Refine your search
Collections
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
Britto Ramesh Kumar, S.
- Extending Social Privacy Protector for Twitter Social Network
Abstract Views :158 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, St. Joseph's College, Trichy-620002, IN
2 Department of Information Technology, St. Joseph's College, Trichy, IN
3 Department of Computer Science, St. Joseph's College, Trichy, IN
1 Department of Computer Science, St. Joseph's College, Trichy-620002, IN
2 Department of Information Technology, St. Joseph's College, Trichy, IN
3 Department of Computer Science, St. Joseph's College, Trichy, IN
Source
Networking and Communication Engineering, Vol 5, No 1 (2013), Pagination: 27-32Abstract
Social Networking Services provide an online service platform that focuses and facilitates sharing their interest of day to day events. Today it has been a space for professionals, religious leaders, politicians, students and likeminded groups to propagate and share their views. A recent survey estimated that 85% of internet users use online social network sites such as Twitter, Linkedln and google+ and Facebook. With all their merits and appreciation, social networks face a number of problems such as scalability, reliability, security like spam, phishing and malware, account sent spam and account hijacking or hacking the password. Hence, protecting the social networks is the major concern of the world community and therefore this paper specifically focuses on privacy. The Facebook is depending on default privacy settings and Twitter is in Public setting. Since Twitter is in public setting, providing privacy is essential and it is the need of time. This paper attempts to improve privacy settings in term of restricted uses for twitter Social Network. To substantiate, the questionnaires are prepared and survey has made for the twitter users. The observations and results are discussed.Keywords
Face Book, Privacy, Profiles, Social Network Analysis, Social Network Security, Twitter.
- Categorization of Metabolic Syndrome x Among Adults Using Learning Techniques
Abstract Views :183 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, St Joseph's College, Tiruchirappalli, IN
1 Department of Computer Science, St Joseph's College, Tiruchirappalli, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 5 (2013), Pagination: 215-218Abstract
Diabetes mellitus (DM) is a set of related diseases in which the body cannot regulate the amount of sugar in the blood sugar, either because the body does not produce enough insulin, or because the cells do not respond to the insulin that is produced. MSX is characterized by chronic hyperglycemia associated with disturbances of carbohydrate, fat, and protein metabolism due to absolute or relative deficiency in insulin secretion and/or action. It is also known as Metabolic Cum Vascular disorder. It causes long term damage, dysfunction and failure of various organs such as eyes, kidneys, nerves, heart and blood vessels. The Metabolic Syndrome x mainly affects the adult. A new methodology is used to find the stages of metabolic syndrome x using Multilayer perceptron (MLP) and EM Clustering. The symptoms and stages of Metabolic syndrome x are classified by using predictive modeling.In Multilayer perceptron technique, data objects are categorized based on the stages of metabolic syndrome x and find out their efficiency and accuracy. It categorizes the data such as IDDM, NIDDM. It helps us to know the various stages of metabolic syndrome x and to predict the recommend preclusion to patients those who are affected by metabolic syndrome x and provide suggestions to that patient.Keywords
Multilayer Perceptron (MLP), Metabolic Syndrome X (MSX)‚ Data Mining Techniques‚ Diabetes Mellitus (DM).- Enhanced Elliptic Curve Cryptography
Abstract Views :169 |
PDF Views:0
Authors
Affiliations
1 Department of Computer science, St. Joseph’s College (Autonomous), Tiruchirappalli - 620002, Tamil Nadu, IN
1 Department of Computer science, St. Joseph’s College (Autonomous), Tiruchirappalli - 620002, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 26 (2015), Pagination:Abstract
