U. Srinivasulu Reddy

Planted (l, d) - Motif Finding using Particle Swarm Optimization

In Bioinformatics, Motif Finding is one of the most popular problems, which has many applications. Generally, it is to locate recurring patterns in the sequence of nucleotides or amino acids. As we can’t expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NPcomplete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. The most of the algorithms suffer with local optima. Particle swarm optimization (PSO) is a new global optimization technique which has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have adopted the features of the PSO to solve the Planted Motif Finding Problem and have designed a sequential algorithm. We have performed experiments with simulated data it outperforms MbGA and PbGA. The PMbPSO also applied for real biological data sets and observe that the algorithm is also able to detect known TFBS accurately when there are no mutations. General Terms: Evolutionary Optimization Techniques, Bioinformatics, Computational Biology.

Cost-sensitive Risk Induced Bayesian Inference Bagging (RIBIB) for credit card fraud detection

Abstract Credit card fraud represents one of the biggest threats for organizations due to the probability of huge losses associated with them. This paper presents a cost-sensitive Risk Induced Bayesian Inference Bagging model, RIBIB, for credit card fraud detection. RIBIB proposes a novel bagging architecture incorporating a constrained bag creation method, a Risk Induced Bayesian Inference method as a base learner and a cost-sensitive weighted voting combiner. Experiments on Brazilian Bank data indicate 1.04–1.5 times reduced cost. Experiments on UCSD-FICO data exhibit robustness of the model in handling unseen data without any need for domain specific parameter fine-tuning.

Person identification system using feature level fusion of multi-biometrics

The use of unimodal biometric system is very low because of physiological defects, modes of user and their environment. Some of those drawbacks are alleviated by providing same identity for multiple evidences. Here a multimodal biometric system is proposed based on LBP, PCA and probabilistic neural network (PNN). In proposed method LBP extracted the Face features from face images and those features are given as input to PCA that generates Face Feature Vector with reduced Dimensions. Finger features are extracted from Fingerprint images using LBP and those features are given as input to PCA that generates Finger Feature Vector with reduced Dimensions. Using LBP, the distinct textual features of face and fingerprint are extracted. Weighted Summation Fusion method is used to combine these unimodal features. A probabilistic neural network is used as Classifier. An average recognition rate of 97.5% achieved with proposed method. Proposed method show that the proposed algorithm requires low computational cost.

A Short Review for Mobile Applications of Sentiment Analysis on Various Domains

Sentiment analysis is an emerging filed with Natural Language Processing and Web Mining which provides a way for the decision making process in various domains such as marketing, education, health care, financial and human resource management etc. Business development people working on various sectors finds emotions of the customers on social networks, web communities, blogs and other online communication media. People mostly express their opinion about the products, organization, their health status and the services of public and private sectors in those communication media without any hesitation which helps others to get benefitted or caution. And also people think that it would be comfortable to have an application on their mobile phone which does the sentiment analysis process locally in their mobile phone. In this paper, we have discussed the few applications of sentiment analysis using efficient classification techniques on different domains such as marketing, health care and education. Issues found in various sentiment analysis based applications can be eliminated and can implementing those applications on mobile phones.