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Thangadurai, K.
- Computing the Activity of Connecting Computers in Network Security
Abstract Views :249 |
PDF Views:3
Authors
K. Sekar
1,
K. Thangadurai
2
Affiliations
1 Department of Computer Application, Vivekanandha Institute of Information and Management Studies, Tiruchengode, Namakkal Dt, Tamilnadu, IN
2 Department of Computer Science, Government Arts College (Autonomous), Karur, Tamilnadu, Pincode–639 005, IN
1 Department of Computer Application, Vivekanandha Institute of Information and Management Studies, Tiruchengode, Namakkal Dt, Tamilnadu, IN
2 Department of Computer Science, Government Arts College (Autonomous), Karur, Tamilnadu, Pincode–639 005, IN
Source
Automation and Autonomous Systems, Vol 3, No 5 (2011), Pagination: 255-259Abstract
Network security is the process of preventing and detecting unauthorized use of your computer. Prevention measures help you to stop unauthorized users (also known as "intruders") from accessing any part of your network system. Detection helps you to determine whether or not someone attempted to break into your system, if they were successful, and what they may have done. We use computers for everything from banking and investing to shopping and communicating with others through email or chat programs. Although you may not consider your communications "top secret," you probably do not want strangers reading your email, using your network to attack other systems, sending forged email from your computer, or examining personal information stored on your network.Keywords
NAT, TCP, UDP, DNS, SSH, POP3, IMAP, SMTP.
- Simulation Based Performance Comparison of Various Routing Protocols in MANET Using Network Simulation Tool
Abstract Views :138 |
PDF Views:3
Authors
Affiliations
1 P.G. and Research Department of Computer Science, Government Arts College (Autonomous), Karur, IN
1 P.G. and Research Department of Computer Science, Government Arts College (Autonomous), Karur, IN
Source
International Journal of Advanced Networking and Applications, Vol 4, No 5 (2013), Pagination: 1744-1751Abstract
A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a network temporarily without any centralized administration of the mobile networks. Each node in MANET moves arbitrarily making the multi-hop network topology to change randomly at unpredictable times. Two nodes in such a network can communicate in a bidirectional manner if and only if the distance between them is at most the minimum of their transmission ranges. When a node wants to communicate with a node outside its transmission range, a multi-hop routing strategy is used which involves some intermediate nodes? Because of the movements of nodes, there is a constant possibility of topology change in MANET. There are several familiar routing protocols like DSDV, AODV, DSR, etc., Which have been proposed for providing communication among all the nodes in the network? This paper presents a performance comparison of proactive and reactive protocols AODV, DSDV and DSR based on metrics such as throughput, packet delivery ratio and average end-to-end delay by using the NS-2 simulator.Keywords
MANET, DSDV, DSR, AODV, Throughput, Packet Delivery Ratio and Average End-to-End Delay.Full Text
- Customer Satisfaction in SCRM with Key Performance Indicator System
Abstract Views :253 |
PDF Views:0
Authors
P. Menaka
1,
K. Thangadurai
2
Affiliations
1 Department of Computer Science, Manaonmaniam Sundaranar University, IN
2 Department of Computer Science, Government Arts College, Karur, IN
1 Department of Computer Science, Manaonmaniam Sundaranar University, IN
2 Department of Computer Science, Government Arts College, Karur, IN
Source
ICTACT Journal on Management Studies, Vol 3, No 2 (2017), Pagination: 515-522Abstract
Customer relation management is a significant feature in the business development which assists for business peoples to get the knowledge about the customer's opinions and can establish the profitable environment. Customer opinions were utilized to enhance the product, so that customer fulfilment and profit will rise desirably. So it is necessary to execute the functional design of customer opinions regarding the products which depends on the time duration. In the earlier work, Customer Knowledge Management (CKM) is established to raise the profit level of industries by tracking the entire regarding the product which is demanded by the customers. Nevertheless, creating and managing the CKM for the huge volume of the customer is a complex task. It results in inaccuracy, if in case it is done manually with less customer information. This issue is rectified in the proposed research methodology by bringing-in the methodology such as Neural Network based Social Customer Relation Management (NN-SCRM). This algorithm is utilized to examine the following factors by gathering the knowledge of the customer review information: "Predict the future, most profitable customers, maintaining quality of product development, customer life time value, identify customers and their products". This is done according to the information of the customer review, which, in turn, obtained from the customers opinions disclosed by their comments regarding the product. This proposed research work is executed and examined in the MATLAB simulation environment from which it is confirmed that the proposed research framework tends to give the best output than the current CKM frameworks.Keywords
Knowledge Management, Review Analysis, Pre-Processing, Customer Opinions, Profit.References
- Colleen Cunningham and Il-Yeol Song, “A Taxonomy of Customer Relationship Management Analyses for Data Warehousing”, Proceedings of 26th International Conference on Conceptual Modeling in Tutorials, Posters, Panels and Industrial Contributions, Vol. 83, pp. 97-102, 2007.
