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Thilagavathy, D.
- Scalable Contributory Key Agreement with Efficient Authentication
Abstract Views :194 |
PDF Views:2
Authors
Affiliations
1 Govt College of Engg, Thirunelveli, IN
2 Adhiyamaan College of Engg, Hosur, IN
1 Govt College of Engg, Thirunelveli, IN
2 Adhiyamaan College of Engg, Hosur, IN
Source
Networking and Communication Engineering, Vol 1, No 3 (2009), Pagination: 124-129Abstract
Many emerging network applications are based upon a group communication model. In a peer-to-peer or ad-hoc network which do not have a previously agreed upon common secret key,communication is susceptible to eavesdropping, Hence a secure distributed group key agreement is required to establish and authenticate a common group key for secure and private communication. This paper presents an authenticated distributed collaborative key agreement for dynamic peer groups. The protocol is distributed in nature in which there is no centralized key server, collaborative in nature in which the group key is contributory,dynamic in nature in which existing members may leave the group while new members may join. Instead of performing individual rekeying an interval-based approach is used. The Queue-batch algorithm used for rekeying substantially reduces the computation and communication cost. Key authentication provided focuses on security improvement.
Keywords
Authentication, Dynamic Peer Groups, Group Key Agreement, Rekeying, Secure Group Communication, Security.
- Identification of Semantic Relation for Disease-Treatment Using Machine Learning Approach
Abstract Views :218 |
PDF Views:1
Authors
P. Menaka
1,
D. Thilagavathy
1
Affiliations
1 Department of Computer Science and Engineering, Adhiyamman College of Engineering, Hosur, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Adhiyamman College of Engineering, Hosur, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 4 (2012), Pagination: 155-158Abstract
The Machine Learning (ML) is almost used in any domain of research and now it has become a reliable tool in the medical domain.ML is a tool by which medical field is integrated with the computer based systems to provide more efficient medical care. The main objective of this work is to show what Natural Language Processing (NLP) and Machine Learning (ML) techniques used for representation of information and what classification algorithms are suitable for identifying and classifying relevant medical information in short texts. It is difficult task to identify the informative sentences in fields such as summarization and information extraction. The work and contribution value with this task is helpful in results and in settings for this task in healthcare field. It provides classification of disease, its cure and prevention. It acknowledges the fact that tools capable of identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-to-date with the latest discoveries. In this research, it focuses on diseases and treatment information, and the relation that exists between these two entities.Keywords
Machine Learning, Classification, NLP.- Hybrid Deep Learning with Alexnet Feature Extraction and Unset Classification for Early Detection in Leaf Diseases
Abstract Views :59 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Engineering, UPL University of Sustainable Technology, IN
2 Department of Information Technology, Adhiyamaan College of Engineering, IN
3 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra Agriculture University, IN
1 Department of Computer Engineering, UPL University of Sustainable Technology, IN
2 Department of Information Technology, Adhiyamaan College of Engineering, IN
3 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra Agriculture University, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 3 (2024), Pagination: 3255-3262Abstract
This study addresses the imperative need for early detection of leaf diseases in tobacco, pepper, and tomato plants, as these diseases significantly impact crop yield and quality. Existing methods often fall short in accurately identifying diseases across diverse plant species. The research aims to bridge this gap by proposing a hybrid deep learning approach, combining the robust feature extraction capabilities of AlexNet with the precise segmentation and classification prowess of UNet. The proposed hybrid model leverages AlexNet proficiency in extracting hierarchical features from plant leaf images and subsequently utilizes UNet for accurate disease classification. This synergistic combination enables the model to overcome the challenges posed by the varied morphologies of tobacco, pepper, and tomato leaves. Experimental results demonstrate the effectiveness of the proposed methodology, showcasing superior performance in terms of accuracy, sensitivity, and specificity compared to existing techniques. The hybrid deep learning approach exhibits promising potential for early and accurate detection of leaf diseases, contributing to sustainable crop management practices.Keywords
Leaf Disease Detection, Hybrid Deep Learning, AlexNet, UNet, Agriculture.References
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- K. Lee and K. Hong, “An Implementation of Leaf Recognition System using Leaf Vein and Shape”, International Journal of Bio- Science and Bio- Technology, Vol. 5, No. 2, pp. 57-66, 2013.
- V. Pooja and V. Kanchana, “Identification of Plant Leaf Diseases using Image Processing Techniques”, Proceedings of IEEE Technological Innovations in ICT for Agriculture and Rural Development, pp. 130-133, 2017.
- N. Krithika and A.G. Selvarani, “An Individual Grape Leaf Disease Identification using Leaf Skeletons and KNN Classification”, Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-5, 2017.
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- T. Munisami, M. Ramsurn, S. Kishnah and S. Pudaruth, “Plant Leaf Recognition using Shape Features and Colour Histogram with K-Nearest Neighbour Classifiers”, Procedia Computer Science, Vol. 58, pp. 740-747, 2015.
- Hidenori Ide and Takio Kurita, “Improvement of Learning for CNN with ReLU Activation by Sparse Regularization”, Proceedings of International Conference on Neural Networks, 2017.
- Sneha Adhikari, “Identification of QTL for Banded Leaf and Sheath Blight in Teosinte-Derived Maize Population”, Agricultural Research, Vol. 65, pp. 1-9, 2021.
- Ian Goodfellow, YoshuaBengio and Aaron Courville, “Deep Learning”, MIT press, 2016.
- P. Ferentinos, Konstantinos, “Deep Learning Models for Plant Disease Detection and Diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018.
- D.R. Smith and D. G. White, “Diseases of Corn”, Corn and Corn Improvement, Vol. 18, pp. 687-766, 1988.
- Karen Simonyan and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of International Conference on Machine Learning, pp. 1-9, 2014.
- Haiguang Wang, “Application of Neural Networks to Image Recognition of Plant Diseases”, Proceedings of International Conference on Systems and Informatics, pp. 1-13, 2012.