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Balakrishna Tripathy

Bio: Balakrishna Tripathy is an academic researcher. The author has contributed to research in topics: Cluster analysis. The author has an hindex of 1, co-authored 1 publications receiving 16 citations.

Papers
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Journal ArticleDOI
TL;DR: A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems.
Abstract: Purpose – The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM. Design/methodology/approach – A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images. Findings – The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yi...

16 citations


Cited by
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Journal ArticleDOI
TL;DR: A hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets and experimental results confirm the fact that the proposed model performs better than the existing machine learning models.
Abstract: The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

226 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: The algorithm proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM) that is comparatively less hampered by noise and performs better than existing algorithms.
Abstract: A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership functions within the neighborhood of the pixel under consideration. The results showed that this approach was not as sensitive to noise as compared to the traditional FCM algorithm and yielded better results. The algorithm we have proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM). Intuitionistic refers to the degree of hesitation that arises as a consequence of lack of information and knowledge. Proposed method is comparatively less hampered by noise and performs better than existing algorithms.

42 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: This paper improves a possibilistic rough C-Means (PRCM) algorithm introduced by Anuradha et, al. and introduces a new algorithm, which is called as possibileistic rough fuzzy C-means (PFCM), which is compared with the improved PRCM and the basic PRFCM algorithm to establish experimentally that this algorithm is comparatively better than PR CM and the corresponding RCM algorithm.
Abstract: Several data clustering techniques have been developed in literature. It has been observed that the algorithms developed by using imprecise models like rough sets, fuzzy sets and intuitionistic fuzzy sets have been better than the crisp algorithms. Also, the hybrid models provide far better clustering algorithms than the individual models. Several such models have been developed by using a combination of fuzzy set introduced by Zadeh, the rough set introduced by Pawlak and the intuitionistic fuzzy introduced by Atanassov. Notable among them being the Rough Fuzzy C-Means (RFCM) introduced by Mitra et al and the rough intuitionistic fuzzy c-means algorithm (RIFCM) introduced and studied by Tripathy et al Krishnapuram and Keller observed that the basic clustering algorithms have the probabilistic flavour; for example due to the presence of the constraint on the memberships used in the fuzzy C-Means (FCM) algorithm. So, they introduced the concept of possibilistic approach and developed a possibilistic fuzzy C-means (PFCM) algorithm. Another approach to PFCM is due to Pal et al. In this paper, we improve a possibilistic rough C-Means (PRCM) algorithm introduced by Anuradha et, al. and introduce a new algorithm, which we call as possibilistic rough fuzzy C-Means (PRFCM) and compare its efficiency with the improved PRCM and the basic PRFCM algorithm to establish experimentally that this algorithm is comparatively better than PRCM and the corresponding RCM algorithm. We perform the experimental analysis by taking different types of numerical datasets and images as inputs and using standard accuracy measures like the DB and the D-index.

14 citations

Proceedings ArticleDOI
28 Sep 2015
TL;DR: A comparative analysis is performed over the Gaussian, hyper tangent and radial basis kernel functions by their application on various vague clustering approaches, revealing that for small sized datasets Gaussian kernel produces more accurate clustering than radial basis andhyper tangent kernel functions however for the datasets which are considerably large hyper tangents kernel is superior to other kernel functions.
Abstract: Application of clustering algorithms for investigating real life data has concerned many researchers and vague approaches or their hybridization with other analogous approaches has gained special attention due to their great effectiveness. Recently, rough intuitionistic fuzzy c-means algorithm has been proposed by Tripathy et al [3] and they established its supremacy over all other algorithms contained in the same set. Replacing the Euclidean distance metric with kernel induced metric makes it possible to cluster the objects which are linearly inseparable in the original space. In this paper a comparative analysis is performed over the Gaussian, hyper tangent and radial basis kernel functions by their application on various vague clustering approaches like rough c-means (RCM), intuitionistic fuzzy c-means (IFCM), rough fuzzy c-means (RFCM) and rough intuitionistic fuzzy c-means (RIFCM). All clustering algorithms have been tested on synthetic, user knowledge modeling and human activity recognition datasets taken from UCI repository against the standard accuracy indexes for clustering. The results reveal that for small sized datasets Gaussian kernel produces more accurate clustering than radial basis and hyper tangent kernel functions however for the datasets which are considerably large hyper tangent kernel is superior to other kernel functions. All experiments have been carried out using C language and python libraries have been used for statistical plotting.

12 citations

Journal ArticleDOI
TL;DR: EDM has been used for predicting the performance about placement of final year students by using the attributes such as academic records, age, and achievement etc., and based on the result, higher education organizations can offer superior education to its students.
Abstract: he purpose of higher education organizations have to offer superior education to its students. The proficiency to forecast student's achievement is valuable in affiliated ways associated with organization education system. Students' scores which they got in an exam can be used to invent training set to dominate learning algorithms. With the academia attributes of students such as internal marks, lab marks, age etc., it can be easily predict their performance. After getting predicted result the performance of the student to engage with desirable assistance to the students will be improved. Educational Data Mining (EDM) offers such information to educational organization from educational data. EDM provides various methods for prediction of students performance, which improve the future result of students. In this paper, by using the attributes such as academic records, age, and achievement etc., EDM has been used for predicting the performance about placement of final year students. Based on the result, higher education organizations can offer superior education to its students. KeywordsMining, Educational Data Mining, Sum of Difference, Prediction.

8 citations