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



                    

https://researchid.co/manivannancse

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
11

Scopus Publications

361

Scholar Citations

8

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images
    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

  • A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images
    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.

  • A fuzzy logic-based meningioma tumor detection in magnetic resonance brain images using CANFIS and U-Net CNN classification
    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.

  • Hotspot revelation in solar panel using sparse reconstruction and extreme learning machine
    International Journal of Advanced Research in Engineering and Technology, ISSN: 09766480, eISSN: 09766499, Pages: 204-215, Published: March 2020

  • Lehality prediction of highly disproportionate data of ICU deceased using extreme learning machine
    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.

  • Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier
    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.

  • Fusion based Glioma brain tumor detection and segmentation using ANFIS classification
    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

  • Dynamic quality of service model for improving performance of multimedia real-time transmission in industrial networks
    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.

  • A dynamic QoS model for improving the throughput of wideband spectrum sharing in cognitive radio networks
    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.

  • A dynamic framework to enhance quality of service for multimedia real time transmission in content delivery networks
    International Review on Computers and Software, ISSN: 18286003, eISSN: 18286011, Pages: 396-405, Published: February 2014

  • A dynamic QoS model for multimedia real time transmission in enterprise networks
    Journal of Theoretical and Applied Information Technology, ISSN: 19928645, eISSN: 18173195, Pages: 214-224, Published: April 2014

RECENT SCHOLAR PUBLICATIONS

  • Retraction Note to: Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images
    S Krishnakumar, K Manivannan
    Journal of Ambient Intelligence and Humanized Computing 14 (Suppl 1), 419-419 2023

  • A Jamming Attacks Detection Approach Based on CNN based Quantum Leap Method for Wireless Sensor Network
    K. Manivannan M. Balasubramani
    International Journal on Recent and Innovation Trends in Computing and 2023

  • Machine Learning Models based Mental Health Detection
    Manivannan Karunakaran, J. Balusamy and K. Selvaraj
    IEEE Third International Conference on Intelligent Computing Instrumentation 2022

  • Hybridization of immune with particle swarm optimization in task scheduling on smart devices
    J Balusamy, M Karunakaran
    Distributed and Parallel Databases 40 (1), 85-107 2022

  • An Obfuscation Technique for Malware Detection and Protection in Sandboxing
    V Sathya, K Manivannan, V Vani, S Chandrasekaran
    Artificial Intelligence for Cyber Security: Methods, Issues and Possible 2021

  • Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images
    S Krishnakumar, K Manivannan
    Journal of Ambient Intelligence and Humanized Computing 12, 6751-6760 2021

  • A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images
    B Ragupathy, M Karunakaran
    International Journal of Imaging Systems and Technology 31 (1), 118-127 2021

  • A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification
    B Ragupathy, M Karunakaran
    International Journal of Imaging Systems and Technology 31 (1), 379-390 2021

  • A hybrid three-wheeler e-gear using three-stage inverter
    MLC Prabhaker, M Revathi, P Ramu
    International Journal of Vehicle Autonomous Systems 15 (3-4), 241-255 2020

  • Lehality prediction of highly disproportionate data of ICU deceased using extreme learning machine
    A Vidya, D Shanthi, P Gokulakrishnan, K Manivannan
    International Journal of Innovative Technology and Exploring Engineering 2019

  • Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier
    A Selvapandian, K Manivannan
    International Journal of Imaging Systems and Technology 28 (4), 295-301 2018

  • Fusion based glioma brain tumor detection and segmentation using ANFIS classification
    A Selvapandian, K Manivannan
    Computer methods and programs in biomedicine 166, 33-38 2018

  • Improved k-means with fuzzy-genetic algorithm for outlier detection in multi-dimensional databases
    C Sumithirdevi, M Parthiban, K Manivannan, P Anbumani, MS Kumar
    International Journal of Pure and Applied Mathematics 118 (20), 3899-3909 2018

  • A detailed survey on brain tumor detection and classification systems based on features and classifiers
    A Selvapandian, K Manivannan
    Advances in Natural and Applied Sciences 11 (7), 341-345 2017

  • Performance based investigation of scheduling algorithm on multicore processor
    MLC Prabhaker, K Manivannan, S Janani, P Sitalakshmi
    Advances in Natural and Applied Sciences 11 (7), 507-519 2017

  • A modified Frank Wolfe algorithm for social recommendation based on user preference in multimedia application
    P Sukanya, D Shanthi, K Manivannan
    Advances in Natural and Applied Sciences 11 (7), 426-433 2017

  • Cotton fiber quality analysis system using fuzzy C-means clustering algorithm
    E Iswarya, K Manivannan, D Shanthi
    Advances in Natural and Applied Sciences 11 (7), 472-480 2017

  • Smart devices code of floading using Mobile Cloud Computing: A survey
    B Jeevanantham, K Manivannan
    Advances in Natural and Applied Sciences 11 (7), 345-350 2017

