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Prabakaran, N.
- Brand Value of Bridgestone Tyre Limited - A Study with Reference to Puducherry State
Abstract Views :302 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, Dept of Management Studies, Sathyabama University, Chennai, Tamil Nadu, IN
2 Professor and HOD, Dept. of Business Administration, Annamalai University, Annamalai Nagar, Tamil Nadu, IN
1 Research Scholar, Dept of Management Studies, Sathyabama University, Chennai, Tamil Nadu, IN
2 Professor and HOD, Dept. of Business Administration, Annamalai University, Annamalai Nagar, Tamil Nadu, IN
Source
International Journal of Marketing and Business Communication, Vol 2, No 3 (2013), Pagination: 57-60Abstract
In modern era, one of the most important aspects of the Marketing Management function is to increase the value of the brand in the market. Therefore companies need to maintain their value among the market by using marketing tools. The study examines the awareness and power of the brand of Bridgestone. This study was conducted in and around Puducherry. Questionnaire responses were collected from 196 respondents who are customers of Bridgestone. A pilot study was conducted on a small sample and the validity of the questionnaire items was tested. Simple percentage and Karl Pearson's coefficient were used to determine the Awareness and preference of the brand. We found that there is a positive association between the age and brand value of the customers.Keywords
Brand Value, Branding, Brand Management, Customer Awareness, Customer Preference, Tyre IndustryReferences
- Aaker, D. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. New York: The Free Press.
- Barth, M. E., Clement, M. B, Foster, G. & Kasynik, R. (1998). Brand values and capital market valuation. Review of Accounting Studies, 3(1-2), 41-68.
- Evans, J. R. & Berman, B. (2007). Marketing Management. New Delhi: Cengage learning.
- Ekeledo, I. & Sivakumar, K. (1998). Foreign market entry mode choice of service fi rms: A contingency perspective. Journal of the Academy of Marketing Science, 26(4), 260-272.
- Jones, J. P. (1998). What’s in a Brand. Tata McGraw Hill Publishing.
- Kotler, P. (1996). Principles of Marketing. New Jersy: Prentice Hall.
- Kotler, P., Armstrong, G., Agnihotri, P. Y. & Haque, E. U. (2010). Principles of Marketing. Pearson Education, Inc. pp. 204-210.
- Sengupta, S. (1995). Brand Positioning, Strategies for Competitive Advantage. Tata McGraw Hill Publishing.
- Co-Channel & Adjacent Channel Interference Blocking Performance in 2.4 GHz Band
Abstract Views :149 |
PDF Views:3
Authors
N. Prabakaran
1,
K. S. Shaji
2
Affiliations
1 Department of Electronics & Telecommunication Engineering, Sathyabama University, Chennai, 600119, Tamilnadu, IN
2 Rajas International Institute of Technology for Women, Nagercoil, IN
1 Department of Electronics & Telecommunication Engineering, Sathyabama University, Chennai, 600119, Tamilnadu, IN
2 Rajas International Institute of Technology for Women, Nagercoil, IN
Source
Wireless Communication, Vol 4, No 8 (2012), Pagination: 405-408Abstract
The emergence of several radio technologies such as Bluetooth, and IEEE 802.11 operating in the 2.4 GHz unlicensed ISM frequency band may lead to signal interference and result in significant performance degradation when devices are co-located in the same environment. In the normal mode of operation of co located Bluetooth piconets, nodes in different piconets transmit independently of each other, and thus in band co-channel interference is certain to occur from time to time. IEEE 802.11 and Bluetooth, these two operating in the unlicensed 2.4 GHz frequency band are becoming more and more popular in the mobile computing world. The number of devices equipped with IEEE 802.11 and Bluetooth is growing drastically. Result is the number of co-located devices, say within 10 meters, grown to a limit, so that it may causes interference issues in the 2.4 GHz radio frequency spectrum. In this paper, we investigate the interference issues of 2.4 GHz frequency band.Keywords
Bluetooth, IEEE 802.11b, Interference, WLAN.- Data Propelling Scheme for Node Level Congestion Control in WSNs
Abstract Views :162 |
PDF Views:2
Authors
Affiliations
1 Pervasive Computing Technologies, Anna University, Tiruchirappalli, IN
2 TRP Engineering College (SRM Group), Tiruchirappalli, IN
3 Trichirappalli, IN
1 Pervasive Computing Technologies, Anna University, Tiruchirappalli, IN
2 TRP Engineering College (SRM Group), Tiruchirappalli, IN
3 Trichirappalli, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 1 (2010), Pagination:Abstract
The rising data centric Wireless sensor network (WSN) is recently emerging technology, which offers the key to isotonic situation in an un-interruptible environment application. It has the ability of keen observation and ties the information with outside world. WSN tenuously collects the dense amount of data, further communicates with the sink through various intermediate nodes. It delivers reckonable response, when unpredictable variation occurs in the environment. Rushing of the enormous data directs to overcrowd in the routing path, which affects vibrant strength of the network. Many of the existing schemes focused on link level congestion. We propose data propelling scheme, which discusses the congestion free environment in node level congestion. Once congestion notification bit is set, new data buffer node awakened, which is near-by to congested node. After its activation, all the data are re-directed to the data buffer and retrieved back in need even at unusual changes occurred further CN bit is cleared. Aspire is, make processing rate which is to be equal to transmitting rate to avoid funneling effect. Our scheme is not consuming too much of energy of new data buffers and resources. It annotates that nodes are intended for working for long time without human intervention. Further our scheme is concentrating on congestion free critical environmental applications, otherwise which drastically decrease the performance of the network.Keywords