- J. Wu, “Customer Relationship Management in Practice: A Case Study of Hi-Tech Company from China”, Proceedings of International Conference on Service Systems and Service Management, pp. 1-6, 2008.
- B. Solis, “Engage: The Complete Guide for Brands and Businesses to Build, Cultivate, and Measure Success in the New Web”, John Wiley & Sons, 2010.
- J.H. Kietzmann, K. Hermkens, I.P. McCarthy and B.S. Silvestre, “Social media? Get Serious! Understanding the Functional Building Blocks of Social Media”, Business Horizons, Vol. 54, No. 3, pp. 241-251, 2011.
- A. Payne and P. Frow, “A Strategic Framework for Customer Relationship Management”, Journal of Marketing, Vol. 69, No. 4, pp. 167-176, 2005.
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- S. Gupta and V. Zeithaml, “Customer Metrics and Their Impact on Financial Performance”, Marketing Science, Vol. 25, No. 6, pp. 718-739, 2006.
- F.F. Reichheld, “The One Number You Need To Grow”, Harvard Business Review, Vol. 81, No. 12, pp. 46-55, 2003.
- D.L. Hoffman and M. Fodor, “Can You Measure the ROI of Your Social Media Marketing?”, MIT Sloan Management Review, Vol. 52, No. 1, pp. 41-49, 2010.
- B.D. Weinberg and E. Pehlivan, “Social Spending: Managing the Social Media Mix”, Business Horizons, Vol. 54, No. 3, pp. 275-282, 2011.
- D. Chauvel and C. Despres, “A Review of Survey Research in Knowledge Management: 1997-2001”, Journal of Knowledge Management, Vol. 6, No. 3, pp. 207-223, 2002.
- S.H. Liao, “Knowledge Management Technologies and Applications-Literature Review from 1995 to 2002”, Expert Systems with Applications, Vol. 25, No. 2, pp. 155-164, 2003.
- M. Du Plessis, “Knowledge Management: What makes Complex Implementations Successful?”, Journal of Knowledge Management, Vol. 11, No. 2, pp. 91-101, 2007.
- Z. Guo and J. Sheffield, “A Paradigmatic and Methodological Examination of Knowledge Management Research: 2000 to 2004”, Decision Support Systems, Vol. 44, No. 3, pp. 673-688, 2008.
- Z. Ma and K.H. Yu, “Research Paradigms of Contemporary Knowledge Management Studies: 1998-2007”, Journal of Knowledge Management, Vol. 14, No. 2, pp. 175-189, 2010.
- A. Serenko, N. Bontis, L. Booker, K. Sadeddin and T. Hardie, “A Scientometric Analysis of Knowledge Management and Intellectual Capital Academic Literature (1994-2008)”, Journal of Knowledge Management, Vol. 14, No. 1, pp. 3-23, 2010.
- D.P. Wallace, C. Van Fleet and L.J. Downs, “The Research Core of the Knowledge Management Literature”, International Journal of Information Management, Vol. 31, No. 1, pp. 14-20, 2011.
- Y.K. Dwivedi, K. Venkitachalam, A.M. Sharif, W. Al-Karaghouli and V. Weerakkody, “Research Trends in Knowledge Management: Analyzing the Past and Predicting the Future”, Information Systems Management, Vol. 28, No. 1, pp. 43-56, 2011.
- M.R. Lee and T.T. Chen, “Revealing Research Themes and Trends in Knowledge Management: From 1995 to 2010”, Knowledge-Based Systems, Vol. 28, pp. 47-58, 2012.
