Maumita Bhattacharya

Charles Sturt University, Australia
0000-0002-5276-1421
http://csusap.csu.edu.au/~mbhattac/
Publications61
h-index13
Citations652
Highly Influential Citations30
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  • Publications
  • Influence
Intelligent financial fraud detection: A comprehensive review
BYOD Security: A New Business Challenge
TLDR
A taxonomy specifically classifying BYOD security challenges is introduced alongside comprehensive frameworks and solutions which are also analysed to gauge their limitations.
A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria
TLDR
Test results show that while the linear genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and computation time, as compared to LGP and neural networks.
Intelligent Financial Fraud Detection Practices: An Investigation
TLDR
This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques.
Genetic Programming: A Review of Some Concerns
TLDR
This paper surveys the major research works in GP from a critical angle and concludes with a general discussion on "code growth" and other critical aspects of GP techniques, while attempting to highlight on future research directions to tackle such problems.
Evolutionary optimization: A big data perspective
Some Experimental Issues in Financial Fraud Mining
Evolutionary Approaches to Expensive Optimisation
TLDR
This paper discusses some of the key issues involved with use of approximation in evolutionary algorithm, possible best practices and solutions, and how to ensure reliable approximation.
Countering Social Engineering Through Social Media: An Enterprise Security Perspective
TLDR
Concerns of social engineering through social media within the enterprise are examined and countermeasures undertaken to stem ensuing risk are explored.
Surrogate based EA for expensive optimization problems
TLDR
An evolutionary algorithm framework which involves use of surrogate models for fitness function evaluation and a enhanced model which incorporates a multiple-model based learning approach for the support vector machine approximator to counter effects of noise are presented.
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