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Priya, K.
- Hypoglycemic and Hypocholesterolemic Effect of Noni (Morinda citrifolia) on Female NIDDM Subjects
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Authors
Affiliations
1 Postgraduate Department of Home Science, Queen Mary's College, Chennai-600 004, IN
1 Postgraduate Department of Home Science, Queen Mary's College, Chennai-600 004, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 45, No 11 (2008), Pagination: 479-483Abstract
Diabetes mellitus is emerging as one of the main threats to human health. The prevalence of this condition and its mortality rate has increased in ieaps and bounds from 5 per cent to 20 per cent. India Is reported to be the capital for the condition of diabetes mellitus. Recent researches have Implied that diabetes mellitus Is the second cause for death. Almost 90 per cent of diabetics In India are NIDDM'. Diabetes mellitus tends to lower 'good' cholesterol and raise triglyceride and 'bad' cholesterol levels, which increases the risk for heart disease. It causes risk factors such as obesity, high blood pressure, stroke and abnormal cholesterol.- Enhanced Image Secret Sharing Through Video Using Two Decoding Option
Abstract Views :202 |
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Authors
Affiliations
1 Sri Ramakrishna Engineering College, Coimbatore, IN
2 Sri Ramakrishna Institute of Technology, Coimbatore, IN
3 Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, IN
1 Sri Ramakrishna Engineering College, Coimbatore, IN
2 Sri Ramakrishna Institute of Technology, Coimbatore, IN
3 Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, IN
Source
Automation and Autonomous Systems, Vol 6, No 2 (2014), Pagination: 66-70Abstract
VCS is one of the recent emerging areas in image secret sharing, where one can easily encrypt and share the image in secure way and which can be decrypted through the human visual system without computation. In this project, this scheme is implemented for hiding image in a video stream using two in one image secret sharing scheme (TiOISSS) with two decoding options. It is based on Visual Cryptography Scheme (VCS) and Polynomial based image secret sharing scheme (PISSS) known as Newton‟s raphson method and comparing the same with already existing Lagrange‟s interpolation method. This comparison is made to show the reduction in file size of shadow image for faster transmission within a distributed multimedia system.Keywords
Secret Sharing, Visual Cryptography Scheme (VCS), Polynomial-based Image Secret Sharing Scheme (PISSS) – Lagrange‟s Interpolation and Newton-Raphson‟s Method.- Key Factors Influencing Parental Choice of School for their Children in Namakkal District
Abstract Views :380 |
PDF Views:326
Authors
K. Priya
1
Affiliations
1 Department of Commerce, Vivekanandha College of Arts & Sciences (Autonomous), Tiruchengode, Namakkal, Tamilnadu, IN
1 Department of Commerce, Vivekanandha College of Arts & Sciences (Autonomous), Tiruchengode, Namakkal, Tamilnadu, IN
Source
HuSS: International Journal of Research in Humanities and Social Sciences, Vol 5, No 2 (2018), Pagination: 85-91Abstract
India is currently the world’s youngest country and one of the fastest growing economies. Education is highly acclaimed as the most essential pre-requisite for human development all over the world. Parents are the primary caregivers of their children and have the responsibility of educating the children in a school of their choice. This study attempts to find out the factors influencing parental choice of school for their children. The study has disclosed that the common reasons attributed by the parents for sending their wards to government aided or unaided schools irrespective of their choice are good infrastructure, availability of trained and experienced teachers, integrity of teachers, good parent-teacher relationship and the factors relating to discipline.Keywords
Parents’ Perception, Quality, Teacher-Student Relationship.References
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- Yi Hsu and Chen Yuan-Fang. An analysis of factors affecting parents’ choice of a Junior High School. International Journal of Business, Humanities and Technology. 2010 Feb; 3(2): 39–49.
- Williams B, Onsman A, Brown T. Exploratory factor analysis: A five step guide for novices. Journal of Emergency Primary Health Care (JEPHC). 2010; 8(3):1–13. Article 990399.
- Hair J, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. 4th Ed. New Jersy: Prentice Hall Inc. 1995; p. 373. PMCid:PMC2549635
- Tabachnick BG, Fidell LS. Using multivariate statistics. Boston: Pearson Education Inc. 2007; p. 611.
- Yi Hsu and Chen Yuan-Fang. op. cit. p. 43–4.
