Informatics Publishing Limited

N. Ranjith ¹, A. Marimuthu ²

Affiliations
1 Karpagam University, Coimbatore - 641021, Tamil Nadu, IN
2 Government Arts College, Coimbatore - 641018, Tamil Nadu, IN

Abstract

Background/Objectives: To select optimal software requirements by introducing Multi-objective Teacher-Learning- Artificial Bee Colony Optimization. Methods/Statistical Analysis: Teaching learning based optimization for the multi-objective software requirements selection has two objectives of minimized cost and maximum client satisfaction. Similarly the constraints namely interaction constraints and cost threshold constraints are considered. However, the efficiency of the software product development can be improved further when more efficient optimization techniques is used for the selection of software requirements along with consideration of more objectives and more constraints in larger real datasets. Findings: In this article, a hybrid optimization technique named Multi-Objective Teacher-Learning Artificial Bee Colony Optimization (MOTLABC) is proposed with set of multiple objectives and constraints. The objectives are minimum cost, maximum client satisfaction, minimum time consumption and maximum reliability. The constraints such as time threshold constraint, interaction constraints and cost threshold constraints are considered. The hybrid approach of MOTLABC with the above objectives improves the collection of set of needs for the development of the software. The Pareto optimal problem occurs in multi objective optimization solutions is resolved by the use of Pareto tournament function. Improvements/Applications: The experimental consequences prove that they obtained results perform improved than algorithms proposed in the literature.

Keywords

Artificial Bee Colony Optimization, Interaction Constraints, Software Requirements Selection, Teaching Learning Based Optimization.

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A. Devi ¹, A. Marimuthu ²

Affiliations
1 Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, IN
2 Department of Computer Science, GovernmentArts College (Autonomous), Coimbatore, IN

Abstract

Facial recognition system is fundamental a computer application for the automatic identification of a person through a digitized image or a video source. The major cause for the overall poor performance is related to the transformations in appearance of the user based on the aspects akin to ageing, beard growth, sun-tan etc. In order to overcome the above drawback, Self-update process has been developed in which, the system learns the biometric attributes of the user every time the user interacts with the system and the information gets updated automatically. The procedures of Plastic surgery yield a skilled and endurable means of enhancing the facial appearance by means of correcting the anomalies in the feature and then treating the facial skin with the aim of getting a youthful look. When plastic surgery is performed on an individua~ the features of the face undergo reconstruction either locally or globally. But, the changes which are introduced new by plastic surgery remain hard to get 11Wdeled by the available face recognition systems and they deteriorate the performances of the face recognition algorithm Hence the Facial plastic surgery produces changes in the facial features to larger extent and thereby creates a significant challenge to the face recognition system This work introduces a fresh Multi11Wdal Biometric approach making use of novel approaches to boost the rate of recognition and security. The proposed method consists of various processes like Face segmentation using Active Appearance Model (AAM), Face Normalization using Kernel Density Estimate/Point Distribution Model (KDE-PDM), Feature extraction using Local Gabor XOR Patterns (LGXP) and Classification using Independent Component Analysis (lCA). Efficient techniques have been used in each phase of the FRAS in order to obtain improved results.

Keywords

Facial Recognition System, Self-Update Procedure, ActiveAppearance Model (AAM), Kernel Density Estimate/Point Distribution Model (KDE-PDM), Independent Component Analysis (ICA).

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