Performance analysis of k-means with different initialization methods for high dimensional data VSN Tajunisha International Journal of Artificial Intelligence and Applications 1 (4), 44-52, 2010 | 35 | 2010 |
An efficient method to improve the clustering performance for high dimensional data by principal component analysis and modified K-means N Tajunisha, V Saravanan International Journal of Database Management Systems 3 (1), 196-205, 2011 | 19 | 2011 |
An increased performance of clustering high dimensional data using Principal Component Analysis N Tajunisha, V Saravanan 2010 First International Conference on Integrated Intelligent Computing, 17-21, 2010 | 19 | 2010 |
A study on evolution of data analytics to big data analytics and its research scope S Sruthika, N Tajunisha 2015 International Conference on Innovations in Information, Embedded and …, 2015 | 13 | 2015 |
Predicting Student Performance Using MapReduce N Tajunisha, M Anjali International journal of Emerging and Computer Science, ISSN: 2319-7242 4 …, 2015 | 12 | 2015 |
A new approach to improve the clustering accuracy using informative genes for unsupervised microarray data sets N Tajunisha, V Saravanan International Journal of Advanced Science and Technology 27, 85-94, 2011 | 10 | 2011 |
Classification of cancer datasets using artificial bee colony and deep feed forward neural networks M Karunyalakshmi, N Tajunisha International Journal of Advanced Research in Computer and Communication …, 2017 | 8 | 2017 |
Automatic classification of ovarian cancer types from CT images using deep semi-supervised generative learning and convolutional neural network N Nagarajan, P.H., Tajunisha Revue d'Intelligence Artificielle 35 (4), pp. 273–280, 2021 | 7 | 2021 |
Concept and Term Based Similarity Measure for Text Classification and Clustering B Sindhiya, N Tajunisha IJERST, ISSN-2319-5991 3 (1), 2014 | 7 | 2014 |
OPTIMAL PARAMETER SELECTION-BASED DEEP SEMI-SUPERVISED GENERATIVE LEARNING AND CNN FOR OVARIAN CANCER CLASSIFICATION. PH Nagarajan, N Tajunisha ICTACT Journal on Soft Computing 13 (2), 2023 | 4 | 2023 |
An improved method of unsupervised sample clustering based on information genes for microarray cancer data sets N Tajunisha, V Saravanan IJCB 2 (1), 2431, 2011 | 3 | 2011 |
Improved channel aware AOMDV routing protocol in manet P Jeevajothi, N Tajunisha International Journal of Computer Science and Management Research, ISSN 1 (4 …, 2012 | 2 | 2012 |
An increased performance of clustering high dimensional data using Priniciapl Component Analysis, 2010 First International Conference on Integrated Intelligent Computing N Tajunisha, V Saravanan DOI 10 (09), 2010 | 2* | 2010 |
An Improved DCNN Classification based on a Modified U-Net Segmentation Approach for Ovarian Cancer N Nagarajan, P.H., Tajunisha International Journal of Intelligent Engineering and Systems, 2022 | 1 | 2022 |
Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network N Nagarajan, P.H., Tajunisha Revue d'Intelligence ArtificielleThis link is disabled. 35 (4), 273–280, 2021 | | 2021 |
Ovarian component analysis: A novel ovarian cancer detection strategies using ovarian cancer classification models towards identifying the survival rate N Nagarajan, P.H., Tajunisha Journal of Advanced Research in Dynamical and Control Systems, 2020 | | 2020 |
MR-CFRVM-ACO with Feature Selection for Efficient Data Mining by Monotonic Constraints DNT A. Shanmugapriya Journal of Adv Research in Dynamical & Control Systems 11 (07-Special Issue), 2019 | | 2019 |
Analysis of Dengue Infection Prognosis Using Classification Techniques DNT Griizma K.R International Journal of Research and Analytical Reviews 6 (1), 2019 | | 2019 |
A Hybrid Fuzzy C-Means Clustering with Artificial Fish Swarm Algorithm Optimization for Data Mining with Cascade RMC-FSVM A Shanmugapriya, N Tajunisha Fuzzy Systems, 57-63, 2018 | | 2018 |
A Survey on Ovarian Cancer Detection using Data Mining Techniques DNT Pillai Honey Nagarajan International Journal of Theoretical and Applied Sciences 10 (1), 2018 | | 2018 |