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226 results on '"SARMA, T"'

1. Approximating Clustering for Memory Management and request processing

Author

Suribabu, D. D. D., Sarma, T. Hitendra, and Reddy, B. Eswar

Subjects
Computer Science - Databases
Abstract

Clustering is a crucial tool for analyzing data in virtually every scientific and engineering discipline. There are more scalable solutions framed to enable time and space clustering for the future large-scale data analyses. As a result, hardware and software innovations that can significantly improve data efficiency and performance of the data clustering techniques are necessary to make the future large-scale data analysis practical. This paper proposes a novel mechanism for computing bit-serial medians. We propose a novel method, two-parameter terms that enables in computation within the data arrays

Published
2023

2. A new approach for efficient clustering using fuzzy prototypes with varying neighborhoods

Author

Mrudula, K., Sarma, T. Hitendra, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Kiran Mai, C., editor, Vasundhara, D. N., editor, Siva Kumar, P. V., editor, Srujan Raju, K., editor, Mukkamala, Raghava Rao, editor, Nagini, S., editor, Brahmananda Reddy, A., editor, Ramesh, G. S., editor, Baby, V., editor, and Venkateswara Rao, P., editor

Published
2023
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3. A New CNN for Pixel Classification in Hyperspectral Images

Author

Hitendra Sarma, T., Kakarla, Syam, 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, Saraswat, Mukesh, editor, Roy, Sarbani, editor, Chowdhury, Chandreyee, editor, and Gandomi, Amir H., editor

Published
2022
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4. A Tunable Particle Swarm Size Optimization Algorithm for Feature Selection

Author

Mallenahalli, Naresh and Sarma, T. Hitendra

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Feature selection is the process of identifying statistically most relevant features to improve the predictive capabilities of the classifiers. To find the best features subsets, the population based approaches like Particle Swarm Optimization(PSO) and genetic algorithms are being widely employed. However, it is a general observation that not having right set of particles in the swarm may result in sub-optimal solutions, affecting the accuracies of classifiers. To address this issue, we propose a novel tunable swarm size approach to reconfigure the particles in a standard PSO, based on the data sets, in real time. The proposed algorithm is named as Tunable Particle Swarm Size Optimization Algorithm (TPSO). It is a wrapper based approach wherein an Alternating Decision Tree (ADT) classifier is used for identifying influential feature subset, which is further evaluated by a new objective function which integrates the Classification Accuracy (CA) with a modified F-Score, to ensure better classification accuracy over varying population sizes. Experimental studies on bench mark data sets and Wilcoxon statistical test have proved the fact that the proposed algorithm (TPSO) is efficient in identifying optimal feature subsets that improve classification accuracies of base classifiers in comparison to its standalone form., Comment: 7 pages, 1 figure, This paper is accepted for oral presentation at IEEE Congress on Evolutionary Computation (CEC) - WCCI 2018, Rio de Janerio, Brazil, #18120

Published
2018

8. Dimensionality Reduction in Hyperspectral Images Using Auto-encoders

Author

Kakarla, Syam, Gangula, Priyaranjan, Singh, C. Sai Charan, Sarma, T. Hitendra, 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, Chillarige, Raghavendra Rao, editor, Distefano, Salvatore, editor, and Rawat, Sandeep Singh, editor

Published
2020
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24. A 3D-Inception CNN for Hyperspectral Image Classification.

Author

Kanthi, Murali, Sarma, T. Hitendra, and Bindu, C. Shoba

Subjects
REMOTE sensing, CLASSIFICATION, CONVOLUTIONAL neural networks
Abstract

The classification of hyperspectral image (HSI) has attracted significant attention from the research community of remote sensing. HSI analysis suffers from overfitting due to the limited number of labelled training samples. As a result, in order to enhance the performance of the HSI classification task, a better efficient neural network architecture should be developed. To tackle this issue, this letter presents a new 3D-Inception CNN (3D-ICNN) model for dynamically extracting features by stacking inception modules in the network that can learn more representative features with fewer training samples by adopting variable spatial size convolutional filters and dynamic CNN architecture. The experimental results exhibit that the presented model can modify the network design adaptively and achieve higher classification performance. To establish the efficiency and robustness of the presented model, the experiments are conducted on the publicly available benchmark data sets and also on the new data sets. The proposed 3D-Inception CNN model obtained accuracies of 86.25% on Ahmedabad-1(AH1) dataset, 80.30% on Ahmedabad2(AH2) dataset, 99.95% on the Pavia University (PU) dataset, 99.86% on the Salinas (SA) dataset, and 99.89% on the Indian Pines (IP) dataset. [ABSTRACT FROM AUTHOR]

Published
2022
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25. A Protocol for Rapid Clonal Propagation and Microrhizome Production of Curcuma caesia Roxb. (Zingiberaceae): A Critically Endangerd Medicinal Plant of North East India.

Author

Sarma, Indrani, Deka, A. C., and Sarma, T. C.

Subjects
CURCUMA, MEDICINAL plants, ZINGIBERACEAE, PLANT regulators, ENDANGERED plants, PLANT shoots
Abstract

Background: Curcuma caesia Roxb. (Zingiberaceae) is a rare, critically endangered medicinal plant of Northeast India. The rhizome of the plant is famous for its significant medicinal properties. Methods: Various concentrations of plant growth regulators in Murashige and Skoog (MS) medium were tried in the study using rhizome bud as explant for development of an efficient cost effective protocol for in vitro mass multiplication and microrhizome induction of Curcuma caesia. Result: Shoot multiplication and plant generation was achieved from freshly sprouted shoots of Curcuma caesia on Murashige and Skoog's medium supplemented with different phytohormones. The best response for shoot multiplication (23.80±0.51shoots/explant) was obtained within 8 weeks in MS medium supplemented with BAP (1.0 mgl -1) and 2,4-D (0.25 mgl -1). Microrhizomes were induced at the base of the in vitro derived shoots upon transferred to medium containing various combinations and concentrations of sucrose and BAP and grown under varying photoperiod. Half strength of MS basal medium containing BAP (1.0 mg/l) and 9% sucrose was found to be optimum for induction of large sized microrhizome within 45 days of incubation under 16 hrs of photoperiod. [ABSTRACT FROM AUTHOR]

Published
2021
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39. Second-Hand Car Price Prediction

Author

Anil Kumar, N., Xhafa, Fatos, Series Editor, Buyya, Rajkumar, editor, Hernandez, Susanna Munoz, editor, Kovvur, Ram Mohan Rao, editor, and Sarma, T. Hitendra, editor

Published
2023
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