Background/Objectives: Today’s Technological world Information Security is an essential for commercial and legal trading, secrecy, truthfulness and non-reputability. The Elliptic Curve Cryptography (ECC) has become one of the latest trends in the field of Public-Key Cryptography (PKC). ECC promises a faster, efficient and more secured. In this paper, the Standard ECC and proposed Improved ECC (IECC) are compared. Methods/Statistical Analysis: The proposed IECC algorithm is designed to be more challenging as the repetitive characters of the text are replaced with the different cipher text in each of the iteration and outperforms the standard ECC in terms of cipher text, encryption, decryption time and security. This algorithm helps to assure end to end encryption for Online Social Network (OSN) users. Findings: The statistical analysis approach reveals a significant feature that the cipher text of IECC does not correlated with the plain text. This approach is improved ECC reduces more security than the Standard ECC. The proposed method is concluded that implemented IECC is better than standard ECC. Applications/Improvements: Except the proposed algorithm used for this research, the possibility of using other algorithms are implemented in future work.Keywords
Cryptosystem, Decryption, Elliptic Curve Cryptography, Encryption, Key GenerationFull Text
- Conflict Resolution and Duplicate Elimination in Heterogeneous Datasets using Unified Data Retrieval Techniques
Abstract Views :184 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, St. Joseph’s College, Trichy - 620002, Tamil Nadu, IN
1 Department of Computer Science, St. Joseph’s College, Trichy - 620002, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 22 (2015), Pagination:Abstract
Background/Objective: Creating queries for a single search term and identifying the viable solutions for the query are the two specific problems in retrieving the data. To resolve this issue, an effective information fusion technology should be provided to obtain effective results. This paper presents a method for resolving conflicts and eliminating duplicates with increased accuracy. Methods/Statistical Analysis: Universal wrappers are designed to retrieve the actual information from the heterogeneous data sources. The process of getting input itself is modified such that the retrieved results are relevant to the context. Ranking and duplicate eliminations are done accordingly to refine the obtained results to the user. Findings: Experimental results show that the improved accuracies of the data being fetched and with reduced conflicts and duplicates. This work uses major data sources from Google, New York Times and other offline data sources. By applying the proposed data retrieval techniques, the produced data is consistent by the help of wrappers. The proposed approach improves the data consistency which is relatively better than the existing technique. Finally, this proposed research work concludes that it is used to identify and resolve the conflict data and delivers the consistent data to the users in a ranked manner. Applications/Improvements: To create a unified repository which can be used for knowledge mining and warehouse based analysis of existing data and retrieve the result.Keywords
Conflict Identification, Conflict Resolution, Data Retrieval, Ranking, WrappersFull Text
- An Efficient Britwari Technique to Enhance Canny Edge Detection Algorithm using Deep Learning
Abstract Views :268 |
PDF Views:96
Authors
Affiliations
1 Department of Computer Science, Bharathidasan University, IN
1 Department of Computer Science, Bharathidasan University, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 3 (2022), Pagination: 2634-2639Abstract
Artificial Intelligence edge detection refers to a set of mathematical techniques used to recognize digital image locations. The picture brightness plays a vital role in detecting dissimilarities and making decisions. Edges are the sharp changes in pictures with respect to the brightness and are commonly categorized into a collection of curved line segments. The main focus of this paper is to find sharp corner edges and the false edges present in the MRI images. The canny edge algorithm is a popular method for detecting these types of edges. The traditional canny edge detection technique has various issues that are discussed in this paper. This study analyses the canny edge algorithm and enhances the smoothing filter, pixel identifier, and feature selection. The proposed Britwari technique, Tabu Search Heuristic Pattern Identifier (TSHPI) enhances the edge detection using SUSAN Filter. Feature Selection is performed to improvise the canny edge method. Deep Learning algorithm is used for classification of pre-trained neural networks to find a greater number of edge pixels. The implementation results show that the Britwari proposed technique (SUSAN Filter Tabu Search Heuristic Pattern Identifier Hill Climbing) reached better accuracy than the traditional Canny Edge Detection algorithms. The results produced better feature set selection using edge detection in MRI images.Keywords
Britwari Technique, Edge Detection, Deep Learning, Image ProcessingFull Text
References
- Saad Albawi, Tareq Abed Mohammed and Saad Al-Zawi, “Understanding of a Convolutional Neural Network”, Proceedings of International Conference on Communication and Electronics Telecommunications, pp. 1-8, 2017.
