Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Karthigai, S.
- Categorization of Lung Carcinoma using Multilayer Perceptron in Output Layer
Abstract Views :163 |
PDF Views:0
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
- K. Akilandeswari and R. Uma Rani, “Weight Optimization of Multilayer Perceptron Neural Network using Hybrid PSO for Improved Brain Computer Interface Data Classification”, International Journal of Computational Intelligence and Informatics, Vol. 6, No. 4, pp. 1-12, 2017.
- R. Bala Krishnan .and N.R. Raajan, “An Enhanced Multilayer Perceptron Based Approach for Efficient Intrusion Detection System”, International Journal of Pharmacy and Technology, Vol. 8, No. 4, pp. 23139-23156, 2016.
- R. Beale and T. Jackson, “Neural Computing: An Introduction”, CRC Press, 1990.
- Divya Tomar and Sonali Agarwal, “A Survey on Pre Processing and Post Processing Technique in Mining”, International Journal of Database Theory and Applications, Vol. 7, No. 4, pp. 21-34, 2014.
- Dong Xiao, Beijing Li and Yachun Mao, “A Multiple Hidden Layers Extreme Learning Machine Method and Its Application”, Mathematical Problems in Engineering, Vol. 2017, pp. 1-10, 2017.
- Foram S. Panchal and Mahesh Panchal, “Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network”, International Journal of Computer Science and Mobile Computing, Vol. 3, No. 11, pp. 455-464, 2014.
- G.D. Garson, “Statnotes: Topics in Multivariate Analysis”, Available at: https://faculty.chass.ncsu.edu/garson/PA765/statnote.htm.
- Jiawei Han and Michelin Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, 2000.
- Rattanawadee Panthong and Anongnart Srivihok, “Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm”, Procedia Computer Science, Vol. 72, pp. 162-169, 2015.
- Sonali B. Wankhede, “Analytical Study of Neural Network Techniques: SOM, MLP and Classifier-A Survey”, IOSR Journal of Computer Engineering, Vol. 16, No. 3, pp. 86-92, 2014.
- R. Vidya, V. Latha and S. Venkatesan, “Mining Lung Cancer Data for Smokers and Non-Smokers by using Data Mining Techniques”, International Journal of Trend in Research and Development, Vol. 3, No. 7, pp. 7622-7626, 2016.
- Categorization of Lung Carcinoma Using Multilayer Perceptron in Output Layer
Abstract Views :165 |
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 :108 |
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
- Shubhanshi Rathore, Rajeev Paulus, A.K. Jaiswal and Aditi Agarwal, “Analysis of QOS and Energy Consumption in IEEE 802.15.4/ZigBee Wireless Sensor Network”, International Journal of Computer Applications, Vol. 121, No. 17, pp. 40-43, 2015.
- Amritpal Kaur, Jaswinder Kaur and Gurjeevan Singh, “An Efficient Hybrid Topology Construction in Zigbee Sensor Network”, Proceedings of IEEE International Conference on Recent Advances and Innovations in Engineering, pp. 1-6, 2014.
- W. Osamy, A. A. El-Sawy and A. Salim, “CSOCA: Chicken Swarm Optimization based Clustering Algorithm for Wireless Sensor Networks”, IEEE Access, Vol. 8, pp. 60676-60688, 2020.
- X. Zhao, H. Zhu, S. Aleksic and Q. Gao, “Energy-Efficient Routing Protocol for Wireless Sensor Networks based on Improved Grey Wolf Optimizer”, KSII Transactions on Internet and Information Systems, Vol. 12, No. 6, pp. 2644-2657, 2018.
- A. Junpei, B. Leonard, X. Fatos and A. Durresi, “A Cluster Head Selection Method for Wireless Sensor Networks based on Fuzzy Logic”, Proceedings of IEEE International Conference on Region 10, pp. 1-4, 2007.
- S.A. Sert, A. Alchihabi and A. Yazici, “A Two-Tier Distributed Fuzzy Logic based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks”, IEEE Transactions on Fuzzy Systems, Vol. 26, No. 6, pp. 3615-3629, 2018.
- P.S. Mehra, M.N. Doja and B. Alam, “Fuzzy based Enhanced Custer Head Selection (FBECS) for WSN”, Journal of King Saud University Science, Vol. 89, No. 1, pp. 1-15, 2018.
- Syed Muhammad Sajjada, Safdar Hussain Boukb and Muhammad Yousafa, “Neighbor Node Trust Based Intrusion Detection System for WSN”, Proceedings of International Conference on Emerging Ubiquitous Systems and Pervasive Networks, pp. 183-188, 2015.
- Xinying Yu, Fengyin Li, Tao Li, Nan Wu, Hua Wang and Huiyu Zhou, “Trust‑Based Secure Directed Diffusion Routing protocol in WSN”, Journal of Ambient Intelligence and Humanized Computing, Vol. 43, pp. 1-13, 2020.
- Jitendra Kurmi, Ram Singar Verma and Sarita Soni, “An Efficient and Reliable Methodology for Wormhole Attack Detection in Wireless Sensor Network”, Advances in Computational Sciences and Technology, Vol. 10, No. 5, pp. 1129-1138, 2017.
- I. Berin Jeba Jingle and J. Jeya A. Celin, “Mining Optimized Positive and Negative Association Rule using Advance ABC Algorithm”, Journal of Theoretical and Applied Information Technology, Vol. 95, No. 24, pp. 6846-6855, 2017.
- Karaboga, Dervis, and Bahriye Basturk, “A Powerful and Efficient Algorithm for Numerical Function Optimization: artificial Bee Colony (ABC) Algorithm”, Journal of Global Optimization, Vol. 39, No. 3, pp: 459-471, 2007.
- Soobin Lee and Howon Lee, “Energy-Efficient Data Gathering Scheme Based on Broadcast Transmissions in Wireless Sensor Networks”, The Scientific World Journal, Vol. 2013, pp. 1-17, 2013.
- Shouling Ji, Raheem Beyah and Zhipeng Cai, “Snapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 13, No. 3, pp. 626-637, 2014.