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Karthigai, S.
- Categorization of Lung Carcinoma using Multilayer Perceptron in Output Layer
Abstract Views :206 |
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Authors
Affiliations
1 Department of Computer Science, Erode Arts and Science College, IN
1 Department of Computer Science, Erode Arts and Science College, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 2 (2020), Pagination: 2035-2039Abstract
Data mining techniques used in many applications as there is an incredible growth in records and it is not feasible to find a solution manually. Amongst them, the medical records in data mining gains more popularity and have many missed values due to emergency cases or complicated situation etc. These missing values have a great influence in the desired output. The traditional mining procedure has to be enhanced to handle that between them and adjust the parameters to minimize the errors. The activation function in the neuron performs the non-linear transformation function making it capable to learn and perform more complex tasks. This function plays a vital role in the output process. This work focus on this function and made some enhancement by applying multi logit regression with Maximum A posteriori method in activation function to handle multi-class classification The proposed Enhanced Activation function in Multi- Layer Perceptron is implemented in Weka 3.9.6 and it is compared with traditional MLP with suitable evaluation metrics.Keywords
Data Mining, Neural Network, Multi Layer Perceptron, Multi Logit Regression, Maximum APosteriori.
References
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- Categorization of Lung Carcinoma Using Multilayer Perceptron in Output Layer
Abstract Views :226 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Erode Arts and Science College Erode, IN
1 Department of Computer Science, Erode Arts and Science College Erode, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 12, No 1 (2020), Pagination: 6-10Abstract
Data mining techniques used in many applications as there is an incredible growth in records and it is not feasible to find a solution manually. Amongst them, the medical records in data mining gains more popularity and have many missed values due to emergency cases or complicated situation etc. These missing values have a great influence in the desired output. The traditional mining procedure has to be enhanced to handle that between them and adjust the parameters to minimize the errors. The activation function in the neuron performs the non-linear transformation function making it capable to learn and perform more complex tasks. This function plays a vital role in the output process. This work focus on this function and made some enhancement by applying multi logit regression with Maximum A posteriori method in activation function to handle multi-class classification The proposed Enhanced Activation Function in Multi layer Perceptron is implemented in WEKA 3.9.6. and is compared with traditional MLP with suitable evaluation metrics.Keywords
Data Mining, Neural network, Multi Layer Perceptron, Multi logit regression, Maximum a Posteriori.- Securing Wireless Sensor Networks Using Deep Learning-Based Approach for Eliminating Data Modification in Sensor Nodes
Abstract Views :163 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, Navarasam Arts and Science College, IN
2 Department of Computer Science and Engineering, S.G. Balekundri Institute of Technology, IN
3 Department of Computer Science, PSG College of Arts and Science, IN
4 Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, IN
5 Department of Chemistry, School of Mathematics and Natural Sciences, Mukuba University, ZM
1 Department of Computer Applications, Navarasam Arts and Science College, IN
2 Department of Computer Science and Engineering, S.G. Balekundri Institute of Technology, IN
3 Department of Computer Science, PSG College of Arts and Science, IN
4 Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, IN
5 Department of Chemistry, School of Mathematics and Natural Sciences, Mukuba University, ZM
Source
ICTACT Journal on Communication Technology, Vol 14, No 2 (2023), Pagination: 2939-2944Abstract
Wireless Sensor Networks (WSNs) play a pivotal role in various domains, including environmental monitoring, surveillance, and industrial automation. However, the inherent vulnerabilities in WSNs make them susceptible to various security threats, such as data modification attacks, which can compromise the integrity and reliability of collected sensor data. To address this issue, we propose a novel approach called Residual Recurrent Transfer Learning (RRTL) to enhance the security of WSNs and eliminate data modification in sensor nodes. RRTL leverages the power of deep learning and transfer learning techniques to develop an intelligent and adaptable security framework. The proposed approach trains a deep residual recurrent neural network (RNN) model using a large dataset of normal sensor readings. This model learns the temporal patterns and dependencies in the data, enabling it to identify abnormal sensor readings that might indicate data modification attempts. To evaluate the effectiveness of RRTL, we conducted extensive experiments using a real-world WSN deployment. The results demonstrate that our approach significantly outperforms existing security mechanisms in terms of accuracy, detection rate, and false positive rate. Furthermore, RRTL exhibits robustness against adversarial attacks and dynamic environmental conditions, making it suitable for real-time applications in challenging WSN environments.Keywords
Securing, Wireless Sensor Networks, Residual Recurrent Transfer Learning, Eliminating, Data Modification, Sensor Nodes.References
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