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@article{170024,
        author = {G. Pooja and V. Pragadheeshwaran and N. Preethika and J.P. Shrivatsha and K. Sreenath},
        title = {Holistic Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2977-2979},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170024},
        abstract = {Holistic detection is essential across fields like computer vision, natural language processing, and anomaly detection. This project presents a Python-based approach to holistic detection, combining multiple types of data and advanced machine learning methods. We explore the basics of holistic detection and its value in real-world applications, such as surveillance, medical diagnosis, and cybersecurity. Using Python and its popular libraries (TensorFlow, PyTorch, and Scikit-learn) we outline a framework that covers data preparation, feature extraction, model training, and evaluation. Our approach focuses on creating scalable, interoperable solutions, supported by practical examples and case studies to show its effectiveness. We also discuss future developments, like deep learning and multimodal techniques, that could further enhance holistic detection. This abstract serves as a resource for anyone interested in building holistic detection systems using Python.},
        keywords = {},
        month = {November},
        }