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Barath Narayanan Narayanan
Researcher at University of Dayton
Publications - 28
Citations - 593
Barath Narayanan Narayanan is an academic researcher from University of Dayton. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 11, co-authored 26 publications receiving 335 citations. Previous affiliations of Barath Narayanan Narayanan include University of Dayton Research Institute.
Papers
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Proceedings ArticleDOI
Performance analysis of machine learning and pattern recognition algorithms for Malware classification
TL;DR: This paper visualize viruses in an image as they capture minor changes while retaining a global structure and implements Principal Component Analysis (PCA) method for feature extraction and studies the performance of various Artificial Neural Network algorithms along with K-Nearest Neighbors and Support Vector Machine classification techniques for identification of malware data into their respective classes.
Journal ArticleDOI
Review—Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors
Srikanth Namuduri,Barath Narayanan Narayanan,Barath Narayanan Narayanan,Venkata Salini Priyamvada Davuluru,Lamar Burton,Shekhar Bhansali +5 more
Proceedings ArticleDOI
Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset
Temesguen M. Kebede,Ouboti Djaneye-Boundjou,Barath Narayanan Narayanan,Anca L. Ralescu,David Kapp +4 more
TL;DR: The architecture of the system is described, which makes use of an architecture comprised of multiple layers (multiple levels of encoding) to carry out the classification process of malicious programs, and the performance of this approach against traditional machine learning and pattern recognition algorithms is compared.
Journal ArticleDOI
Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities
TL;DR: A novel cluster-based classifier architecture for lung nodule computer-aided detection systems in both modalities is presented and a novel optimized method of feature selection for both cluster and classifier components is proposed.
Proceedings ArticleDOI
Performance analysis of machine learning and deep learning architectures for malaria detection on cell images
TL;DR: A fast Convolutional Neural Network architecture is proposed for the classification of cell images that would enhance the workflow of microscopists and provide a valuable second opinion on the detection of Plasmodium in digital microscopy.