attack Detection, Intrusion Detection System, Machine Learning, Generative Adversarial Network, UGR16 Dataset.},
        month = {June},
        }