- Effect of Severe Plastic Deformation on Mechanical Charecteristics of Al-6061:SiCP Composites
Abstract Views :198 |
PDF Views:0
Authors
Affiliations
1 Dept. of Production Engg., National Institute of Technology, Trichy, IN
2 Dept. of Mech. Engg., Jayaram College of Engineering & Technology, Trichy, IN
3 Dept. of Mech. Engg., KLN College of Engineering & Technology, Sivagangai, IN
1 Dept. of Production Engg., National Institute of Technology, Trichy, IN
2 Dept. of Mech. Engg., Jayaram College of Engineering & Technology, Trichy, IN
3 Dept. of Mech. Engg., KLN College of Engineering & Technology, Sivagangai, IN
Source
Manufacturing Technology Today, Vol 9, No 2 (2010), Pagination: 17-24Abstract
Nowadays modern manufacturing system is enforced to find out the new material such as metal matrix composite or ceramic matrix composite. The aim of this research is to invent the aluminium based metal matrix composite such as 80% of Al 6061-20% of SiC and 90% of Al 6061-10% of SiC. The aluminum 6061 alloy based composite is prepared through stir casting route. Then the composite is heat treated at the critical temperature. After heat treatment, the mechanical properties of composite are compared before extrusion and after heat treatment. Mechanical properties of both heat-treated and as fabricated composites showed a high dependence on the ceramic content. The yield, ultimate tensile strength, and the elastic modulus of the material were increased with volume fraction of carbide, whereas the ductility was decreased. The tensile properties are seen to vary significantly with ageing treatment. The highest strengths were obtained for the solution heat-treated and naturally aged Al-6061: SiC composites containing 20 vol. % SiC.Keywords
Plastic Deformation, Metal Matrix Composite, Microstructure.- Optimization of Operating Parameters in Wire Electric Discharge Machining Using Particle Swarm Optimization and Memetic Algorithm
Abstract Views :200 |
PDF Views:0
Authors
Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy, Thanjavur - 613 402, IN
2 Department of Mechanical Engineering, J.J. College of Engineering, Tiruchirappalli-620 009, IN
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy, Thanjavur - 613 402, IN
2 Department of Mechanical Engineering, J.J. College of Engineering, Tiruchirappalli-620 009, IN
Source
Manufacturing Technology Today, Vol 3, No 7 (2004), Pagination: 17-21Abstract
Wire Electric Discharge Machining process is one of the important nontraditional machining process which is used to machine hard materials, complex shapes and contours which are difficult by conventional methods. In this paper Particle Swarm Optimization (PSO) and Memetic Algorithm (MA) based optimization procedures have been developed to optimize machining parameters viz machining speed, pulse on time, pulse off time, and peak current by using two response equations for material removal rate and surface roughness. The objective function considered for optimization is maximization of material removal rate and minimization of surface roughness. Here objective function is solved by taking combined objective function (weightage given 50% to material removal rate and 50% to surface roughness) i.e. minimization of material removal rate and surface roughness. The output results o f these two algorithms are compared.- Integration of Rough Set theory and Genetic Algorithm for Optimal Feature Subset Selection on Diabetic Diagnosis
Abstract Views :227 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Karur Arts and Science College, IN
2 Department of Computer Science, Periyar University, IN
1 Department of Computer Science, Karur Arts and Science College, IN
2 Department of Computer Science, Periyar University, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 2 (2018), Pagination: 1623-1634Abstract
Diabetic diagnosis is an important research in health care domain to analyze relevant microorganisms at an earlier stage. Due to large growth in world’s population, feature subset selection model receives a great deal in any domain of research and also a reliable tool for diabetic diagnosis. Several data mining techniques have been developed to evaluate the significant causes of diabetes with least sets of risk factors. These minimum set is selected without considering the potential significance of the risk factors and optimal feature subset selection, hence it failed to diagnosis the pattern of diabetes accurately. In order to improve the feature subset selection, an Integration of Fuzzy Rough Set Theory and Optimized Genetic algorithm (IFRST-OGA) is introduced. The main objective of the IFRST-OGA is to find optimal risk factors for efficient pattern recognition on diabetes healthcare data. Initially, feature selection is performed using Fuzzy Rough Set Theory (FRST) for diagnosing the diabetes. After that, the Optimized Genetic Algorithm (OGA) is applied which mainly searches for an optimal feature subset through the selection, crossover, and mutation operations to diagnose the disease at an earlier stage. This helps to identify the risk factor and diagnosing the diabetes disease efficiently. Experimental results show that the proposed IFRST-OGA increases the performance in terms of true positive rate, computation time and diabetes diagnosing accuracy.Keywords
Diabetic Diagnosis, Risk Factors Analysis, Rough Set Theory, Feature Selection, Optimized Genetic Algorithm, Selection, Crossover, Mutation, Optimal Feature Subset Selection.References
- T. Santhanam and M.S Padmavathi, “Application of K-Means and Genetic Algorithms Dimension Reduction by Integrating SVM for Diabetes Diagnosis”, Procedia Computer Science, Vol. 47, pp. 76-83, 2015.
- Fei Ye, “Evolving the SVM Model based on a Hybrid Method using Swarm Optimization Techniques in Combination with a Genetic Algorithm for Medical Diagnosis”, Multimedia Tools and Applications, pp. 1-30, 2016.
