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Vanitha, N.
- Investigation of Deep Learning Optimizers for False Window Size Injection Attack Detection in Unmanned Aerial Vehicle Network Architecture
Abstract Views :278 |
PDF Views:1
Authors
N. Vanitha
1,
G. Padmavathi
1
Affiliations
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Communication Technology, Vol 12, No 3 (2021), Pagination: 2465-2470Abstract
The Unmanned Aerial Vehicle (UAV) network plays a prominent role in this pandemic era. Nowadays UAVs are applied in various applications like military, civil etc. This article works on the Search and Rescue application part. UAV networks are applied in search and rescue operations in order to find the missing people in Hill areas. Due to false data dissemination attacks some UAVs in the network will lost the data so the rescue will become an issue. In order to detect those attacks this work uses Feed Forward Neural network with back propagation algorithm. This work experiments chosen optimizers to get the accurate detection of attack and compares the results among the optimizers All the more explicitly this examination did in the Delay- Tolerant based Decentralized Multi-Layer UAV ad-hoc organization Assisting VANET (DDMUAV) design utilizing Opportunistic Network Environment (ONE) test system.Keywords
Unmanned Aerial Vehicle, Delay Tolerant, Neural Network, Optimizer, Simulation.
References
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- Yirui Wu, Dabao Wei and Jun Feng, “Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey”, Security and Communication Networks, Vol. 2020, pp. 1-18, 2020.
- N. Vanitha and G. Padmavathi, “A Study on Various Cyber-Attacks and their Classification in UAV Assisted Vehicular Ad-Hoc Networks”, Proceedings of International Conference on Computational Intelligence, Cyber Security and Computational Models, pp. 1-13, 2018.
- N. Vanitha and P. Ganapathi, “Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN)”, Proceedings of International Conference on Research on Machine and Deep Learning Applications for Cyber Security, pp. 219-244, 2020.
- A Study on Deep Learning Methods for Skin Disease Classification
Abstract Views :83 |
PDF Views:0
Authors
N. Vanitha
1,
M. Geetha
1
Affiliations
1 Department of Information Technology, Dr.N.G. P Arts and Science College Coimbatore, India, IN
1 Department of Information Technology, Dr.N.G. P Arts and Science College Coimbatore, India, IN
Source
Digital Image Processing, Vol 13, No 1 (2021), Pagination: 6-9Abstract
Dermatological disorders are one among the foremost widespread diseases within the world. Despite being common its diagnosis is extremely difficult due to its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision-based techniques (deep learning) to automatically predict the varied sorts of skin diseases. The system makes use of deep learning technology to coach itself with the varied skin images. the most objective of this technique is to realize maximum accuracy of disease of the skin prediction. The people health quite the other diseases. Skin diseases are mostly caused by mycosis, bacteria, allergy, or viruses, etc. The lasers advancement and Photonics based medical technology is employed in diagnosis of the skin diseases quickly and accurately. The medical equipment for such diagnosis is restricted and costliest. So, Deep learning techniques helps in detection of disease of the skin at an initial stage. The feature extraction plays a key role in classification of skin diseases. The usage of Deep Learning algorithms has reduced the necessity for human labor, like manual feature extraction and data reconstruction for classification purposeKeywords
Disease of the Skin, Deep Learning, Types, Significance.References
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- Analysis of Machine Learning Techniques for Breast Cancer Prediction
Abstract Views :90 |
PDF Views:0
Authors
N. Vanitha
1,
R. Srimathi
1
Affiliations
1 Department of Information Technology, N.G.P Arts and Science College Coimbatore, IN
1 Department of Information Technology, N.G.P Arts and Science College Coimbatore, IN
Source
Digital Image Processing, Vol 13, No 1 (2021), Pagination: 10-14Abstract
The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support Vector Machine (SVM), K-closest neighbours, irregular woodlands, fake/artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.Keywords
Breast Cancer, Prediction, Machine Learning.References
- Naveen, Dr. K Sharma (2019), Effective breast cancer prediction using ensemble machine learning model, International Conference on Recent Trends on Electronics, Information, Communication &technology.
- Ebru aydindag, Bayrak, Pinar kirchi (2019) Comparison of machine learning methods for breast cancer Diagnosis978-1 7281-1013.
- Dhanya irenic rose Perl, Sai Sindhu, Madhumathi, Siva Kumar (2019) A Comparative study of breast cancer prediction using machine learning and feature selection, conference on intelligent computing and control system, Amrita Vishwa Vidyapeetham, Amritapuri, India.
- Amarna, ikram Gagauz Meriam (2018) Breast cancer classification using machine learning LRDSI laboratory, University of Blida 1, Blida, Algeria 1.
- Tanishk Thomas, Nitesh, Pradhan, (2020), comparative analysis to predict breast cancer using machine learning algorithm, conference on inventive computational technology, Manipal university Jaipur.
- Gupta, P., and P. S. “analysis of Machine earning techniques for Breast Cancer Prediction”. International Journal of Engineering and Computer Science, Vol. 7, no. 05, May 2018, pp. 23891-5, http://www.ijecs.in/index.php/ijecs/article/view/4071.
- S. Mythili and A. V. S. Kumar, "CTCHABC- hybrid online sequential fuzzy Extreme Kernel learning method for detection of Breast Cancer with hierarchical Artificial Bee," 2015 IEEE International Advance Computing Conference (IACC), Bangalore, 2015, pp. 343-348.