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@article{162429,
        author = {Ridhima Gunti and P. Shiva Kumar and P. Sai Puneeth and Mrs. L. Swathi},
        title = {Using AI – Neural Networks For Healthy Oceans},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {391-395},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162429},
        abstract = {Every year, a significant quantity of plastic is dumped; the majority of this plastic ends up in our oceans and other bodies of water, affecting marine life and our environment. It would be very expensive, time-consuming, and perhaps result in significant additional carbon emissions to remove this plastic by hand. The identification of photographs and laborious site visits are the main methods used to identify plastic contamination in the ocean. To solve this problem, we have been using deep-learning algorithms and AI and computer vision research since the late 2010s. To make this procedure simpler, researchers have been working on machine-learning approaches. Using CNNs (convolutional neural networks), a form of deep learning architecture of neural networks used for image processing and recognition in computer vision applications, to facilitate deep learning. Listening and learning about humpback whales using AI. Whales communicate using sound in all species. Certain vocalizations are exclusive to a species or perhaps to the population in that area. Although many whales are difficult to see with the naked eye, their underwater sounds may be heard for a great distance.
We can follow the behavior and detect any unexpected noises by employing passively acoustic monitoring of the sounds generated by marine animals, such as humpback whales, to observe their natural behavior. We can track migration patterns and analyze population shifts over time. Using a convolutional neural network trained on deep learning to detect humpback whale singing in more than 187,000 hours of recorded audio.
},
        keywords = {CNN, neural networks, acoustic monitoring, deep learning, artificial intelligence},
        month = {},
        }