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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
Thangaraj, P.
- Clustering and Classifying Diabetic Data Sets Using K-means Algorithm
Abstract Views :875 |
PDF Views:6
Authors
Affiliations
1 Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., IN
1 Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., IN
Source
Journal of Applied Information Science, Vol 1, No 1 (2013), Pagination: 23-27Abstract
The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present the Classification of diabetic's data set and the k-means algorithm to categorical domains. Before classify the data set preprocessing of data set is done to remove the noise in the data set. We use the missing value algorithm to replace the null values in the data set. This algorithm is also used to improve the classification rate and cluster the data set using two attributes namely plasma and pregnancy attribute.Keywords
Classification, Cluster Analysis, Clustering Algorithms, Categorical Data, Pre-processing
References
- Huang, Z. (1998). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values, Data Mining and Knowledge Discovery, 2, 283-304.
- Mitchell, T. (1997). Decision Tree Learning (52-78). McGraw-Hill Companies, Inc.
- Yasodha, P. & Kannan, M. (2011). Analysis of a population of diabetic patients databases in Weka tool. Proceedings of the International Journal of Scientific & Engineering Research, 2(5).
- Editorial, Diagnosis and Classification of Diabetes Mellitus, American Diabetes Association, Diabetes Care. (2004). 27(1).
- Karegowda, A. G., Punya, V., Manjunath, A. S. & Jayaram, M. A. (2012). Rule based classification for diabetic patients using cascaded K-means and decision tree C4.5. International Journal of Computer Applications, 45(12), (0975-8887).
- Karegowda, A. G., Jayaram, M. A. & Manjunath, A. S. (2012). Cascading K-means clustering and K-nearest neighbor classifier for categorization of diabetic patients. International Journal of Engineering and Advanced Technology, 1(3).
- Wu, C., Steinbauer, J. R. & Kuo, G. M. (2005). EM Clustering Analysis of Diabetes Patients Basic Diagnosis Index. Articles from AMIA Annual Symposium Proceedings are provided here courtesy of American Medical Informatics Association.
- Maseri, W., Mohd, W., Herawan, T. & Ahmad, N. (2013). Applying Variable Precision Rough Set for Clustering Diabetics Dataset. In: AST2013 and Soft-tech 2013 International Conference.
- Vijayalakshmi, D. & Thilagavathi, K. (2012). An Approach for Prediction of Diabetic Disease by Using b-Colouring Technique in Clustering Analysis. Proceedings of International Journal of Applied Mathematical Research, 1(4), 520-530.
- Reliable Data Security Architecture for Mobile Ad Hoc Networks
Abstract Views :162 |
PDF Views:2
Authors
M. Renuka
1,
P. Thangaraj
2
Affiliations
1 Department of Applied Science, SSM College of Engineering, Komarapalayam, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IN
1 Department of Applied Science, SSM College of Engineering, Komarapalayam, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IN
Source
Wireless Communication, Vol 3, No 5 (2011), Pagination: 318-322Abstract
Mobile ad hoc networks proved their efficiency in the deployment for different fields, but highly vulnerable to security attacks. It seems to be more challenging of in wireless networks. Existing research carried out provides authentication, confidentiality, availability, and secure routing and intrusion detection in ad hoc networks. Ad hoc network characteristics should be taken into consideration to design efficient data security along its path of transmission. The proposal work present, reliable data security architecture (RDSA) in improving the data transmission security of ad hoc networks with reliable multi-path routing. Reliable multiple paths between nodes in the ad hoc network increase the security level of transmitted data. The original message to be secured is split into parts that are transmitted in reliable multiple paths. The disseminated messages are encrypted on its course of transmission to improve the security further. Experimental simulations are conducted to the proposed RDSA approach and compared with existing ad hoc multi-path security solutions. RDSA shows better performance compared to that of generic data security architecture in terms of path stability and data loss to 5% and 7% respectively.Keywords
Mobile Ad Hoc Networks, Public Key Infrastructure.- An Effective Anomaly Intrusion Detection Using Statistical Change Point Detection
Abstract Views :158 |
PDF Views:4
Authors
M. Thangavel
1,
P. Thangaraj
2
Affiliations
1 Department of Computer Applications at Erode Sengunthar Engineering College, Erode, Tamilnadu, IN
2 School of Computer Technology and Applications, Kongu Engineering College, Erode, Tamilnadu, IN
1 Department of Computer Applications at Erode Sengunthar Engineering College, Erode, Tamilnadu, IN
2 School of Computer Technology and Applications, Kongu Engineering College, Erode, Tamilnadu, IN
Source
Networking and Communication Engineering, Vol 2, No 9 (2010), Pagination: 301-307Abstract
Understanding the nature of intrusion attacks is critically important to the development of effective counter measures to anomaly traffic detection problem. Anomaly intrusion traffic attacks combined with traditional network intruders became most serious threats to network security. The existing work monitors available traffic attacks and take appropriate action to mitigate them, before they have had much time to propagate across the network. The proposed working model of statistical traffic anomaly detection method is carried out on the principle traces of non intrusive packet header data with quick detection rate. Traffic is monitored at regular intervals to obtain a signal that can be analyzed through statistical techniques and compared to historical norms to detect anomalies (change detection). The proposed methodology of anomaly intrusion traffic detection envisions statistical change detection theory for real-time data source extracted from Net Con server (Internet Service Provider popularly running at Erode Region). The experimental results suggest little use of address spoofing by attackers, which imply that such attacks will be invisible to indirect backscatter measurement techniques. The proposed traffic anomaly intrusion detection provides an improvement of 12% average through put compared to the existing ones. The propagation delay metric shows a reduction of nearly 9% with other methods of anomaly intrusion detection.
