Electrical impedance tomography (EIT) and its medical applications: a review R Harikumar, R Prabu, S Raghavan Int. J. Soft Comput. Eng 3 (4), 193-198, 2013 | 127 | 2013 |
Analysis of decision tree and k-nearest neighbor algorithm in the classification of breast cancer H Rajaguru, SC SR Asian Pacific journal of cancer prevention: APJCP 20 (12), 3777, 2019 | 102 | 2019 |
An overview of batteries for photovoltaic (PV) systems P Manimekalai, R Harikumar, S Raghavan International Journal of Computer Applications 82 (12), 2013 | 97 | 2013 |
Automatic detection and classification of mammograms using improved extreme learning machine with deep learning SRS Chakravarthy, H Rajaguru Irbm 43 (1), 49-61, 2022 | 85 | 2022 |
Performance analysis and detection of micro calcification in digital mammograms using wavelet features C Abirami, R Harikumar, SRS Chakravarthy 2016 International Conference on Wireless Communications, Signal Processing …, 2016 | 66 | 2016 |
Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals M Manjusha, R Harikumar 2016 International Conference on Wireless Communications, Signal Processing …, 2016 | 61 | 2016 |
Performance Analysis of Neural Networks for Classification of Medical Images with Wavelets as a Feature Extractor H Rajaguru, VK Bojan Int J Imaging Syst Technol, 25 (1), 33-47, 2015 | 60* | 2015 |
Fuzzy techniques for classification of epilepsy risk level from EEG signals R Harikumar, BS Narayanan TENCON 2003. Conference on convergent Technologies for Asia-Pacific Region 1 …, 2003 | 58 | 2003 |
Lung cancer detection using probabilistic neural network with modified crow-search algorithm SC SR, H Rajaguru Asian Pacific journal of cancer prevention: APJCP 20 (7), 2159, 2019 | 57 | 2019 |
A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms SK Prabhakar, H Rajaguru, SW Lee IEEE Access 8, 39875-39897, 2020 | 54 | 2020 |
Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison SR Sannasi Chakravarthy, H Rajaguru International Journal of Imaging Systems and Technology 30 (1), 126-146, 2020 | 53 | 2020 |
Genetic algorithm optimization of fuzzy outputs for classification of epilepsy risk levels from EEG signals R Harikumar, R Sukanesh, PA Bharathi Conference Record of the Thirty-Eighth Asilomar Conference on Signals …, 2004 | 43 | 2004 |
Bayesian linear discriminant analysis for breast cancer classification H Rajaguru, SK Prabhakar 2017 2nd international conference on communication and electronics systems …, 2017 | 32 | 2017 |
Schizophrenia EEG signal classification based on swarm intelligence computing SK Prabhakar, H Rajaguru, K Sun-Hee Computational Intelligence and Neuroscience: CIN 2020, 2020 | 28 | 2020 |
Adaboost Classifier with dimensionality reduction techniques for Epilepsy Classification from EEG SK Prabhakar, H Rajaguru Precision Medicine Powered by pHealth and Connected Health: ICBHI 2017 …, 2018 | 28 | 2018 |
Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer SRS Chakravarthy, N Bharanidharan, H Rajaguru IRBM 44 (3), 100749, 2023 | 27 | 2023 |
Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification SK Prabhakar, H Rajaguru Heliyon 6 (12), 2020 | 27 | 2020 |
Development of patient remote monitoring system for epilepsy classification SK Prabhakar, H Rajaguru The 16th International Conference on Biomedical Engineering: ICBME 2016, 7th …, 2017 | 27 | 2017 |
Dimensionality reduction techniques for processing epileptic encephalographic signals R Harikumar, PS Kumar Biomedical and Pharmacology Journal 8 (1), 103-106, 2015 | 26 | 2015 |
Multi-deep CNN based experimentations for early diagnosis of breast cancer SR Sannasi Chakravarthy, N Bharanidharan, H Rajaguru IETE Journal of Research 69 (10), 7326-7341, 2023 | 25 | 2023 |