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2,623 results on '"Kavitha S"'

52. Lung Cancer Classification and Prediction Based on Statistical Feature Selection Method Using Data Mining Techniques

Author

Kavitha, S., Prasad, N. H., Sowmya, K., Naik, Ramavathu Durga Prasad, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Shetty, N. R., editor, Prasad, N. H., editor, and Nagaraj, H. C., editor

Published
2024
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53. Detection of Content-Based Image Retrieval Using OCR Tools

Author

MariyanRichard, A., Prasad, N. H., Kavitha, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Shetty, N. R., editor, Prasad, N. H., editor, and Nalini, N., editor

Published
2024
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66. Wilms Tumour with Intracardiac Extension

Author

Kavitha S Srivatsa, Appaji L, and Aruna Kumari BS

Subjects
Wilms tumour, Intracardiac extension, Nephroblastoma, Medicine
Abstract

Wilms tumour or nephroblastoma is the most common renal tumour of in children. It accounts for 6% of all pediatric tumours and is the second most frequent intrabdominal solid organ tumour in children. Inferior vena cava (IVC) involvement by Wilms tumour occurs in 4-10% of patients and right atrium thrombus extension in less than 1%. Using a multidisciplinary approach, overall survival is excellent in Wilms tumour. We are presenting this case because Wilms tumour with right atrial thrombus is relatively rare and continues to remain a challenge for treating physicians

Published
2012

75. Automated Screening for Three Inborn Metabolic Disorders: A Pilot Study

Author

Kavitha S, Sarbadhikari SN, and Ananth N Rao

Subjects
Decision support techniques, Metabolic diseases, Computer-assisted diagnosis, Expert system, Medicine
Abstract

Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling us to take decisions on the screening and clinical diagnosis of a patient. Settings and Design: A non-incremental concept learning classification algorithm was applied to a set of patient data and the procedure followed to obtain a decision on a patient’s disorder. Materials and Methods: Initially a training set containing 13 cases was investigated for three inborn errors of metabolism. Results: A total of thirty test cases were investigated for the three inborn errors of metabolism. The program identified 10 cases with galactosemia, another 10 cases with fructosemia and the remaining 10 with propionic acidemia. The program successfully identified all the 30 cases. Conclusions: This kind of decision support systems can help the healthcare delivery personnel immensely for early screening of IMDs.

Published
2006

81. Brahma safeguards canalization of cardiac mesoderm differentiation

Author

Hota, Swetansu K, Rao, Kavitha S, Blair, Andrew P, Khalilimeybodi, Ali, Hu, Kevin M, Thomas, Reuben, So, Kevin, Kameswaran, Vasumathi, Xu, Jiewei, Polacco, Benjamin J, Desai, Ravi V, Chatterjee, Nilanjana, Hsu, Austin, Muncie, Jonathon M, Blotnick, Aaron M, Winchester, Sarah AB, Weinberger, Leor S, Hüttenhain, Ruth, Kathiriya, Irfan S, Krogan, Nevan J, Saucerman, Jeffrey J, and Bruneau, Benoit G

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Genetics, Stem Cell Research - Embryonic - Non-Human, Pediatric, Cardiovascular, Heart Disease, Stem Cell Research, 1.1 Normal biological development and functioning, Animals, Bone Morphogenetic Protein 4, Cell Differentiation, Cell Lineage, Chromatin, Chromatin Assembly and Disassembly, DNA Helicases, Embryo, Mammalian, Epigenesis, Genetic, Female, Gene Expression Regulation, Male, Mesoderm, Mice, Myocardium, Myocytes, Cardiac, Neurogenesis, Neurons, Nuclear Proteins, Octamer Transcription Factor-6, Phenotype, Repressor Proteins, Stem Cells, Time Factors, Transcription Factors, General Science & Technology
Abstract

Differentiation proceeds along a continuum of increasingly fate-restricted intermediates, referred to as canalization1,2. Canalization is essential for stabilizing cell fate, but the mechanisms that underlie robust canalization are unclear. Here we show that the BRG1/BRM-associated factor (BAF) chromatin-remodelling complex ATPase gene Brm safeguards cell identity during directed cardiogenesis of mouse embryonic stem cells. Despite the establishment of a well-differentiated precardiac mesoderm, Brm-/- cells predominantly became neural precursors, violating germ layer assignment. Trajectory inference showed a sudden acquisition of a non-mesodermal identity in Brm-/- cells. Mechanistically, the loss of Brm prevented de novo accessibility of primed cardiac enhancers while increasing the expression of neurogenic factor POU3F1, preventing the binding of the neural suppressor REST and shifting the composition of BRG1 complexes. The identity switch caused by the Brm mutation was overcome by increasing BMP4 levels during mesoderm induction. Mathematical modelling supports these observations and demonstrates that Brm deletion affects cell fate trajectory by modifying saddle-node bifurcations2. In the mouse embryo, Brm deletion exacerbated mesoderm-deleted Brg1-mutant phenotypes, severely compromising cardiogenesis, and reveals an in vivo role for Brm. Our results show that Brm is a compensable safeguard of the fidelity of mesoderm chromatin states, and support a model in which developmental canalization is not a rigid irreversible path, but a highly plastic trajectory.