Keywords
Statistical Anomaly Detection, Network Traffic, Intrusion Detection.- Improving the Image Retrieval Performance Using False Image Filtering Approach
Abstract Views :150 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engg., Institute of Road and Transport Technology, Erode, IN
2 Department of Computer Science and Engg, Bannariamman Institute of Technology, Sathyamangalam, Autonomous Institution Affiliated to Anna University, Chennai, IN
1 Department of Computer Science and Engg., Institute of Road and Transport Technology, Erode, IN
2 Department of Computer Science and Engg, Bannariamman Institute of Technology, Sathyamangalam, Autonomous Institution Affiliated to Anna University, Chennai, IN
Source
Digital Image Processing, Vol 5, No 4 (2013), Pagination: 204-212Abstract
The novel approach combines color and texture features for content based image retrieval (CBIR). This paper is used to retrieve the images from the huge collection of image databases. Most of the research interest in recent years uses feature indexing techniques for the image retrieval. If the number of features are more, then the more time is spent on the comparing the features in low level image retrieval. The proposed system has focused on minimizing the number of comparision by considering the structure of the color theory which says that human color vision system is sensitive to light–dark variations. Here, the color theory is used to eliminate the irrelevant images from the huge collection of images. The feature extraction methods are used to retrive the relevant images. The irrelevant images are filtered by mesuring the deviation between light and dark colors. The opponent values of color and texture features of the image are taken. The image retrieval performance is improved by minimizing the number of comparisions. The proposed method outperforms the other previously developed methods by providing the classification accuracy of more than 89% for the various types of natural images taken from coral database. Hence, this paper concentrates on color and texture features for image retrieval in different directions. The proposed method significantly improves efficiency with less computational complexity.Keywords
Color, Texture, Tamura, Threshold, Retrieval, Image Database, Mean, Standard Deviation, Hash Queue, Color Theory, Median Features.- Employee Appraisal Report Processing Using Weka
Abstract Views :166 |
PDF Views:2
Authors
Affiliations
1 Dept. of Computer Science and Engineering, Institute of Road and Transport Technology, Erode-638316, IN
2 Dept. of Computer Science and Engineering, Bannariamman Institute of Technology, Sathyamangalam-638401, IN
1 Dept. of Computer Science and Engineering, Institute of Road and Transport Technology, Erode-638316, IN
2 Dept. of Computer Science and Engineering, Bannariamman Institute of Technology, Sathyamangalam-638401, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 5 (2013), Pagination: 202-208Abstract
The main objective is to evaluate the appraisal report of an employee using a decision tree algorithm. The decision tree is one of inductive learning method used in artificial intelligence. It is used for data classification and prediction. The data mining applications use the decision tree for information retrieval and information extraction. This paper discuss about the method of applying decision tree for predicting the performance of an employee working in an organization. The decision tree is created by using WEKA tool which is used to evaluate the performance of an employee by processing the appraisal report of an employee. The processed data is mainly used for giving promotion, yearly increment and career advancement. In order to provide yearly increment for an employee, it should be evaluated by using past historical data of employees. The historical data are stored in the form of ARFF(Attribute-Relation File Format) and the performance are found by testing the attributes of an employee against the rules generated by the decision tree classifier in WEKA tool. This paper concentrates on collecting data about employees, generating a decision tree from the historical data, testing the decision tree with attributes of an employee and generating the output as whether to give the promotion or not using WEKA tool. The information about an employee are collected by using the user interface. This information is compared with the trained data stored in the decision tree. The final goal node is to determine whether the employee will get yearly increment, promotion or not.Keywords
Classification, Decision Tree, J48 Algorithm, Training.- Performance Comparison of Multilayer Feed Forward and Radial Basis Feed Forward Neural Networks in River Stage Prediction
Abstract Views :159 |
PDF Views:1
Authors
R. Kalaivani
1,
P. Thangaraj
2
Affiliations
