Author
Jaya Sil
Other affiliations: University of Calcutta, National University of Singapore
Bio: Jaya Sil is an academic researcher from Indian Institute of Engineering Science and Technology, Shibpur. The author has contributed to research in topics: Rough set & Feature selection. The author has an hindex of 16, co-authored 169 publications receiving 1292 citations. Previous affiliations of Jaya Sil include University of Calcutta & National University of Singapore.
Papers published on a yearly basis
Papers
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01 Dec 2008
TL;DR: A software prototype system for rice disease detection based on the infected images of various rice plants is described, which is both image processing and soft computing technique applied on number of diseased rice plants.
Abstract: The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants.
220 citations
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TL;DR: A rule base classifier has been built that cover all the diseased rice plant images and provides superior result compare to traditional classifiers.
165 citations
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TL;DR: An algorithm based on the concept of type-2 fuzzy sets to handle uncertainties that automatically selects the threshold values needed to segment the gradient image using classical Canny's edge detection algorithm is proposed.
103 citations
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TL;DR: A collusion resilient optimized spread spectrum (SS) image watermarking scheme using genetic algorithms (GA) and multiband (M-band) wavelets and Fuzzy logic is used to classify decision magnitudes in multiple group combined interference cancelation used in the intermediate stage(s).
47 citations
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TL;DR: Development of an efficient real time intrusion detection system (IDS) has been proposed in the paper by integrating Q-learning algorithm and rough set theory (RST), which demonstrates superior performance compared to other classifiers.
43 citations
Cited by
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2,345 citations
01 Mar 2001
TL;DR: Using singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype.
Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
1,815 citations
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TL;DR: A novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques, trained to identify 10 common rice diseases with much higher accuracy than conventional machine learning model.
593 citations
01 Dec 2004
TL;DR: In this article, a novel technique for detecting salient regions in an image is described, which is a generalization to affine invariance of the method introduced by Kadir and Brady.
Abstract: In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.
501 citations