Dr. R. RAJKUMAR
@sjctnc.edu.in
Assistant Professor, PG and Research Department of Commerce,
St. Joseph's College of Arts and Science (Autonomous), Cuddalore, Tamil Nadu
RESEARCH INTERESTS
Artificial Intelligence, Management, Marketing
Scopus Publications
Scopus Publications
T. Porkodi, R. Rajkumar, R. Sathiya, and M. Senthil Raja
IEEE
The implications of financial fraud are becoming more significant as the risk of it grows. For two main reasons, data mining for the purpose of detecting ATM card fraud is quite difficult. First, datasets about ATM card fraud frequently display significant bias. Second, what constitutes legitimate and fraudulent behavior is defined differently every time. The variables chosen and the sample methods used have a direct bearing on how well ATM card fraud detection algorithms work. Card fraud at ATMs is a major threat to the security of online payment systems and banking. Model training, feature reduction, preprocessing, and procedure selection must all be part of a systematic approach. The importance of preprocessing in avoiding inaccurate model output and keeping data quality high cannot be overstated. As part of the preprocessing step known as feature reduction, correlation-based measures are used to examine feature correlation. After that, the model is trained using AOA-CNN-XGBoost, which produces remarkable outcomes with a 97.13% accuracy rate.
N. Kogila, R. Rajkumar, Sudha Rajesh, and S. Vennila
IEEE
For online stores to offer speedier shipping times, precise sales forecasting is essential. Online advertising campaigns and the competitive nature of comparable but distinct products are only two of many potential variables that could make accurate sales forecasting more difficult. Typically, these characteristics are disregarded in this approach since traditional methods that rely on time series analysis primarily take sales data into account. Preprocessing, feature selection, and training the model must be carried out in an exact sequence. Data quality concerns, missing information, and product grouping are all part of the preprocessing process. In feature selection, a statistical method known as principal component analysis (PCA) is used to reduce the dimensionality of a dataset that contains multiple linked variables. To train the model, Hybrid-ABC-AdaBoost was utilized. Incredibly, the data show that an accuracy of 97.22 % was reached.
R. Rajkumar, N. Kogila, Sudha Rajesh, and A. Rucksar Begum
IEEE
In the modern day, online banking has become the most popular service used by banking users. Banks collect vast amounts of useful information on their customers and their transactions every second. Financial institutions can't gain the insights they need without first securing and properly analyzing this important data utilizing big data analytic methods. The current business climate places a premium on analyzing massive data sets consisting of diverse data in order to unearth previously unseen patterns, market trends, client preferences, and other business insights. The purpose of this research is to suggest a strategy for employing IPSO-SVM to detect and prevent financial fraud in the digital sphere. This investigation introduces an improved particle swarm optimization of support vector machine (IPSO-SVM) technique model for fraud detection by combining optimized particle swarm optimization (IPSO) and support vector machine (SVM). The proposed approach outperforms other two models such as CNN and SVM.
Marketing Management, one of the major functions of the business, facilitates the strongest affiliation of business to the customer through the delivery of what the customer likes, wants, demands and cherish. The brand delivers a clear message about its product and the company, confirms the credibility, motivates the consumer, builds up and concretes the loyalty. Brand Extension has been a commonly accepted marketing strategy used to break the entry barriers between product categories through the carryover of a brand’s reputation. It is important, hence, to study how strong the brands which have already extended have, in reality, grown by studying the level of awareness among the consumers. Hence, there arises a need to understand the reach of brand extension based on the brand awareness of the market under the title, “Quantifying the Level of Awareness on Brand Extension using Index as the Tool”. A renowned/successful brand helps an organization to launch products in new categories more easily. Reduction of the risk perceived by customers, reduction in the promotional expenditure and reduction of the cost of developing a new brand are the benefits of Brand Extension. The reach of Brand Extension has been found to be satisfactory and the level of awareness on Foreign Brands. Brand Extension should be used to improve the CSR capability of the company besides being to enhance the marketing and the profitability of the company.