- Ruohui Wang, “Edge Detection using Convolutional Neural Network”, Proceedings of International Conference on Computer Science and Engineering, pp. 12-20, 2019.
- A.S. Pooja and P. Smitha Vas, “Edge Detection using Deep Learning”, International Research Journal of Engineering and Technology, Vol. 5, No. 7, pp. 1-12, 2018.
- Mohamed A. El-Sayed, Yarub A. Estaitia and Mohamed A. Khafagy, “Automated Edge Detection using Convolutional Neural Network”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 3, pp. 1-13, 2013.
- A. Ahmed, Y.C. Byun and D. Hazra, “Edge Detection for Roof Images using Transfer Learning”, Proceedings of 18th International Conference on Computer and Information Science, pp. 1-7, 2019.
- Chenxing Xue, Jun Zhang, Jiayuan Xing, Yuting Lei and Yan Sun, “Research on Edge Detection Operator of a Convolutional Neural Network”, Proceedings of Joint International Conference on Information Technology and Artificial Intelligence, pp. 1-14, 2020.
- Z. Qu, P. Wang and Z.K. Shen, “Fast SUSAN Edge Detector by Adapting Step-Size”, Optik - International Journal for Light and Electron Optics, Vol. 124, No. 3, pp. 747-750, 2013.
- C. Gao, H. Zhu and Y. Guo, Y. (2012), “Analysis and improvement of SUSAN algorithm Signal Processing”, Vol. 92, No. 10, pp. 2552-2559, 2012.
- Shenghua Xu, Litao Han and Lihua Zhang, “An Algorithm to Edge Detection Based on SUSAN Filter and Embedded Confidence”, Proceedings of 6th International Conference on Intelligent Systems Design and Applications, pp. 1-11, 2006.
- X. Wei, S. Jiang and Y. Li, “Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology”, IEEE Transactions on Intelligent Transportation System, Vol. 21, No. 3, pp. 947-958, 2019.
- Huanli Li, Lihong Guo and Tao Chen, “The Corner Detector of Teeth Image Based on the Improved SUSAN Algorithm”, Proceedings of International Conference on Biomedical Engineering and Informatics, pp. 16-18, 2010.
- E. Rafajlowicz, “SUSAN Edge Detector Reinterpreted, Simplified and Modified”, Proceedings of International Workshop on Multidimensional Systems, pp. 1-14, 2021.
- Xiaofeng Li, Hongshuang Jiao and Yanwei Wang, “Edge Detection Algorithm of Cancer Image based on Deep Learning”, Bioengineered, Vol. 11, No. 1, pp. 693-707, 2020.
- Hafiza Huma Taha, Syed Sufyan Ahmed and Haroon Rasheed, “Tumor Detection through Image Processing using MRI”, International Journal of Scientific and Engineering Research, Vol. 6, No. 2, pp. 1-14, 2015.
- H.N.T.K. Kaldera, S.R. Gunasekara and M.B. Dissanayake, “MRI based Glioma Segmentation using Deep Learning Algorithms”, Smart Computing and Systems Engineering, Vol. 8, pp. 1-16, 2019.
- Shanaka Ramesh Gunasekara, Shanaka Ramesh Gunasekara and Maheshi B. Dissanayake, “A Systematic Approach for MRI Brain Tumor Localization and Segmentation using Deep Learning and Active Contouring”, Journal of Healthcare Engineering, Vol. 2021, pp. 1-13, 2021.
- Github, Available at https://github.com/
- Algorithm to Code Converter, Available at http://codershunt.weebly.com/projects/algorithm-to-code-converter
- Visual Studio, Available at https://visualstudio.microsoft.com/
- BSDS500, “Berkeley Segmentation Dataset 500”, Available at https://paperswithcode.com/dataset/bsds500#:~:text=Berkeley%20Segmentation%20Data%20Set%20500%20(BSDS500)%20is%20a%20standard%20benchmark,interior%20boundaries%20and%20background%20boundaries.