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
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
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S. Krishnakumar and K. Manivannan
Springer Science and Business Media LLC
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.
Manivannan K and Sathiamoorthy S
Seventh Sense Research Group Journals
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%
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%.
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.
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.
S. Krishnakumar and K. Manivannan
Springer Science and Business Media LLC
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.
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.
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.
K. Venkatesan, K. Manivannan, S. Devendiran, Arun Tom Mathew, Nouby M Ghazaly, Aadhavan, and S.M. Neha Benny
Elsevier BV
Asif Qureshi, K. Manivannan, Vivek Khanzode, and Sourabh Kulkarni
Inderscience Publishers
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.
A Selvapandian and K Manivannan
Elsevier BV