K Manivannan

@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

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
66

Scopus Publications

394

Scholar Citations

9

Scholar h-index

8

Scholar i10-index

Scopus Publications


  • A Jamming Attacks Detection Approach Based on CNN based Quantum Leap Method for Wireless Sensor Network
    M. Sahaya Sheela, M. Balasubramani, J. J. Jayakanth, R. Rajalakshmi, K. Manivannan, and D. Suresh

    Auricle Technologies, Pvt., Ltd.
    The wireless sensor network is the most significant largest communication device. WSN has been interfacing with various wireless applications. Because the wireless application needs faster communication and less interruption, the main problem of jamming attacks on wireless networks is that jamming attack detection using various machine learning methods has been used. The reasons for jamming detection may be user behaviour-based and network traffic and energy consumption. The previous machine learning system could not present the jamming attack detection accuracy because the feature selection model of Chi-Squared didn’t perform well for jamming attack detections which determined takes a large dataset to be classified to find the high accuracy for jamming attack detection. To resolve this problem, propose a CNN-based quantum leap method that detects high accuracy for jamming attack detections the WSN-DS dataset collected by the Kaggle repository. Pre-processing using the Z-score Normalization technique will be applied, performing data deviations and assessments from the dataset, and collecting data and checking or evaluating data. Fisher’s Score is used to select the optimal feature of a jamming attack. Finally, the proposed CNN-based quantum leap is used to classify the jamming attacks. The CNN-based quantum leap simulation shows the output for jamming attacks with high precision, high detection, and low false alarm detection.

  • Bird Mating Optimizer with Deep Learning-based Tuberculosis Detection using Chest Radiographs
    Manivannan K and Sathiamoorthy S

    Seventh Sense Research Group Journals

  • Pelican Optimization with Majority Voting Ensemble Model for Tuberculosis Detection and Classification on Chest X-Ray Images
    K. Manivannan and S. Sathiamoorthy

    The Intelligent Networks and Systems Society
    : Tuberculosis (TB) detection and classification on chest X-ray (CXR) images remains the most significant task in medical diagnosis. TB is a contagious disorder that affects the pulmonary region, and its diagnosing process often depends on CXR. CXR images are utilized for classifying and detecting TB lesions, including infiltrates, cavities, pleural effusions, and nodules. Manual analysis by radiologists includes a visual assessment of the X-ray images by a skilled physician or radiologist. There were many techniques to automatically classify and detect TB on CXR, including deep learning-based approaches, manual interpretation by radiologists, and computer-aided diagnosis (CAD) systems. This manuscript offers the design of pelican optimization with majority voting ensemble model for tuberculosis detection and classification (POMVE-TDC) technique on the CXR images. The core objective of the POMVE-TDC approach is to classify the incidence of TB on the CXR images. At the primary stage, the POMVE-TDC technique undergoes a contrast enhancement process. Besides, the densely connected network (DenseNet-161) model is applied for the extraction of feature vectors. Meanwhile, a pelican optimization algorithm (POA) based hyperparameter optimizer is designed for the DenseNet-161 model. Finally, a majority voting ensemble classifier comprising graph convolution network (GCN), autoencoder (AE), and extreme learning machine (ELM) models are used. The performance evaluation of the POMVE-TDC technique on the medical dataset highlights the significant outcomes with maximum accuracy of 98.83%

  • Robust Tuberculosis Detection using Optimal Deep Learning Model using Chest X-Rays
    K. Manivannan and S. Sathiamoorthy

    IEEE
    Accurate Tuberculosis (TB) screening using chest X-rays and artificial intelligence (AI) has the potential in increasing the quality of the healthcare services. Early detection of TB using automated tools find beneficial to decrease the severity level of the diseases. Therefore, the recent developments of the deep learning (DL) models are used in the design of automated TB detection tools. With this motivation, this article focuses on the design of new Harris Hawks optimization with Deep Learning Enabled Tuberculosis Classification (HHODL-TBC) model on chest X-rays. The proposed HHODL-TBC model focuses on the recognition and classification of TB effectually. It follows a three stage process: median filtering based noise removal, U-Net segmentation, MobileNetv2 feature extraction, HHO based hyperparameter tuning, and gated recurrent unit (GRU) classifier. The design of the HHO algorithm assist in the optimal hyperparameter selection of the GRU model. A comprehensive set of simulations were performed for illustrating the improvised results of the HHODL-TBC model, and the results demonstrate the improved outcomes of the HHODL-TBC model with higher accuracy of 99.33%.

