Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

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Author: search.filters.author.Singh, ParvinderSubject: search.filters.subject.Machine learning

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    Sentiment analysis of Hindi language text: a critical review
    (Springer, 2023-11-11T00:00:00) Sidhu, Simran; Khurana, Surinder S.; Kumar, Munish; Singh, Parvinder; Bamber, Sukhvinder S.
    Sentiment analysis involves extracting sentiments from various forms of text, including customer reviews, tweets, blogs, and news clips expressing opinions on diverse subjects, even populist events. The advent of tools supporting regional languages has resulted in a substantial surge of regional language texts. As Hindi ranks fourth in terms of native speakers, the development of sentiment analysis mechanisms for Hindi text becomes crucial. This paper provides a comprehensive review of specific approaches used in Hindi sentiment analysis, encompassing negation handling and the evolution of SentiWordNet for the Hindi Language. Moreover, it offers an overview of available Hindi lexicons and insights into diverse stemmers and morphological analyzers designed for the language. Additionally, the paper conducts an in-depth literature review of various sentiment analysis tasks carried out in Hindi, thereby opening avenues for future research in sentiment analysis and opinion mining in the Hindi language. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Feature Engineering and Ensemble Learning-Based Classification of VPN and Non-VPN-Based Network Traffic over Temporal Features
    (Springer, 2023-07-29T00:00:00) Abbas, Gazy; Farooq, Umar; Singh, Parvinder; Khurana, Surinder Singh; Singh, Paramjeet
    With the rapid advancement in technology, the constant emergence of new applications and services has resulted in a drastic increase in Internet traffic, making it increasingly challenging for network analysts to maintain network security and classify traffic, especially when encrypted or tunneled. To address this issue, the proposed strategy aims to distinguish between regular traffic and traffic tunneled through a virtual private network and characterize traffic from seven different applications. The proposed approach utilizes various ensemble machine learning techniques, which are efficient and accurate and consume minimal computational time for training and prediction compared to conventional machine and deep learning models. These models were applied for both the classification and characterization of network traffic, deriving efficient results. The extreme and light gradient boosting algorithms performed well in multiclass classification, while AdaBoost and Light GBM performed well in binary classification. However, when all the datasets were merged and categorized into two classes and various feature engineering methods were applied, the proposed system achieved an accuracy of more than 99%, with minimal error scores using light GBM with min�max scaling over stratified fivefold, thereby outperforming all existing approaches. This research highlights the efficiency and potential of the proposed model in detecting network traffic. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.