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@article{185928,
        author = {Smt. Kavya S N and Dr. SudhaMani M and Kusumanjali R and Prathiksha Urs and Shalini R and Sri Raksha Rao},
        title = {FOREST FIRE AND SMUGGLING DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {3452-3462},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185928},
        abstract = {Forest fire detection and smuggling monitoring are critical tasks for preserving natural resources and ensuring national security. This study proposes an integrated approach that employs Convolutional Neural Networks (CNNs) for simultaneous detection of forest fires and smuggling activities using animal tracking surveillance imagery. The CNN architecture is tailored to effectively identify the unique visual patterns associated with both forest fires and smuggling incidents. For forest fire detection, the CNN is trained on a dataset comprising images of various forest environments under different lighting and weather conditions, enabling robust recognition of flames and smoke. Similarly, smuggling detection, the CNN is trained on imagery containing potential indicators of smuggling activities such as irregular movement patterns, and concealed cargo. The proposed system offers real-time monitoring capabilities by processing streaming aerial imagery and promptly alerting authorities to detected incidents. Experimental results demonstrate the effectiveness of the CNN-based approach in accurately identifying forest fires and smuggling activities, showcasing its potential for enhancing environmental conservation efforts and border security measures.},
        keywords = {CNN, Quality assurance, Alpha-beta test, smuggling activities},
        month = {October},
        }