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@article{184571,
        author = {R VINOTH and R Harshavarthini},
        title = {IMAGE PROCESSING TECHNIQUE FOR TROPICAL CYCLONE INTENSITY DETECTION USING DEEP LEARNING ALGORITHM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {2024-2032},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184571},
        abstract = {The prediction and detection of tropical cyclones (TCs) is one of the newest fields of study. Meteorologists employ a variety of ways to anticipate and estimate TC intensity, starting with the Dvorak methodology. We present an image processing-based technique to categorize cyclone strength using feature vectors in this research. The mean, variance, density, and decentricity are used to generate the feature vector of a TC. in machine learning algorithm is the prediction of cyclone image and classification with less efficiency. In this paper we proposed deep convolution neural network scheme is designed for extracted the more feature of images deep learning approach for identifying tropical cyclones (TCs) and their precursors. Twenty year simulated outgoing longwave radiation (OLR) calculated using a cloud-resolving global atmospheric simulation is used for training two-dimensional deep convolutional neural networks (CNNs). Image processing algorithm with feature extraction algorithm is used for extracted feature of image Deep convolution neural network-based algorithm provide the better feature extraction of cyclone images},
        keywords = {Tropical Cyclones, Dvorak Methodology, Intensity, Satellite Photos, deep learning, CNN.},
        month = {September},
        }