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S. S. Gantayat

Bio: S. S. Gantayat is an academic researcher from GMR Institute of Technology. The author has contributed to research in topics: Fuzzy logic & Missing data. The author has an hindex of 3, co-authored 15 publications receiving 35 citations.

Papers
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Book ChapterDOI
01 Jan 2014
TL;DR: There are many techniques to overcome the imperfect knowledge and manage data with incomplete items, but no one is absolutely better than the others.
Abstract: Incomplete data are questions without answers or variables without observations. Even a small percentage of missing data can cause serious problems with the analysis leading to draw wrong conclusions and imperfect knowledge. There are many techniques to overcome the imperfect knowledge and manage data with incomplete items, but no one is absolutely better than the others.

13 citations

Journal ArticleDOI
TL;DR: In this article , a local binary pattern-based reversible data hiding (LBP-RDH) technique has been proposed to maintain a fair symmetry between the perceptual transparency and hiding capacity.

12 citations

Journal ArticleDOI
01 Dec 2021
TL;DR: Wang et al. as discussed by the authors proposed an improved reversible data hiding (RDH) technique that offers larger capacity, better stego-image (SI) quality and higher attack survival ability (ASA).
Abstract: Image steganographic communication demands a fair trade-off among the three diametrically opposed metrics such as higher capacity, larger visual quality, and attack survival ability (ASA). Recently, some reversible data hiding (RDH) techniques using dual images have shown promising results to achieve the aforementioned needs. However, maintaining a balance among these metrics is still an open challenge. In this paper, using the concept of shadow image, which is basically the replica of the cover image (CI) and performing some simple addition and subtraction logic on the shadow image pixels, we propose an improved RDH technique that offers larger capacity, better stego-image (SI) quality and higher ASA. At first, during embedding, three shadow images of the CI are produced. Then, the shadow image pixels are adjusted based on their XOR features of the least significant bit planes. After embedding the secret bits, a maximum of ± 1 modification has been observed in the SI pixels. Later at the receiving end, the CI has been restored by applying the round function on the obtained SI pixels. Experimental results show that the proposed technique offers excellent visual quality with peak signal-to-noise ratio and structural similarity index (SSIM) of 52.47 dB, 53.91 dB, 52.48 dB and 0.9974, 0.9981, 0.9974 for the respective shadow images. Further, the proposed technique show exceptional anti-steganalysis ability to regular and singular analysis, pixel difference histogram analysis, and bit pair analysis. Additionally, the proposed technique successfully avoids the falling-off boundary problem.

7 citations

Journal ArticleDOI
TL;DR: The map operator on a list, define list indexing the cons operator and establish their properties and parallel notions and properties for fuzzy lists are considered.

4 citations


Cited by
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Journal ArticleDOI

3,152 citations

Journal ArticleDOI
TL;DR: A systematic review of complex fuzzy sets and logic is conducted to provide a framework to position new research in the field, consolidate the available theoretical results, catalogue the current applications, and identify the key open questions facing researchers in this area.

152 citations

Journal ArticleDOI
27 Jun 2016
TL;DR: Why machine learning methods are underutilized in natural science is discussed, and solutions for barriers preventing wider ML adoption are proposed.
Abstract: The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

117 citations

Journal ArticleDOI
TL;DR: KeywoRDS Ant Colony Optimization, Artificial Bee Colony, Cancer, Diabetes, Disease Diagnosis, Genetic Algorithm, Heart Disease, Nature Inspired Techniques, Particle Swarm Optimization.
Abstract: Genetic Algorithms GA, Ant Colony Optimization ACO, Particle Swarm Optimization PSO and Artificial Bee Colonies ABC are some vital nature inspired computing NIC techniques. These approaches have be...

30 citations