  • Detection of meningioma tumor images using Modified Empirical Mode Decomposition (MEMD) and convolutional neural networks
    S. Krishnakumar and K. Manivannan

    IOS Press
    The meningioma brain tumor detection is more important than the other tumor detection such as Glioma and Glioblastoma, due to its high severity level. The tumor pixel density of meningioma tumor is high and it leads to sudden death if it is not detected timely. The meningioma images are detected using Modified Empirical Mode Decomposition- Convolutional Neural Networks (MEMD-CNN) classification approach. This method has the following stages data augmentation, spatial-frequency transformation, feature computations, classifications and segmentation. The brain image samples are increased using data augmentation process for improving the meningioma detection rate. The data augmented images are spatially transformed into frequency format using MEMD transformation method. Then, the external empirical mode features are computed from this transformed image and they are fed into CNN architecture to classify the source brain image into either meningioma or non-meningioma. The pixels belonging tumor category are segmented using morphological opening-closing functions. The meningioma detection system obtains 99.4% of Meningioma Classification Rate (MCR) and 99.3% of Non-Meningioma Classification Rate (NMCR) on the meningioma and non-meningioma images. This MEMD-CNN technique for meningioma identification attains 98.93% of SET, 99.13% of SPT, 99.18% of MSA, 99.14% of PR and 99.13% of FS. From the statistical comparative analysis of the proposed MEMD-CNN system with other conventional detection systems, the proposed method provides optimum tumor segmentation results.

  • Optimal Deep Transfer Learning Model for Automated Tuberculosis Classification on Chest Radiographs
    K. Manivannan and S. Sathiamoorthy

    IEEE
    Tuberculosis (TB) is the fifth leading cause of mortality rates across the world, adding nearly 10 million new cases and 1.5 million deaths annually. As TB caused by the bacteria that mostly affect the lungs is prevented and cured, the World Health Organization (WHO) reported a systematic and broad screening for eradicating the disease. Despite its interpretational difficulty and low specificity, poster anterior (PA) chest radiography becomes one of the preferred TB screening techniques. TB is majorly a disease in poor nations; thus, medical practitioners trained to interpret such CXRs were rare. Numerous computer-aided diagnosis (CAD) researches which deal with CXR abnormalities do not give more attention to other diseases (i.e., non-TB). This article devises an Optimal Deep Transfer Learning Model for Automated Tuberculosis Classification (ODTLATC) model. The presented ODTLATC model majorly concentrates on the identification of TB on chest radiographs. To attain this, the ODTLATC model follows a three-stage process such as pre-processing, feature extraction, and classification. At the initial stage, the ODTLATC model employs Weiner filtering (WF) approach for image denoising process. For feature extraction, deep convolutional neural network based residual network (ResNet50) model is utilized. At last, whale optimization algorithm (WOA) with bidirectional recurrent neural network (BiRNN) model is exploited for TB classification purposes. To demonstrate the better performance of the ODTLATC model, a extensive variety of simulations are conducted and the outcomes were inspected on chest radiographs. The comparative study reported the improved performance of the ODTLATC model over other DL models.


  • 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

    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

    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.


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

  • Study of forces, surface finish and chip morphology on machining of Inconel 825
    K. Venkatesan, K. Manivannan, S. Devendiran, Arun Tom Mathew, Nouby M Ghazaly, Aadhavan, and S.M. Neha Benny

    Elsevier BV

  • Musculoskeletal disorders and ergonomic risk factors in foundry workers
    Asif Qureshi, K. Manivannan, Vivek Khanzode, and Sourabh Kulkarni

    Inderscience Publishers

  • Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier
    A. Selvapandian and K. Manivannan

    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.


  • Free vibration of damaged and undamaged hybrid CFRP/GFRP composite laminates


  • Vibration based condition monitoring and fault diagnosis technologies for bearing and gear components-A review


  • Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms


  • Comparative study of different shapes and sizes of heat expansion slots in a ceramic-coated piston


  • An early bearing fault diagnosis using effective feature selection methods and data mining techniques


  • Bearing fault diagnosis using CWT, BGA and artificial bee colony algorithm


  • An Intelligent Gear Fault Diagnosis Based On Wavelet Packet Transform, Information Gain And Multiclass Least Squares Support Vector Machines


  • Linear stability analysis of a motorcycle model using Matlab


  • An intelligent gear fault diagnosis model based on EMD and evolutionary algorithms


RECENT SCHOLAR PUBLICATIONS

  • 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

  • Detection of meningioma tumor images using Modified Empirical Mode Decomposition (MEMD) and convolutional neural networks
    S Krishnakumar, K Manivannan
    Journal of Intelligent & Fuzzy Systems, 1-12 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

  • Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. J Ambient Intell Human Comput 12, 6751–6760 (2021)
    S Krishnakumar, K Manivannan
    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

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: 124

  • 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: 65

  • 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: 53

  • 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

  • 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: 20

  • 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: 18

  • 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: 9

  • CSI Calendar 2012
    MA Pandey, IKI Ahamed, MT Sabapathy, K Venkat, MM Pillai, T Upadhyay, ...
    New Horizon 13, 15 2012
    Citations: 7

  • Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. J Ambient Intell Human Comput 12, 6751–6760 (2021)
    S Krishnakumar, K Manivannan
    2021
    Citations: 6

  • 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

  • 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: 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

  • 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