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.Classification technique

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    Resolving the celestial classification using fine k-NN classifier
    (Institute of Electrical and Electronics Engineers Inc., 2016) Yadav, Sangeeta; Kaur, Amandeep; Bhauryal, Neeraj Singh
    With the rapid growth in space technology, space exploration is on the high demand. With each such type of mission, data is accumulating in heaps. Be it manned or unmanned mission, its credibility is defined by the quality of research which can be conducted on the data collected in such missions through remote or on the capsule experiments. Thus there is huge demand of soft techniques, which can make the space or celestial data as useful as possible. One of the major issues is dearth of automated technique for image classification of celestial bodies. Though many image classification techniques exist, but none of them is totally attributed to celestial bodies. An artificial neural network based classifier is proposed to classify celestial object from its image. Texture features are extracted from 90 images of size of 225-225 of different planets. Different classifiers were applied on this training data. Accuracy of different classifiers is compared to find out the best classifier for space data classification. Different validation schemes are applied and the results are compared to figure out the best validation scheme. ? 2016 IEEE.
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    Comparison of classification techniques for intrusion detection dataset using WEKA
    (Institute of Electrical and Electronics Engineers Inc., 2014) Garg, T.; Khurana, S.S.
    As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design 'Intrusion Detection Models' which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance. Rotation Forest classification approach outperformed the rest. ? 2014 IEEE.