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K. Shankar

Bio: K. Shankar is an academic researcher from Alagappa University. The author has contributed to research in topics: Encryption & Deep learning. The author has an hindex of 37, co-authored 276 publications receiving 3916 citations. Previous affiliations of K. Shankar include National University of Singapore & University of Oxford.


Papers
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Journal ArticleDOI
TL;DR: An innovative automated diagnosis classification method for Computed Tomography images of lungs with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA) is presented.

288 citations

Journal ArticleDOI
TL;DR: This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies, and identified a diverse encryption algorithm with its optimization methods with the most extreme peak signal-to-noise ratio values.
Abstract: The development of the Internet of Things (IoT) is predicted to change the healthcare industry and might lead to the rise of the Internet of Medical Things. The IoT revolution is surpassing the present-day human services with promising mechanical, financial, and social prospects. This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies. For the most part, the patient data are stored as a cloud server in the hospital due to which the security is vital. So another framework is required for the secure transmission and effective storage of medical images interleaved with patient information. For increasing the security level of encryption and decryption process, the optimal key will be chosen using hybrid swarm optimization, i.e., grasshopper optimization and particle swarm optimization in elliptic curve cryptography. In view of this method, the medical images are secured in IoT framework. From this execution, the results are compared and contrasted, whereas a diverse encryption algorithm with its optimization methods from the literature is identified with the most extreme peak signal-to-noise ratio values, i.e., 59.45 dB and structural similarity index as 1.

200 citations

Journal ArticleDOI
TL;DR: A deep learning-based automated detection and classification model for fundus DR images that offers better classification over the existing models is proposed.

164 citations

Journal ArticleDOI
TL;DR: The novelty and objective of this proposed model as feature selection, it’s used to enhance the performance of classifying process with the help of improved gray wolf optimization.
Abstract: Thyroid diseases are across the board around the world. In India as well, there is a critical issue caused because of this disease. Different research studies estimate that around 42 million individuals in India suffer from the ill effects of “thyroid diseases.” Diagnosis of health situations is an energetic and testing undertaking in the field of therapeutic science. Our proposed model is to classify this thyroid data utilizing optimal feature selection and kernel-based classifier process. We will create classifications models and its group show for classification of data using “multi kernel support vector machine.” The novelty and objective of this proposed model as feature selection, it’s used to enhance the performance of classifying process with the help of improved gray wolf optimization. Reason for this optimal feature selection as the insignificant features from unique dataset and computationally increment the performance of the model. The proposed thyroid classification results in the accuracy, sensitivity, and specificity of 97.49, 99.05 and 94.5%, compared to the existing model. This performance measure is computed from the confusion matrix with the diverse measures contrasted with individual models and in addition to the existing classifier and optimization techniques.

159 citations

Journal ArticleDOI
TL;DR: Efficient Lightweight integrated Blockchain (ELIB) model is developed to meet necessitates of IoT and shows maximum performance under several evaluation parameters, and is deployed in a smart home environment.

148 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2016
TL;DR: The learning to teach in higher education is universally compatible with any devices to read, so you can get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for reading learning to teach in higher education. As you may know, people have look numerous times for their favorite books like this learning to teach in higher education, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some infectious bugs inside their laptop. learning to teach in higher education is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the learning to teach in higher education is universally compatible with any devices to read.

1,332 citations

Journal ArticleDOI
TL;DR: A smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches and obtains accuracy of 98.5%, which is higher than existing systems.

379 citations

Journal ArticleDOI
TL;DR: This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images, which achieved desired results on the currently available dataset.

358 citations