Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Peer Consonance in Blockchain based Healthcare Application using AI-based Consensus Mechanism
    (Institute of Electrical and Electronics Engineers Inc., 2020) Kumar, N.; Parangjothi, C.; Guru, S.; Manjappa, M.
    The term 'Blockchain', commonly referred to as the brain behind the Bitcoin network, works on the simple principle of the presence of a distributed and decentralized ledger in a public or private network. Since blockchain is decentralized, it is the duty of the Consensus Algorithm to substantiate the details in the blockchain. Traditional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS), although widely used, are a matter of concern due to computationally expensive operations and convergence towards a monopolized system respectively. Though optimizations of PoW and PoS algorithms were subsequently introduced, their features precincts. This paper aims to provide a solution by presenting a consensus algorithm based on Artificial Intelligence (AI) technology while maintaining the fairness of the system. A Healthcare based system was set up on top of the blockchain network to generate the dataset about the miners in order to train our neural network. On the whole, it incorporated the advantages of the state of the art consensus models which can increase the efficiency of the healthcare industry while diminishing their demerits. © 2020 IEEE.
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    Automated Marine Debris Detection from Sentinel-2 Satellite Imagery
    (Institute of Electrical and Electronics Engineers Inc., 2024) Priyadarshini, R.; Arya, V.; Sowmya Kamath, S.
    Marine debris present a severe, escalating threat to oceans and coastal ecosystems, requiring effective monitoring and detection. This work proposes an automated marine debris detection system utilizing satellite imagery data from the MARIDA dataset, sourced from Sentinel-2. Advanced AI techniques are leveraged to analyze high-resolution satellite imagery, and the models are trained to facilitate the identification/tracking of marine debris across various water bodies. Experiments reveal that the machine learning models form a robust baseline, while the UNet model achieves improved precision. The proposed Attention-activated UNet model achieved the best performance, particularly in challenging conditions. © 2024 IEEE.