Conference Papers

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

Browse

Filters

2020 - 20242

Settings

Author: search.filters.author.Venkatesan, M.Subject: search.filters.subject.SVM

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Mineral identification using unsupervised classification from hyperspectral data
    (Springer, 2020) Gupta, P.; Venkatesan, M.
    Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset. © Springer Nature Singapore Pte Ltd 2020.
  • No Thumbnail Available
    Item
    Detecting Fake News: A Comparative Evaluation of Machine Learning Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2024) Aishwarya, C.; Venkatesan, M.; Prabhavathy, P.; Shetty, A.S.
    Fake news is a significant and well-acknowledged problem in contemporary society due to its rapid spread via social media and various online networking platforms, thereby making it difficult to determine the validity of information. In this study, we examine literature for this issue, prevalent datasets like LIAR, Politifact, and COVID-19, as well as classical machine learning and deep learning models such as SVM, BiLSTM, and CNN- BiGRU for fake news detection, and analyze their effectiveness and scope of application for fake news detection. © 2024 IEEE.