K Manivannan
Verified @jainuniversity.ac.in
Professor, Department of CSE, School of CSE, JAIN Deemed to be University, JAIN Global Campus,
JAIN Deemed to be University
K Manivannan is an Professor of Computer Science and Engineering in
JAIN Deemed to be University,Bangalore, India. He received
B.E degree in Computer Science and Engineering from Anna University, Chennai and
M.E degree in Computer Science and Engineering from the same University. He has
A Ph.D in Computer Science and Engineering from Anna University, Chennai,
Tamilnadu, India. He has Published 37 National and International Journals, 10
Conferences and 4 books. He has successfully guided 5 Ph.D students in Anna
University, Chennai. His area of interests includes Medical Image Processing, High Performance
Computing, Distributed and Network Architecture.
EDUCATION
B.E CSE
M.E CSE
Ph.D(CSE)
RESEARCH INTERESTS
Machine Learning, Medical Image Processing
FUTURE PROJECTS
Automatic and Reliable Plant Virus detection Using Deep Learning
Specifically we aim to 1.Create a computer-aided tool to automatically detect and classify the various levels of plant pathogens especially in virus 2.Train AI to identify additional features associated 3.Now a days various biosensors are available for the detection of plant pathogens in- suit analysis. 4.If plant pathogen detection can be done using deep learning techniques by analyzing the images of plant leaves, it will be beneficial for taking prevention methods in an early stage. Real time images of crops from large fields can be taken occasionally and can be analyzed for infection using the images taken will be really beneficial. 5.Thousands of images of plant leaves are kept for the analysis and the biosensor result can also be incorporated for fine tuning of the results. 6.The viruses that caused the infection can be identified and the disease can be detected.Early stage detection will prevent the spread of infection over a large field. . KeywordsPlant virus pathogens, Deep lea
Applications Invited
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S. Krishnakumar and K. Manivannan
Journal of Ambient Intelligence and Humanized Computing, ISSN: 18685137, eISSN: 18685145, Pages: 6751-6760, Published: June 2021
Springer Science and Business Media LLC
Balakumaresan Ragupathy and Manivannan Karunakaran
International Journal of Imaging Systems and Technology, ISSN: 08999457, eISSN: 10981098, Pages: 118-127, Published: March 2021
Wiley
In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost.
Balakumaresan Ragupathy and Manivannan Karunakaran
International Journal of Imaging Systems and Technology, ISSN: 08999457, eISSN: 10981098, Pages: 379-390, Published: March 2021
Wiley
This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.
International Journal of Advanced Research in Engineering and Technology, ISSN: 09766480, eISSN: 09766499, Pages: 204-215, Published: March 2020
International Journal of Innovative Technology and Exploring Engineering, eISSN: 22783075, Issue: 9 Special Issue 2, Pages: 716-719, Published: July 2019
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Big data in mortality prediction is rationed with enormous amount of dataset of patients admitted in ICU for the healthcare providers to clarify and interpret about the status of the patients. However, it is difficult to process these large datasets for which big data is used. Mortality prediction of patients admitted in ICU faces many challenges such as imbalance distribution, high dimensionality etc. This paper focuses on overcoming the challenges that arise during the prediction of mortality of ICU patients through pre-processing, feature selection, feature extraction, and classification have been developed. The performance of classifiers has been affected by the high dimensional and unbalanced data of patients. Therefore, a classifier called Extreme Learning Machine has been used for a generalized performance of the classification. In order to predict the rate of mortality for the patients admitted in the ICU by solving the challenges using various methods and tools. For this work, the dataset is collected from a rural hospital that provides medical services in the particular locality. To evaluate the performance of the proposed model, various algorithms have been used and the obtained results are compared. The proposed approach is implemented and experimented in MATLAB software. Various statistical reports are obtained as results and verified. From the results and comparison, it is noticed that the proposed method outperforms than other approaches.
A. Selvapandian and K. Manivannan
International Journal of Imaging Systems and Technology, ISSN: 08999457, eISSN: 10981098, Pages: 295-301, Published: December 2018
Wiley
Detection of abnormal regions in brain image is complex process due to its similarity between normal and abnormal regions. This article proposes an automated technique for the detection of meningioma tumor using Gradient Boosting Machine Learning (GBML) classification method. This proposed system consists of preprocessing, feature extraction and classification stages. In this article, Grey Level Co occurrence Matrix (GLCM) features, intensity features, and Gray Level Run Length Matrix features are derived from the test brain MRI image. These derived feature set are classified using GBML classification approach. Morphological functions are used to segment the tumor region in classified abnormal brain image. The performance of the proposed system is evaluated on brain MRI images which are obtained from open access data set. The proposed methodology stated in this article achieves 93.46% of sensitivity, 96.54% of specificity, and 97.75% of accuracy with respect to ground truth images.
A Selvapandian and K Manivannan
Computer Methods and Programs in Biomedicine, ISSN: 01692607, eISSN: 18727565, Volume: 166, Pages: 33-38, Published: November 2018
Elsevier BV
Ravichandran C. Gopalakrishnan and Manivannan Karunakaran
PLoS ONE, eISSN: 19326203, Published: 29 August 2014
Public Library of Science (PLoS)
Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows. In particular, video compressors make variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by classical real-time protocols, severely reducing the efficiency of network utilization. Thus, it is necessary to enlarge the communication bandwidth to transfer the compressed multimedia streams using Flexible Time Triggered- Enhanced Switched Ethernet (FTT-ESE) protocol. FTT-ESE provides automation to calculate the compression level and change the bandwidth of the stream. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. This proposed framework deals with a dynamic QoS for multimedia transmission over Ethernet with FTT-ESE protocol. This paper also presents distinct QoS metrics based both on the image quality and network features. Some experiments with recorded and live video streams show the advantages of the proposed framework. To validate the solution we have designed and implemented a simulator based on the Matlab/Simulink, which is a tool to evaluate different network architecture using Simulink blocks.
K. Manivannan, C. Ravichandran and B. Durai
KSII Transactions on Internet and Information Systems, ISSN: 19767277, eISSN: 22881468, Pages: 3731-3750, Published: 2014
Korean Society for Internet Information (KSII)
This paper considers a wideband cognitive radio network (WCRN) which can simultaneously sense multiple narrowband channels and thus aggregate the detected available channels for transmission and studies the ergodic throughput of the WCRN that operated under: the wideband sensing-based spectrum sharing (WSSS) scheme and the wideband opportunistic spectrum access (WOSA) scheme. In our analysis, besides the average interference power constraint at PU, the average transmit power constraint of SU is also considered for the two schemes and a novel cognitive radio sensing frame that allows data transmission and spectrum sensing at the same time is utilized, and then the maximization throughput problem is solved by developing a gradient projection method. Finally, numerical simulations are presented to verify the performance of the two proposed schemes.
International Review on Computers and Software, ISSN: 18286003, eISSN: 18286011, Pages: 396-405, Published: February 2014
Journal of Theoretical and Applied Information Technology, ISSN: 19928645, eISSN: 18173195, Pages: 214-224, Published: April 2014