Published
2022

92. Efficient DWT-based fusion techniques using genetic algorithm for optimal parameter estimation

Author

Kavitha, S. and Thyagharajan, K. K.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing, I.2.6, I.2.10, I.4.6, I.4.7, I.4.10, I.5
Abstract

Image fusion plays a vital role in medical imaging. Image fusion aims to integrate complementary as well as redundant information from multiple modalities into a single fused image without distortion or loss of information. In this research work, discrete wavelet transform (DWT)and undecimated discrete wavelet transform (UDWT)-based fusion techniques using genetic algorithm (GA)foroptimalparameter(weight)estimationinthefusionprocessareimplemented and analyzed with multi-modality brain images. The lack of shift variance while performing image fusion using DWT is addressed using UDWT. The proposed fusion model uses an efficient, modified GA in DWT and UDWT for optimal parameter estimation, to improve the image quality and contrast. The complexity of the basic GA (pixel level) has been reduced in the modified GA (feature level), by limiting the search space. It is observed from our experiments that fusion using DWT and UDWT techniques with GA for optimal parameter estimation resulted in a better fused image in the aspects of retaining the information and contrast without error, both in human perception as well as evaluation using objective metrics. The contributions of this research work are (1) reduced time and space complexity in estimating the weight values using GA for fusion (2) system is scalable for input image of any size with similar time complexity, owing to feature level GA implementation and (3) identification of source image that contributes more to the fused image, from the weight values estimated., Comment: 17 pages, 3 figures

Published
2020
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93. Features based Mammogram Image Classification using Weighted Feature Support Vector Machine

Author

Kavitha, S. and Thyagharajan, K. K.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia, Electrical Engineering and Systems Science - Image and Video Processing, I.2.6, I.2.10, I.4.6, I.4.7, I.4.10, I.5
Abstract

In the existing research of mammogram image classification, either clinical data or image features of a specific type is considered along with the supervised classifiers such as Neural Network (NN) and Support Vector Machine (SVM). This paper considers automated classification of breast tissue type as benign or malignant using Weighted Feature Support Vector Machine (WFSVM) through constructing the precomputed kernel function by assigning more weight to relevant features using the principle of maximizing deviations. Initially, MIAS dataset of mammogram images is divided into training and test set, then the preprocessing techniques such as noise removal and background removal are applied to the input images and the Region of Interest (ROI) is identified. The statistical features and texture features are extracted from the ROI and the clinical features are obtained directly from the dataset. The extracted features of the training dataset are used to construct the weighted features and precomputed linear kernel for training the WFSVM, from which the training model file is created. Using this model file the kernel matrix of test samples is classified as benign or malignant. This analysis shows that the texture features have resulted in better accuracy than the other features with WFSVM and SVM. However, the number of support vectors created in WFSVM is less than the SVM classifier., Comment: 9 pages, 3 figures, "submitted to International Conference on Computing and Communication Systems"

Published
2020
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98. Smart Driving Alert System Using MT CNN

Author

Kavitha, S., Anchitaalagammai, J. V., Murali, S., Soundarya, M., Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Chandran K R, Sarath, editor, N, Sujaudeen, editor, A, Beulah, editor, and Hamead H, Shahul, editor

Published
2023
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99. Single-Phase Bi-Directional AC/DC Converters for Fast DC Bus Voltage Controller

Author

Karpaga Priya, R., Kavitha, S., Malathi, M., Sinthia, P., Suresh Kumar, K., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rathore, Vijay Singh, editor, Piuri, Vincenzo, editor, Babo, Rosalina, editor, and Ferreira, Marta Campos, editor

Published
2023
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100. Detection of Covid-19 Using an Infrared Fever Screening System (IFSS) Based on Deep Learning Technology

Author

Muthu, V., Kavitha, S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Suma, V., editor, Lorenz, Pascal, editor, and Baig, Zubair, editor

Published
2023
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