- A Concise Chronological Reassess of Different Swarm Intelligence Methods with Multi Robotics Approach
Abstract Views :371 |
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Authors
K. Priya
1
Affiliations
1 Department of Information Science and Engineering, MVJ College of Engineering, IN
1 Department of Information Science and Engineering, MVJ College of Engineering, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No SP 2 (2019), Pagination: 1874-1879Abstract
Swarm insight is the discipline that arrangements with normal and fake frameworks made out of numerous people that facilitate utilizing decentralized control and self-association. Specifically, the order focuses on the collective behaviours that outcome from the nearby cooperation’s of the people with one another and with their environment. We can discover swarm in provinces of ants, school of fishes, herds of feathered creatures and so on. The different Swarm Intelligence models, for example, the Ant Colony Optimization where it depicts about the development of ants, their conduct, and how do it conquer the impediments, in fowls we see about the Particle swarm advancement it depends on the swarm knowledge and how the positions must be put in view of the standards. Next is the Bee state streamlining that arrangements with the conduct of the honey bees, their associations, likewise portrays about the Movement and how they function as developing aggregate knowledge of gatherings of basic self-governing operators. As a new research territory by which swarm knowledge is connected to multi-robot frameworks; swarm mechanical technology thinks about how to facilitate extensive gatherings of generally straightforward robots using neighbourhood rules. It centers on concentrate the plan of huge measure of generally basic robots, their physical bodies and their controlling practices. Since its presentation in 2000, a few fruitful experimentations had been acknowledged, and till now more tasks are under examinations. This paper tries to give a review of this space look into for the aim to orientate the readers, particularly the individuals who are recently coming to this research field.Keywords
Pheromone, Stigmergy, Particle Swarm Optimization, Ant Colony Optimization, Bee Colony Optimization.References
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- B.K. Panigrahi, Y. Shi and M.H. Lim, “Adaptation, Learning, and Optimization”, Springer, 2009.
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- A.E. Turgut , H. Celikkanat, F. Gokce and E. Sahin “Self-Organized Flocking in Mobile Robot Swarms”, Swarm Intelligence, Vol. 2, No. 2, pp. 97-120, 2008.
- B. Grob and M. Dorigo, “Evolution of Solitary and Group Transport Behaviors for Autonomous Robots Capable of Self-Assembling”, Adaptive Behavior, Vol. 16, No. 5, pp. 285-305, 2008.
- S. Garnier, “Aggregation Behaviour as a Source of Collective Decision in a Group of Cockroach-like Robots”, Proceedings of International Conference on Advances in Artificial Life, pp. 169-178, 2005.
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- Krishna H. Hingrajiya, Ravindra Kumar Gupta and Gajendra Singh Chandel, “An Ant Colony Optimization Algorithm for Solving Travelling Salesman Problem”, International Journal of Scientific and Research Publications, Vol. 2, No. 8, pp. 1-6, 2012.
- Haitham Bou Ammar, Karl Tuyls and Michael Kaisers, “Evolutionary Dynamics of Ant Colony Optimization”, Proceedings of German Conference on Multiagent System Technologies, pp. 40-52, 2012.
- Yassiah Bissiri, W. Scott Dunbar and Allan Hall, “Swarm based Truck-Shovel Dispatching System in Open Pit Mine Operations”, Master Thesis, Department of Mining and Mineral Process Engineering, University of British Columbia, 2001.
- M. Beekman, G.A. Sword and S.J. Simpson, “Biological Foundations of Swarm Intelligence”, Swarm Intelligence, 2008.
- Bijaya Ketan Panigrahi et al., “Handbook of Swarm Intelligence Concepts, Principles and Applications”, Springer, 2011.
- M. Fleischer, “Foundations of Swarm Intelligence: From Principles to Practice”, Available at: https://arxiv.org/pdf/nlin/0502003.pdf.
- T. Ying, “Research Advance in Swarm Robotics”, Defence Technology, Vol. 9, No. 1, pp. 18-39, 2013.
- J.C.S. Amanda, “Swarm Robotics and Minimalism”, Connection Science, Vol. 19, No. 3, pp. 245-260, 2017.