- Kung-Jeng Wang, Angelia Melani Adrian, Kun-Huang Chen , Kung-Min Wang, “An Improved Electromagnetism-like Mechanism Algorithm and its Application to the Prediction of Diabetes Mellitus”, Journal of Biomedical Informatics, Vol. 54, pp. 220-229, 2015.
- Madonna M. Roche and Peizhong Peter Wang, “Factors Associated with a Diabetes Diagnosis and Late Diabetes Diagnosis for Males and Female”, Journal of Clinical and Translational Endocrinology, Vol. 1, pp. 77-84, 2014.
- Ioannis Kavakiotis, Olga Tsave , Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas and Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal, Vol. 15, pp. 104-116, 2017.
- Jiye Liang, Feng Wang, Chuangyin Dang and Yuhua Qian, “A Group Incremental Approach to Feature Selection Applying Rough Set Technique”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 2, pp. 294-308, 2014.
- Mohamed Amine Chikh, Meryem Saidi and Nesma Settouti, “Diagnosis of Diabetes Diseases using an Artificial Immune Recognition System with Fuzzy K-Nearest Neighbor”, Journal of Medical Systems, Vol. 36, No. 5, pp. 2721-2729, 2012.
- Mustafa Serter Uzer, Nihat Yilmaz and Onur Inan, “Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification”, The Scientific World Journal, Vol. 2013, pp. 1-10, 2013.
- Chih-Fong Tsai, William Eberle and Chi-Yuan Chu, “Genetic Algorithms in Feature and Instance Selection”, Knowledge-Based Systems, Vol. 39, pp. 240-247, 2013.
- Divya Tomar and Sonali Agarwal, “Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes”, Advances in Artificial Neural Systems, Vol. 2015, pp. 1-10, 2015.
- Filippo Amato, Alberto Lopez, Eladia Maria Pena-Mendez, Petr Vanhara, Ales Hamp and Josef Havel, “Artificial Neural Networks in Medical Diagnosis”, Journal of Applied Biomedicine, Vol. 11, pp. 47-58, 2013.
- Abid Sarwar and Vinod Sharma, “Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2”, International Journal of Computer Application, Vol. 3, pp. 14-16, 2012.
- A. Pradhan, G.R. Bamnote, Vinit Tribhuvan, Kiran Jadhav, Vijay Chabukswar, Vijay Dhobale, “A Genetic Programming Approach for Detection of Diabetes”, International Journal of Computational Engineering Research, Vol. 2, No. 6, pp. 91-94, 2012.
- Liying Fang, Han Zhaoa, Pu Wanga, Mingwei Yud, Jianzhuo Yana, Wenshuai Cheng and Peiyu Chen, “Feature Selection Method based on Mutual Information and Class Separability for Dimension Reduction in Multidimensional Time Series for Clinical Data”, Biomedical Signal Processing and Control, Vol. 21, pp. 82-89, 2015.
- Ahmed Hamza Osman and Hani Moetque Aljahdali, “Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM”, International Journal of Advanced Computer Science and Applications, Vol. 8, No. 1, pp. 236-244, 2017.
- Xue-Hui Meng, Yi-Xiang Huang, Dong-Ping Rao, Qiu Zhang and Qing Liu, “Comparison of Three Data Mining Models for Predicting Diabetes or Prediabetes by Risk Factors”, Kaohsiung Journal of Medical Sciences, Vol. 29, pp. 93-99, 2013.
- Razieh Sheikhpour and Mehdi Agha Sarram, “Diagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms”, Iranian Journal of Diabetes and Obesity, Vol. 6, No. 2, pp. 74-84, 2014.
- Fatma Patlar Akbulut and Aydın Akan, “Support Vector Machines Combined with Feature Selection for Diabetes Diagnosis”, Istanbul University-Journal of Electrical and Electronics Engineering, Vol. 17, No. 1, pp. 3219-3225, 2017.
- Dilip Kumar Choubey and Sanchita Paul, “GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis”, International Journal of Intelligent Systems and Applications, Vol. 1, pp. 49-59, 2016.
- Ahmad Taher Azar and Aboul Ella Hassanien “Dimensionality Reduction of Medical Big Data using Neural-Fuzzy Classifier”, Soft Computing, Vol. 19, No. 4, pp. 1115-1127, 2015.
- Ahmed Hamza Osman Hari “Diabetes Disease Diagonosis method based on Feature Extraction using KSVM”, International Journal of Advanced Computer Science and Application, Vol. 8, No. 1, pp. 236-244, 2017.
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