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Karunambiga, K.
- Fuzzy Based Optimization for Improving the Trust Score in Manets
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Authors
Affiliations
1 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
3 Department of Information Technology, Sri Ramakrishna Engineering College, India., IN
4 Department of Computer Science and Engineering, Sona College of Technology, India., IN
1 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
3 Department of Information Technology, Sri Ramakrishna Engineering College, India., IN
4 Department of Computer Science and Engineering, Sona College of Technology, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2854-2860Abstract
In this paper, research develop a method for identifying abnormal behavior based on two inputs: the trustworthiness of the user, as well as the reliability of the recommendations that they make. Specifically, research look at the reliability of the user recommendations. The next thing that needs to be done is to calculate the node general trust value in order to determine if there has been any kind of malicious attack. This will show whether or not the node has been compromised in any way. It is conceivable that this could lessen the amount of power that is needed for the communication that takes place between different networks. Additionally, it demonstrates that the model is better able to utilize the evaluation results of the common neighbor nodes to synthesize the confidence value when fewer nodes are deployed in the network. This is demonstrated by the fact that fewer nodes are deployed in the network. The reliability of the trust assessment improves while the number of trusts for which recommendations are made decreases.Keywords
Fuzzy Optimization, Trust, Score, MANETs, Direct Trust
References
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- An Enhanced Ensemble Hybrid Deep Learning Algorithm For Improving the Accuracy in Iris Segmentation
Abstract Views :87 |
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Authors
Affiliations
1 1Department of Information Technology, Karpagam Institute of Technology, India., IN
2 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
3 DVR and Dr. HS MIC College of Technology, India., IN
4 Department of Computer Science and Engineering, PSV College of Engineering and Technology, India., IN
1 1Department of Information Technology, Karpagam Institute of Technology, India., IN
2 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
3 DVR and Dr. HS MIC College of Technology, India., IN
4 Department of Computer Science and Engineering, PSV College of Engineering and Technology, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2947-2952Abstract
In recent years, there has been a meteoric rise in the application of deep neural networks for the purpose of iris segmentation. This can be attributed to the extraordinary capacity for learning possessed by the convolution kernels that are utilised by CNNs. Conventional methods have several drawbacks, some of which can be partially compensated for by using CNN-based algorithms, which increase the segmentation precision. On the other hand, the CNN-based iris segmentation approaches that are currently in use typically require a more complex network, which results in an increase in the number of parameters. This is essential to realise a higher degree of precision in the results. CNN-based techniques are effective, they can only be used for a specific application. This makes them inappropriate for general iris segmentation goals, even though they are effective.Keywords
Ensemble Model, Deep Learning, Iris Segmentation.References
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