RAMESH SUNDER NAYAK
@canaraengineering.in
ASSOCIATE PROFESSOR, INFORMATION SCIENCE & ENGINEERING DEPARTMENT
CANARA ENGINEERING COLLEGE
RESEARCH INTERESTS
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, BIG DATA, IMAGE PROCESSING
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sannidhan M. S., Jason Elroy Martis, Ramesh Sunder Nayak, Sunil Kumar Aithal, and Sudeepa K. B.
IGI Global
Treatment of influenza and its complications is a major challenge for healthcare systems. Pyrazine is one drug used in treating influenza. Aspergillic acid is major antibiotic constituent in pyrazine compounds mined from Aspergillus flavus' final stage. This stage of flavus is detected through color change forming a pale-yellow crystal structure. Detection of the same is complex and demands an experienced fraternity to continuously monitor the growth of fungus and identify its color change. However, researches proved that the task needs to be perfect and a tiny human error leads to a catastrophe in antibiotic creation. To avoid these flaws, druggists make a huge investment on costly equipment for accurate detection. To overcome these drawbacks, this article proposes a hybrid quantum convolutional neural network that predicts various stages of the fungus from the microscope's sample. To train the network, about 47,000 samples were poised under typical lab settings. The proposed system was tested in usual conditions and positively isolated the mature samples with 96% efficiency.
Asim Shahzad, Mudassar Raza, Jamal Hussain Shah, Muhammad Sharif, and Ramesh Sunder Nayak
Springer Science and Business Media LLC
AbstractWhite blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.
Javaria Amin, Muhammad Sharif, Anandakumar Haldorai, Mussarat Yasmin, and Ramesh Sundar Nayak
Springer Science and Business Media LLC
AbstractBrain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.
Javaria Amin, Muhammad Sharif, Eman Gul, and Ramesh Sunder Nayak
Springer Science and Business Media LLC
AbstractWireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.
D. K. Thara, B. G. Premasudha, Ramesh Sunder Nayak, T. V. Murthy, G. Ananth Prabhu, and Naeem Hanoon
Springer Science and Business Media LLC
Ayush Dogra, Bhawna Goyal, Sunil Agrawal, Urcun John Tanik, Sanjeev Kumar, and Ramesh Sunder Nayak
Springer Science and Business Media LLC
A. Bakiya, K. Kamalanand, V. Rajinikanth, Ramesh Sunder Nayak, and Seifedine Kadry
Springer Science and Business Media LLC
Muhammad Attique Khan, Muhammad Sharif, Tallha Akram, Syed Ahmad Chan Bukhari, and Ramesh Sunder Nayak
Elsevier BV
Muhammad Attique Khan, Muhammad Sharif, Tallha Akram, Mussarat Yasmin, and Ramesh Sunder Nayak
Springer Science and Business Media LLC
B. Sadhana, Ramesh Sunder Nayak, and B. Shilpa
Springer Singapore