2.Book Chapter (12)

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/3950

Browse

Author: search.filters.author.Vijaya, M S

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A STUDY ON MACHINE LEARNING-BASED APPROACHES FOR PM2.5 PREDICTION
    (Springer Link, 2022-01-17) Santhana Lakshmi, V; Vijaya, M S
    Clean air and water is the fundamental need of humans. But people are exposed to polluted air produced due to several reasons such as combustion of fossil fuels, industrial discharge, dust and smoke which generates aerosols. Aerosols are tiny droplets or solid particles such as dust and smoke that floats in the atmosphere. The size of the aerosol also called as particulate matter ranges from 0.001–10 μm which when inhaled by human affects the respiratory organs. Air pollution affects the health of 9% of the people every year. It is observed as the most important risk factor that affects human health. There is a need for an efficient mechanism to forecast the quality of air to save the life of the people. Statistical methods and numerical model methods are largely employed for the predicting the value of PM2.5. Machine learning is an application of artificial intelligence that gives a system capability to learn automatically from the data, and hence, it can be applied for the successful prediction of air quality. In this paper, various machine learning methods available to predict the particulate matter 2.5 from time series data are discussed.
  • No Thumbnail Available
    Item
    STUDYING THE EFFECTIVENESS OF COMMUNITY DETECTION ALGORITHMS USING SOCIAL NETWORKS
    (Springer Link, 2022-11-01) Kiruthika, R; Vijaya, M S
    Social network analysis is a significant area of research for analyzing the interconnection between the people within network. Community detection is one of the most important applications in SNA. The main motive of CD is to discover the collection of node that are tightly correlated within the network and weakly correlated to another network for partitioning the network to form the group of communities. The aim of this work is to detect communities from undirected disjoint social networks in which it is implemented on lesmis and email-Eu-core-department-labels networks. Effective partitioning and detection of the network are the primary factors for implementing this work by using Girvan–Newman, greedy modularity maximization, and Kernighan–Lin bipartition CD algorithms. The effectiveness of these CD algorithms is analyzed with respect to ground-truth communities based on measures such as recall, normalized mutual information score, precision, and F1-score. Experimental results show that the greedy modularity maximization algorithm provides best results for CD on email-Eu-core-department-labels network with respect to corresponding ground-truth communities.