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Browsing by Subject "data classification"

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  • thumbnail.journal.alt
    Item type:Thesis, Access status: Restricted ,
    Klasyfikacja obrazów SAR za pomocą systemu neuro-rozmytego
    (Data obrony: 2014-10-03) Kwiatkowski, Arkadiusz
    Wydział Geologii, Geofizyki i Ochrony Środowiska
    The objective of this thesis was to investigate the suitability of the neuro-fuzzy system for the classification of compressed polarimetric signatures of SAR data in the form of a 100-element vectors. The Fuzzy ART network was implemented by using Visual Studio 2010. The behavior of the network and the variability of the results obtained for different datasets with additional changes of network parameters are described in this dissertation. The effectiveness of system and selection of the best network configuration for the respective groups of data was evaluated. Moreover, the network resistance for the perturbed datasets was investigated.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Optchain: an advanced optimization method for enhancing IoT Data security via blockchain
    (Wydawnictwa AGH, 2025) Kokate, Shatakshi; Shrawankar, Urmila
    The increased use of IoT devices in various domains generates abundant data traffic. Securing this data during its transfer and storage is essential. Blockchain is now a trending technology to provide security to the data; however, it is observed that blockchain performs poorly while managing large volume data. To mitigate this issue, an advanced Optchain method to reduce the data size before submitting it to the blockchain network is discussed in this paper. This Optchain method optimizes IoT-generated data using data-classification and compression techniques. The classification of data as relevant or irrelevant is based on predefined thresholds of critical healthcare parameters. Subsequently, the Optchain method employs the Z-standard algorithm for compressing only the relevant data, ensuring efficient storage and faster blockchain transactions. Simulation results using the iFogSim simulator and Ethereum blockchain demonstrated improved storage costs and computational times compared to traditional methods.