Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

An exploratory study on job and demographic attributes affecting employee satisfaction in the Indian BPO industry

Abstract: Purpose – The business process outsourcing (BPO) industry in India has always been characterized by ungodly hours, monotonous job, low perceived value, dispirited efficiency resulting to high attrition level. Notwithstanding the ever rising attrition rate, it has become critical for the companies to satisfy their employees in order to retain them. The purpose of this paper is to determine what and how job‐related and demographic variables are associated with employee satisfaction of the BPO employees.Design/methodology/approach – Data collected from 500 middle level BPO employees was analyzed using SPSS 16.0. T‐tests and Duncan's post hoc tests were done to compare the various dimensions of employee satisfaction across selected demographic variables such as gender, marital status, education, age and tenure. Correlation was done to find out the relationship between employee satisfaction and various job characteristics as well as demographic variables and finally, regression was done to find out the actual ...

A new measure of divergence with its application to multi-criteria decision making under fuzzy environment

Abstract: Divergence measure is an important tool for determining the amount of discrimination between two probability distributions. Since the introduction of fuzzy sets, divergence measures between two fuzzy sets have gained attention for their applications in various fields. Exponential entropy measure has some advantages over Shannon's entropy. In this paper, we used the idea of Jensen---Shannon divergence to define a new divergence measure called `fuzzy Jensen-exponential divergence (FJSD)' for measuring the discrimination/difference between two fuzzy sets. The measure is demonstrated to satisfy some very elegant properties, which shows its strength for applications in multi-criteria decision-making problems. Further, we develop a method to solve multi-criteria decision-making problems under fuzzy phenomenon by utilizing the proposed measure and demonstrate by a numerical example.

Fraud detection and frequent pattern matching in insurance claims using data mining techniques

Abstract: Fraudulent insurance claims increase the burden on society. Frauds in health care systems have not only led to additional expenses but also degrade the quality and care which should be provided to patients. Insurance fraud detection is quite subjective in nature and is fettered with societal need. This empirical study aims to identify and gauge the frauds in health insurance data. The contribution of this insurance claim fraud detection experimental study untangle the fraud identification frequent patterns underlying in the insurance claim data using rule based pattern mining. This experiment is an effort to assess the fraudulent patterns in the data on the basis of two criteria-period based claim anomalies and disease based anomalies. Rule based mining results according to both criteria are analysed. Statistical Decision rules and k-means clustering are applied on Period based claim anomalies outliers detection and association rule based mining with Gaussian distribution is applied on disease based anomalies outlier detection. These outliers depict fraud insurance claims in the data. The proposed approach has been evaluated on real-world dataset of a health insurance organization and results show that our proposed approach is efficient in detecting fraud insurance claim using rule based mining.

Trapezoidal fuzzy model to optimize transportation problem

Abstract: The main aim of this paper is to develop an approach based on trapezoidal fuzzy numbers to optimize transportation problem in fuzzy environment. The present algorithm has representation of availability, demand and transportation cost as trapezoidal fuzzy numbers. This algorithm is found quicker in terms of runtime as comparison to fuzzy VAM discussed in [Kaur A., Kumar A., A new method for solving fuzzy transportation problem using ranking function, Appl. Math. Model. 35:5652–5661, 2011; Ismail Mohideen S., Senthil Kumar P., A comparative study on transportation problem in fuzzy environment, Int. J. Math. Res. 2:151–158, 2010]. On the other hand this technique gives much better results than some classical methods like north-west corner and least cost method. Another benefit of this algorithm is that for certain transportation problems it directly gives optimal solution. It is one of the simplest methods to apply and perceive. Practical usefulness of the new method over other existing methods is demonstrated with two numerical examples.