Data Propelling, Congestion Control, Node Level Congestion, Sink.- A New Security on Neural Cryptography with Queries
Abstract Views :193 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, Rajalakshmi Engineering College, Chennai-602105, IN
2 Knowledge Data Centre, Anna University, Chennai-600025, IN
1 Department of Computer Applications, Rajalakshmi Engineering College, Chennai-602105, IN
2 Knowledge Data Centre, Anna University, Chennai-600025, IN
Source
International Journal of Advanced Networking and Applications, Vol 2, No 1 (2010), Pagination: 437-444Abstract
We can generate a secret key using neural cryptography, which is based on synchronization of Tree Parity Machines (TPMs) by mutual learning. In the proposed TPMs random inputs are replaced with queries which are considered. The queries depend on the current state of A and B TPMs. Then, TPMs hidden layer of each output vectors are compared. That is, the output vectors of hidden unit using Hebbian learning rule, left-dynamic hidden unit using Random walk learning rule and right-dynamic hidden unit using Anti-Hebbian learning rule are compared. Among the compared values, one of the best values is received by the output layer. The queries fix the security against majority flipping and geometric attacks are shown in this paper. The new parameter H can accomplish a higher level of security for the neural key-exchange protocol without altering the average synchronization time.Keywords
Majority Attacks, Neural Synchronization, Queries, Tree Parity Machines.- Diabetic Medical Data Classification using Machine Learning Algorithms
Abstract Views :212 |
PDF Views:0
Authors
Affiliations
1 School of Computer Science and Engineering, VIT University, Vellore-632014, IN
1 School of Computer Science and Engineering, VIT University, Vellore-632014, IN
Source
Research Journal of Pharmacy and Technology, Vol 11, No 1 (2018), Pagination: 97-100Abstract
Data mining is the process of analyzing data from different perspectives and summarizing it into a useful information. In this paper we propose a different classification algorithm to identify the accuracy on diabetic data sets. The diabetic person has risk and leads to other disease such as blood vessel damage, blindness, heart diseases, nerve damage and kidney diseases. Diabetics also classified as two types such as type insulin diabetes and non-insulin dependent, diabetes is a disease in which the blood glucose increases which is due to the defects of secretion of insulin, or its action or both. Diabetes is a prolonged medical disease. In diabetes the cells of person produce insufficient amount of insulin or defective insulin or may insulin or may unable use insulin properly and efficiently that further leads to hyperglycemia and type-2 diabetes. We are proposing an efficient two level for classifying data. During initial phase we use training data for analyzing the optimality of dataset then new dataset is formed as optimal training dataset now we apply our classification mechanism on new diabetic datasets. The data mining methods and techniques will be explored to identify suitable methods and techniques for efficient classification on diabetic data set and in mining it in useful patterns.Keywords
Data Mining, Diabetic Dataset, Classification, Naive Bayes Classification, Random Forest.References
- Rahman, R. M. and Afroz, F. Comparison of various classification techniques using different data mining tools for diabetes diagnosis. Journal of Software Engineering and Applications, 2013; 6(03): 85-97.
- R. S. Kamath, Weka Approach for Exploration Mining in Diabetic Patients Database, Chatrapati Shahu Institute of Business Education and Research Kolhapur,India.2013
- Labatut, V and Cherifi, H. Evaluation of performance measures for classifiers comparison. Ubiquitous Computing and Communication Journal, 2011; 6, 2011:21-34
- Kumari, M., Vohra, R., and Arora, A. Prediction of Diabetes Using Bayesian Network, International Journal of Computer Science and Information Technologies, 2014; 5(4) : 5174-5178.
- Keerthana, G., and Srividhya, V. (2014). Performance Enhancement of Classifiers using Integration of Clustering and Classification Techniques. International Journal of Computer Science Engineering 2014;3(3) : 200-203.
- Marom, N. D., Rokach, L., and Shmilovici, A. Using the confusion matrix for improving ensemble classifiers. In 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), 2010:555-559.
- Internet of Things based Remote Patients Observatory System Using Biomedical Sensors
Abstract Views :282 |
PDF Views:0
Authors
Affiliations
1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IN
1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, IN