Computer Science And Technology - Research Publications

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

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Subject: search.filters.subject.Clustering

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    Semi-supervised labeling: a proposed methodology for labeling the twitter datasets
    (Springer, 2022-01-28T00:00:00) Jan, Tabassum Gull; Khurana, Surinder Singh; Kumar, Munish
    Twitter has nowadays become a trending microblogging and social media platform for news and discussions. Since the dramatic increase in its platform has additionally set off a dramatic increase in spam utilization in this platform. For Supervised machine learning, one always finds a need to have a labeled dataset of Twitter. It is desirable to design a semi-supervised labeling technique for labeling newly prepared recent datasets. To prepare the labeled dataset lot of human affords are required. This issue has motivated us to propose an efficient approach for preparing labeled datasets so that time can be saved and human errors can be avoided. Our proposed approach relies on readily available features in real-time for better performance and wider applicability. This work aims at collecting the most recent tweets of a user using Twitter streaming and prepare a recent dataset of Twitter. Finally, a semi-supervised machine learning algorithm based on the self-training technique was designed for labeling the tweets. Semi-supervised support vector machine and semi-supervised decision tree classifiers were used as base classifiers in the self-training technique. Further, the authors have applied K means clustering algorithm to the tweets based on the tweet content. The principled novel approach is an ensemble of semi-supervised and unsupervised learning wherein it was found that semi-supervised algorithms are more accurate in prediction than unsupervised ones. To effectively assign the labels to the tweets, authors have implemented the concept of voting in this novel approach and the label pre-directed by the majority voting classifier is the actual label assigned to the tweet dataset. Maximum accuracy of 99.0% has been reported in this paper using a majority voting classifier for spam labeling. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Clustering of tweets: A novel approach to label the unlabelled tweets
    (Springer, 2020) Jan T.G.
    Twitter is one of the fastest growing microblogging and online social networking site that enables users to send and receive messages in the form of tweets. Twitter is the trend of today for news analysis and discussions. That is why Twitter has become the main target of attackers and cybercriminals. These attackers not only hamper the security of Twitter but also destroy the whole trust people have on it. Hence, making Twitter platform impure by misusing it. Misuse can be in the form of hurtful gossips, cyberbullying, cyber harassment, spams, pornographic content, identity theft, common Web attacks like phishing and malware downloading, etc. Twitter world is growing fast and hence prone to spams. So, there is a need for spam detection on Twitter. Spam detection using supervised algorithms is wholly and solely based on the labelled dataset of Twitter. To label the datasets manually is costly, time-consuming and a challenging task. Also, these old labelled datasets are nowadays not available because of Twitter data publishing policies. So, there is a need to design an approach to label the tweets as spam and non-spam in order to overcome the effect of spam drift. In this paper, we downloaded the recent dataset of Twitter and prepared an unlabelled dataset of tweets from it. Later on, we applied the cluster-then-label approach to label the tweets as spam and non-spam. This labelled dataset can then be used for spam detection in Twitter and categorization of different types of spams.
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    Advance Stable Election Protocol in Wireless Sensor Network
    (Excel India Publishers, 2014) Singh, Mandeep; Sidhu, Navjot
    Wireless sensor network (WSN) is an emerging research field. There are large numbers of sensors that collect and send data to base station. Saving energy by using various routing techniques is a challenge. Clustering is main technique used for this. Various protocols like SEP (Stable Election Protocol) and ESEP (Extended Stable Election Protocol) are clustering based heterogeneous aware protocols. In this paper, a new protocol ASEP (Advance Stable Election Protocol) has been proposed based on SEP. This is based on changing more efficiently and dynamically the cluster head election probability. Performance of this protocol has been evaluated in MATLAB and graphical results have been shown. The performance of ASEP is better than SEP in form of first node dies and total number of packets delivered.
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    GRAY HOLE ATTACK ON HYBRID 5G NETWORK
    (International Journal of Advanced Research in Computer Science, 2017) Bhan, Anshu; Khurana, Surinder Singh
    5G device to device communication is such a product of progressive thinking, a network that uses both LTE communication scenario in conjugation with Wi-Fi low band communication. The main idea for conjunction of two different types of network is based on the fact that base stations suffer large amount of traffic and tend to drop data and information in such cases. Apart from these facts another main stream goal is to provide security for such a communication technology. The network is based on the transitioning nodes, a set of cluster head communicates with another cluster head using base stations and nodes in between transfer from one cluster head to another. Gray hole attack is a situation in which the attacker inserts a malicious node into cluster head and steals information. This paper is based on the performance of 5G networks and effects of gray hole attacks on 5G networks.