- Hyperspectral And Multispectral Image Fusion Using Fully Constrained Nonlinear Coupled Nonnegative Matrix Factorization
Abstract Views :153 |
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Authors
K. Priya
1,
K.K. Rajkumar
1
Affiliations
1 Department of Information Technology, Kannur University, IN
1 Department of Information Technology, Kannur University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2644-2649Abstract
Hyperspectral images (HSI) have a wide range of spectral information compared to conventional images. This rich spectral information leads to store more information about the image. Even though the hyperspectral images have multiple spectrum bands that makes narrow division of each spectral band in the image. This narrow band division reduces the spatial quality of HSI and hence it necessitates the improvement of the spatial quality of the hyperspectral image. One of the most emerging methods to improve or enhance the hyperspectral image quality is the HS-MS image fusion. Most of the existing image fusion methods neglects the nonlinear data associated with the image. To overcome this limitation, we proposed a nonlinear unmixing-based fusion model, namely Fully Constrained Nonlinear-CNMF (FCNCNMF) by consider the nonlinearity data associated with the image. To improve the performance of our nonlinear unmixing-based fusion method, we imposed certain constraints on both spectral and spatial data. The constraints include minimum volume simplex with spectral data and total variance and sparsity with spatial data to enhance the quality of the image. We applied all these constraints to both hyperspectral and multispectral images and then fused these data to obtain the final high-quality image. The fused image’s quality is measured using five standard quality measures on four benchmark datasets and found that the proposed method shows superiority over all baseline methods.Keywords
Hyperspectral Image, Nonlinearity, Spectral Unmixing, Spectral Image FusionReferences
- Ting Xu, Ting Zhu Huang, Liang Jian Deng, XiLe Zhao and Jie Huang, “Hyperspectral Image Superresolution using Unidirectional Total Variation with Tucker Decomposition”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, pp. 4381-4398, 2020.
- Renwei Dian, Shutao Li, Leyuan Fang and Qi Wei. “Multispectral and Hyperspectral Image Fusion with Spatial-Spectral Sparse Representation”, Information Fusion, Vol. 49, pp. 262-270, 2019.
- Naoto Yokoya, Claas Grohnfeldt and Jocelyn Chanussot. “Hyperspectral and Multispectral Data Fusion: A Comparative Review of the Recent Literature”, IEEE Geoscience and Remote Sensing Magazine, Vol. 5, No. 2, pp. 29-56, 2017.
- Renwei Dian, Shutao Li, Leyuan Fang, Ting Lu and Jose M. Bioucas-Dias, “Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Image Fusion”, IEEE Transactions on Cybernetics, Vol. 50, No. 10, pp. 4469-4480, 2019.
- Xuelong Li, Yue Yuan and Qi Wang, “Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 1, pp. 550-562, 2020.
- Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot and Xiao Xiang Zhu, “An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing”, IEEE Transactions on Image Processing, Vol. 28, No. 4, pp. 1923-1938, 2019.
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- Xinyu Zhou, Ye Zhang, Junping Zhang and Shaoqi Shi, “Alternating Direction Iterative Nonnegative Matrix Factorization Unmixing for Multispectral and Hyperspectral Data Fusion”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, pp. 52235232, 2020.
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- C.H. Lin, F. Ma, C.Y. Chi and C.H. Hsieh, “A Convex Optimization-Based Coupled Nonnegative Matrix Factorization Algorithm for Hyperspectral and Multispectral Data Fusion”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 3, pp. 1652-1667, 2018.
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- Ricardo Augusto Borsoi, Tales Imbiriba and Jose Carlos Moreira Bermudez, “Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability”, IEEE Transactions on Image Processing, Vol. 19, pp. 116-127, 2019.
- Feixia Yang, Ziliang Ping, Fei Ma and Yanwei Wang, “Fusion of Hyperspectral and Multispectral Images With Sparse and Proximal Regularization”, IEEE Access, Vol. 7, pp. 891-899, 2019.
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- Naoto Yokoya, Takehisa Yairi and Akira Iwasaki, “Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 2, pp. 528-537, 2012.
- Li Sun, Kang Zhao and Ziwen Liu, “Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization”, IEEE Access, Vol. 7, pp. 171023-171031, 2019.
- K. Priya and K.K. Rajkumar, “Multiplicative Iterative Nonlinear Constrained Coupled Non-negative Matrix Factorization (MINC-CNMF) for Hyperspectral and Multispectral Image Fusion”, International Journal of Advanced Computer Science and Applications, Vol. 9, No. 1, pp. 1-23, 2021.
- Dataset for Classification, Available at http://lesun.weebly.com/hyperspectral-data-set.html, Accessed at 2021.