IJARAI Volume 1 Issue 9

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: An Optimization of Granular Networks Based on PSO and Two-Sided Gaussian Contexts

Abstract: This paper is concerned with an optimization of GN (Granular Networks) based on PSO (Particle Swarm Optimization) and Information granulation). The GN is designed by the linguistic model using context-based fuzzy c-means clustering algorithm performing relationship between fuzzy sets defined in the input and output space. The contexts used in this paper are based on two-sided Gaussian membership functions. The main goal of optimization based on PSO is to find the number of clusters obtained in each context and weighting factor. Finally, we apply to coagulant dosing process in a water purification plant to evaluate the predication performance and compare the proposed approach with other previous methods.

Author 1: Keun Chang Kwak

Keywords: granular networks, particle swarm optimization, linguistic model, two-sided Gaussian contexts

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Paper 2: A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization

Abstract: This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control over the design space reduces unnecessary objective function evaluations. The algorithm’s novel arrangement of genetic operations provides fast and robust convergence to multiple local optima. Benchmark tests alongside three other multi-niching algorithms show that the CMN GA has a greater convergence ability and provides an order-of-magnitude reduction in the number of objective function evaluations required to achieve a given level of convergence.

Author 1: Matthew Hall

Keywords: genetic algorithm; cumulative; memory; multi-niching; multi-modal; optimization; metaheuristic.

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Paper 3: Method for 3D Object Reconstruction Using Several Portion of 2D Images from the Different Aspects Acquired with Image Scopes Included in the Fiber Retractor

Abstract: Method for 3D object reconstruction using several portions of 2D images from the different aspects which are acquired with image scopes included in the fiber retractor is proposed. Experimental results show a great possibilityfor reconstruction of acceptable quality of 3D object on the computer with several imageswhich are viewed from the different aspects of 2D images.

Author 1: Kohei Arai

Keywords: 3D image reconstruction, fiber retractor, image scope

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Paper 4: LSVF: a New Search Heuristic to Reduce the Backtracking Calls for Solving Constraint Satisfaction Problem

Abstract: Many researchers in Artificial Intelligence seek for new algorithms to reduce the amount of memory/ time consumed for general searches in Constraint Satisfaction Problems. These improvements are accomplished by the use of heuristics which either prune useless tree search branches or even indicate the path to reach the (optimal) solution faster than the blind version of the search. Many heuristics were proposed in the literature, like the Least Constraining Value (LCV). In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First: a solution whenever the LCV solely cannot measure how much a value is constrained. In this paper, we present a pedagogical example, as well as the preliminary results.

Author 1: Cleyton Rodrigues
Author 2: Ryan Ribeiro de Azevedo
Author 3: Fred Freitas
Author 4: Eric Dantas

Keywords: Backtracking Call, Constraint Satisfaction Problems, Heuristic Search.

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Paper 5: Measures for Testing the Reactivity Property of a Software Agent

Abstract: Agent technology is meant for developing complex distributed applications. Software agents are the key building blocks of a Multi-Agent System (MAS). Software agents are unique in its nature as it possesses certain distinctive properties such as Pro-activity, Reactivity, Social-ability, Mobility etc., Agent’s behavior might differ for same input at different cases and thus testing an agent and to evaluate the quality of an agent is a tedious task. Thus the measures to evaluate the quality characteristics of an agent and to evaluate the agent behavior are lacking. The main objective of the paper is to come out with a set of measures to evaluate agent’s characteristics in particular the reactive property, so that the quality of an agent can be determined.

Author 1: N. Sivakumar
Author 2: K.Vivekanandan

Keywords: Software Agent, Multi-agent system, Software Testing

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Paper 6: Method for Face Identification with Facial Action Coding System: FACS Based on Eigen Value Decomposion

Abstract: Method for face identification based on eigen value decomposition together with tracing trajectories in the eigen space after the eigen value decomposition is proposed. The proposed method allows person to person differences due to faces in the different emotions. By using the well known action unit approach, the proposed method admits the faces in the different emotions. Experimental results show that recognition performance depends on the number of targeted peoples. The face identification rate is 80% for four peoples of targeted number while 100% is achieved for the number of targeted number of peoples is two.

Author 1: Kohei Arai

Keywords: face recognitio; action unit; face identification

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Paper 7: Analysis of Gumbel Model for Software Reliability Using Bayesian Paradigm

Abstract: In this paper, we have illustrated the suitability of Gumbel Model for software reliability data. The model parameters are estimated using likelihood based inferential procedure: classical as well as Bayesian. The quasi Newton-Raphson algorithm is applied to obtain the maximum likelihood estimates and associated probability intervals. The Bayesian estimates of the parameters of Gumbel model are obtained using Markov Chain Monte Carlo(MCMC) simulation method in OpenBUGS(established software for Bayesian analysis using Markov Chain Monte Carlo methods). The R functions are developed to study the statistical properties, model validation and comparison tools of the model and the output analysis of MCMC samples generated from OpenBUGS. Details of applying MCMC to parameter estimation for the Gumbel model are elaborated and a real software reliability data set is considered to illustrate the methods of inference discussed in this paper.

Author 1: Raj Kumar
Author 2: Ashwini Kumar Srivastava
Author 3: Vijay Kumar

Keywords: Probability density functio; Bayes Estimation; Hazard Function; MLE; OpenBUGS; Uniform Priors.

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Paper 8: Hand Gesture recognition and classification by Discriminant and Principal Component Analysis using Machine Learning techniques

Abstract: This paper deals with the recognition of different hand gestures through machine learning approaches and principal component analysis. A Bio-Medical signal amplifier is built after doing a software simulation with the help of NI Multisim. At first a couple of surface electrodes are used to obtain the Electro-Myo-Gram (EMG) signals from the hands. These signals from the surface electrodes have to be amplified with the help of the Bio-Medical Signal amplifier. The Bio-Medical Signal amplifier used is basically an Instrumentation amplifier made with the help of IC AD 620.The output from the Instrumentation amplifier is then filtered with the help of a suitable Band-Pass Filter. The output from the Band Pass filter is then fed to an Analog to Digital Converter (ADC) which in this case is the NI USB 6008.The data from the ADC is then fed into a suitable algorithm which helps in recognition of the different hand gestures. The algorithm analysis is done in MATLAB. The results shown in this paper show a close to One-hundred per cent (100%) classification result for three given hand gestures.

Author 1: Sauvik Das Gupta
Author 2: Souvik Kundu
Author 3: Rick Pandey
Author 4: Rahul Ghosh
Author 5: Rajesh Bag
Author 6: Abhishek Mallik

Keywords: Surface EMG; Bio-medical; Principal Component Analysis; Discriminant Analysis

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