  • Low power accuracy substitution circuit for image processing application
    S Venkateshbabu, CG Ravichandran
    2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics 2017

  • An efficient brain tumor detection method based on image registration technique
    CG Ravichandran, K Rajesh
    Asian Journal of Research in Social Sciences and Humanities 7 (7), 354-366 2017

MOST CITED SCHOLAR PUBLICATIONS

  • Fusion based glioma brain tumor detection and segmentation using ANFIS classification
    A Selvapandian, K Manivannan
    Computer methods and programs in biomedicine 166, 33-38 2018
    Citations: 118

  • Bearing fault diagnosis using wavelet packet transform, hybrid PSO and support vector machine
    C Rajeswari, B Sathiyabhama, S Devendiran, K Manivannan
    Procedia Engineering 97, 1772-1783 2014
    Citations: 61

  • Particulate matter characterization by gray level co-occurrence matrix based support vector machines
    K Manivannan, P Aggarwal, V Devabhaktuni, A Kumar, D Nims, ...
    Journal of hazardous materials 223, 94-103 2012
    Citations: 51

  • Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images
    S Krishnakumar, K Manivannan
    Journal of Ambient Intelligence and Humanized Computing 12, 6751-6760 2021
    Citations: 48

  • Condition monitoring on grinding wheel wear using wavelet analysis and decision tree C4. 5 algorithm
    S Devendiran, K Manivannan
    International Journal of Engineering and Technology 5 (5), 4010-4024 2013
    Citations: 19

  • Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier
    A Selvapandian, K Manivannan
    International Journal of Imaging Systems and Technology 28 (4), 295-301 2018
    Citations: 16

  • Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms
    C Rajeswari, B Sathiyabhama, S Devendiran, K Manivannan
    Journal of Vibroengineering 17 (3), 1169-1187 2015
    Citations: 13

  • A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification
    B Ragupathy, M Karunakaran
    International Journal of Imaging Systems and Technology 31 (1), 379-390 2021
    Citations: 11

  • A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images
    B Ragupathy, M Karunakaran
    International Journal of Imaging Systems and Technology 31 (1), 118-127 2021
    Citations: 8

  • Performance based investigation of scheduling algorithm on multicore processor
    MLC Prabhaker, K Manivannan, S Janani, P Sitalakshmi
    Advances in Natural and Applied Sciences 11 (7), 507-519 2017
    Citations: 4

  • Machine Learning Models based Mental Health Detection
    Manivannan Karunakaran, J. Balusamy and K. Selvaraj
    IEEE Third International Conference on Intelligent Computing Instrumentation 2022
    Citations: 2

  • Survey of Hard Real Time Task Scheduling Algorithm on Multicore Processor
    LCP Micheal, K Manivannan
    Asian Journal of Research in Social Sciences and Humanities 6 (12), 135-153 2016
    Citations: 2

  • Hybridization of immune with particle swarm optimization in task scheduling on smart devices
    J Balusamy, M Karunakaran
    Distributed and Parallel Databases 40 (1), 85-107 2022
    Citations: 1

  • Lehality prediction of highly disproportionate data of ICU deceased using extreme learning machine
    A Vidya, D Shanthi, P Gokulakrishnan, K Manivannan
    International Journal of Innovative Technology and Exploring Engineering 2019
    Citations: 1

  • Improved k-means with fuzzy-genetic algorithm for outlier detection in multi-dimensional databases
    C Sumithirdevi, M Parthiban, K Manivannan, P Anbumani, MS Kumar
    International Journal of Pure and Applied Mathematics 118 (20), 3899-3909 2018
    Citations: 1

  • A modified Frank Wolfe algorithm for social recommendation based on user preference in multimedia application
    P Sukanya, D Shanthi, K Manivannan
    Advances in Natural and Applied Sciences 11 (7), 426-433 2017
    Citations: 1

  • Cotton fiber quality analysis system using fuzzy C-means clustering algorithm
    E Iswarya, K Manivannan, D Shanthi
    Advances in Natural and Applied Sciences 11 (7), 472-480 2017
    Citations: 1

  • Performance analysis of healthcare application using parallel k-means clustering algorithm
    T Vithyaa, K Manivannan
    Advances in Natural and Applied Sciences 10 (4), 57-64 2016
    Citations: 1

  • Dynamic Quality of Service Model for improving performance of Multimedia Real-Time Transmission in Industrial Networks
    RC Gopalakrishnan, M Karunakaran
    Plos one 9 (8), e105885 2014
    Citations: 1

  • A dynamic QoS model for improving the throughput of wideband spectrum sharing in cognitive radio networks
    K Manivannan, CG Ravichandran, B Durai
    KSII Transactions on Internet and Information Systems (TIIS) 8 (11), 3731-3750 2014
    Citations: 1