1 Institute of Road and Transport Technology, Erode, Tamilnadu, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
1 Institute of Road and Transport Technology, Erode, Tamilnadu, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 5 (2012), Pagination: 290-295Abstract
Nowadays, satellite image processing plays a crucial role for the research developments in many fields of study including Astronomy, Remote Sensing, GIS, Agriculture Monitoring and Disaster Management. The remotely sensed images are utilized in many of the researches with the aim of predicting natural disasters so that essential precautions can be taken to protect the environment. Besides the other, the water resource analysis plays a vital role in these researches. Traditionally, lots of methods are utilized for the analysis and determination of the level of water in water resources. In this work, the river water resource is analyzed to determine the stage of the water level using multilayer feed forward and radial basis feed forward networks and their performance is measured. The existing works are not effective because they determine only the changes that occur in the water level and does not translate them into meaningful results that indicates its status i.e., whether it is in the danger zone or not.Keywords
Satellite Image Processing, River Stage, Back Propagation, Radial Basis Feed Forward Network, Sensitivity, Specificity, Accuracy.- Low Power Shift and Add Multiplier Design
Abstract Views :211 |
PDF Views:129
Authors
Affiliations
1 Maharaja Engineering College, Avinashi, Anna University, IN
2 Computer Applications, Kongu Engineering College, Perundurai, Anna University, IN
3 II ME Applied Electronics, Maharaja Engineering College, Avinashi, Anna University, Tamil nadu, IN
1 Maharaja Engineering College, Avinashi, Anna University, IN
2 Computer Applications, Kongu Engineering College, Perundurai, Anna University, IN
3 II ME Applied Electronics, Maharaja Engineering College, Avinashi, Anna University, Tamil nadu, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 2, No 3 (2010), Pagination: 12-22Abstract
Today every circuit has to face the power consumption issue for both portable device aiming at large battery life and high end circuits avoiding cooling packages and reliability issues that are too complex. It is generally accepted that during logic synthesis power tracks well with area. This means that a larger design will generally consume more power. The multiplier is an important kernel of digital signal processors. Because of the circuit complexity, the power consumption and area are the two important design considerations of the multiplier. In this paper a low power low area architecture for the shift and add multiplier is proposed. For getting the low power low area architecture, the modifications made to the conventional architecture consist of the reduction in switching activities of the major blocks of the multiplier, which includes the reduction in switching activity of the adder and counter. This architecture avoids the shifting of the multiplier register. The simulation result for 8 bit multipliers shows that the proposed low power architecture lowers the total power consumption by 35.25% and area by 52.72 % when compared to the conventional architecture. Also the reduction in power consumption increases with the increase in bit width.Keywords
Low Power Multiplier, Low Power Ring Counter, Sources of Switching Activities.Full Text
- Cloud Computing for Biomedical Information Management
Abstract Views :113 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamilnadu, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
1 Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamilnadu, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
Source
International Journal of Scientific Engineering and Technology, Vol 2, No 4 (2013), Pagination: 239-244Abstract
Biomedical informatics community shares data and applications, can take advantage of a new resource called “cloud computing”. Clouds generally offer resources on demand. In most clouds, charges are pay per use, based on large farms of inexpensive, dedicated servers, sometimes supporting parallel computing. When clouds are used for biomedical information management, it costs much lower than dedicated laboratory systems or even institutional data centers. Biomedical applications that are not I/O intensive and do not demand a fully mature environment can use clouds which has major technological improvements. Instead of listing the strengths and weaknesses of cloud-based systems (e.g., for security or data preservation), this paper reviews the changes from individual lab systems to a grid and cloud based environment for Biomedical Informatics. Many observers believe that clouds represent the next generation of computing paradigm for biomedical application.Keywords
Cloud Computing, Data Sharing, Bioinformatics, Security, Distributed Computing.Full Text