Conference Papers

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

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    Privacy preserving data clustering using a heterogeneous data distortion
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2019) Preethi, P.; Kumar, K.P.; Ullhaq, M.R.; Naveen, A.; Hyma, H.
    Modern age computation leads to huge amount of data. The whole data is analysed using data mining. In return, it made its path to disruption of the privacy of data owners. In order to achieve privacy on data we use Privacy Preserving Data Mining (PPDM). But when the privacy is maintained the data utility is decreased and vice versa. So, in order to maintain a balance in both privacy and data utility, Privacy Preserving Data Clustering (PPDC) using a Heterogeneous data distortion is introduced. In this article both original and perturbed data are analysed using K-means and density based clustering techniques and the results are compared to show the balance between privacy and utility of the data. © Springer Nature Singapore Pte Ltd. 2019.
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    Monitoring land use and land cover changes in coastal karnataka
    (Springer Science and Business Media Deutschland GmbH, 2021) Kumar, M.S.; Venkatesh, V.; Gowthami, S.B.; Anjita, N.A.; Nayana, N.; Regi, L.; Dwarakish, G.S.
    The dynamics of land use/land cover can be studied by using digital change detection techniques which are highly significant for the evaluation and development of management strategies in a region. The environmental and hydrological processes prevailing in the area can be interpreted only by analyzing the alterations in the past and present land use and land cover classes. In view of this, the present study is executed to analyze the typical land use change in the coastal region over the three decades by analyzing historical and current land use/land cover (LU/LC) datasets. Landsat 5 and Landsat 8 satellite datasets were considered for change detection analysis using unsupervised classification method. K-means algorithm, a widely used unsupervised classification technique, was adopted in this study to classify coastal region of Karnataka for the years 1990 and 2019. The level-II classification was performed on LU/LC raster datasets (Landsat 5 and 8) which segregated the entire study area into ten classes, namely agricultural land, barren land, built-up area, water, forest, fallow or cultivated land, scrub forest, sandy area, swampy forest and wetlands. This study encapsulated that about 40% of the study area was occupied by water body followed by forestry with a percentage of around 30%. Major changes were observed in the barren land and scrub forest between 1990 and 2019, where the barren land was replaced by scrub forest in 2019. The accuracy assessment is performed to analyze the efficiency of the algorithm and the precision of the classified image which showed an accuracy of 81% in 1990 and 84% in 2019 demonstrating the ability of an algorithm to classify reliably. © Springer Nature Singapore Pte Ltd 2021.