International Journal of Computational Intelligence Studies

Published by Inderscience
Online ISSN: 1755-4985
Print ISSN: 1755-4977
Publications
Since the beginning of the nineteenth century, a significant evolution in optimization theory has been noticed. Classical linear programming and traditional non-linear optimization techniques such as Lagrange’s Multiplier, Bellman’s principle and Pontyagrin’s principle were prevalent until this century. Unfortunately, these derivative based optimization techniques can no longer be used to determine the optima on rough non-linear surfaces. One solution to this problem has already been put forward by the evolutionary algorithms research community. Genetic algorithm (GA), enunciated by Holland, is one such popular algorithm. This chapter provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE). The algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. The chapter explores several schemes for controlling the convergence behaviors of PSO and DE by a judicious selection of their parameters. Special emphasis is given on the hybridizations of PSO and DE algorithms with other soft computing tools. The article finally discusses the mutual synergy of PSO with DE leading to a more powerful global search algorithm and its practical applications.
 
In this paper an efficient approach for human face recognition based on the use of minutiae points in thermal face image is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted. Blood perfusion data are related to distribution of blood vessels under the face skin. In the present work, three different methods have been used to get the blood perfusion image, namely bit-plane slicing and medial axis transform, morphological erosion and medial axis transform, sobel edge operators. Distribution of blood vessels is unique for each person and a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. Two different methods are discussed for extracting minutiae points from blood perfusion data. For extraction of features entire face image is partitioned into equal size blocks and the total number of minutiae points from each block is computed to construct final feature vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition methodologies with varying block size on the database created at our own laboratory. It has been found that the first method supercedes the other two producing an accuracy of 97.62% with block size 16X16 for bit-plane 4.
 
We present a novel test case generation technique using the features of UML 2.0 sequence diagrams. First, we construct the UML sequence diagram of a system. Then, we construct message dependency graph (MDG) from the sequence diagram (SD) and select conditional predicates by traversing MDG. Then, we compute slices corresponding to each conditional predicate. Finally, we generate test cases with respect to a given slicing criterion. Our testing strategy derives test cases using slice test coverage, high path coverage and full predicate coverage criteria. Here, we focus on testing of sequences of messages among objects of use case scenarios. Our technique can be used for system and cluster level testing accommodating the object message and condition information. Thus, our test cases are suitable for detecting object interactions and operational faults. Finally, we have made an analysis and comparison of our approach with the existing approaches, through a case study.
 
This paper presents a continuous ACO approach to solve 0-1 knapsack problem. In this method, groups of candidate values of the components are constructed, and an amount of pheromone is initialised randomly for each candidate value a real random number between 0.1 and 0.9 in each candidate group. To solve binary knapsack problem for each object a candidate group is constructed where candidate value is either 0 or 1. Each ant selects a value from each group to construct a path or a solution. After certain number of generation, store the best solution in a temporary population. When temporary population size is equal to the number of ants, then temporary population will be considered as initial population by re-initialising fresh set of pheromone. This procedure will continue until the maximum generation defined is reached. In experimental section, we compare the results of standard test functions and 0-1 knapsack problem with existing literature.
 
We have proposed an adaptive structure learning of deep belief network (DBN) that can determine the suitable number of hidden layers and hidden neurons of restricted Boltzmann machines (RBMs). The method shows high classification performance to the big data benchmark test. However, the method could not classify the unknown pattern correctly, since an input data with ambiguous patterns leads the classification to the wrong judgment. In such a case, a fine-tuning method that patches a part of network signal flow based on the knowledge will be a helpful method even in terms of both the improvement of classification capability and the reduction of computational cost by learning again. In this paper, network signal patterns which lead the given misclassified patterns were visualised for knowledge acquisition. By fine-tuning the trained network using the acquired knowledge, the classification capability can achieve great success.
 
We have proposed an adaptive structure learning of deep belief network (DBN) that can determine the suitable number of hidden layers and hidden neurons of restricted Boltzmann machines (RBMs). The method shows high classification performance to the big data benchmark test. However, the method could not classify the unknown pattern correctly, since an input data with ambiguous patterns leads the classification to the wrong judgment. In such a case, a fine-tuning method that patches a part of network signal flow based on the knowledge will be a helpful method even in terms of both the improvement of classification capability and the reduction of computational cost by learning again. In this paper, network signal patterns which lead the given misclassified patterns were visualised for knowledge acquisition. By fine-tuning the trained network using the acquired knowledge, the classification capability can achieve great success.
 
An ad-hoc network considers an automatic network formation and maintenance of critical services as nodes come closer or go far away from each other. There is no wired infrastructure or cellular network in wireless ad-hoc network. Each mobile node has an adjustable transmission range. A node n can receive signal from another node m if node n is within the transmission range of the sender m otherwise, two nodes communicate by relaying the message using intermediate nodes. In this paper, we studied how to construct a sparse spanner efficiently for wireless ad-hoc network topology. For any given pair of nodes there is a power efficient path. Power values may be assigned to the nodes in ad-hoc networks, to tackle the topology control issue. This paper considers topology control problems under optimisation objectives to include minimising the maximum power and also overall power. We have tackled the topology control problem, by formulating as a linear programming for the traffic loads and an optimal solution was computed. Theoretical analysis and simulation study verify that the proposed scheme is better up to some extent.
 
Multi-attribute data, needed to be clustered may have different consequences of attributes on clustering criteria. In this paper, a new soft clustering technique is proposed in which similarity measures between data points and impact of each attribute is calculated using grey relational analysis. Algorithm provides the flexibility to choose significant number of attributes for classification purpose using feature subset selection. An iterative approach is adopted to find desired number of clusters having more appropriate and unique centroid. In addition, the use of proposed technique is instanced on software cost estimation because inherent uncertainty in software attributes due to the measurement by expert judgment has a significant impact on estimation accuracy. Combination of clustering and regression technique reduces the potential problem in efficacy of predictive assays due to heterogeneity of the data. Clustered approach creates the subsets of data having a degree of homogeneity that elaborate more accurate prediction.
 
The speed of image denoising by adaptive thresholding approach in Wavelet Transform (WT) domain depends mainly upon the learning algorithm used for optimising the performance of adaptive thresholding function. In this context, in the literature, steepest gradient-based optimisation technique has been used in WT-based thresholding neural network (WT-TNN) approach, which has low learning speed. In this paper, a new computationally efficient approach, that is, Particle Swarm Optimisation (PSO)-based approach has been proposed in place of steepest gradient-based approach. The proposed hybrid computing approach utilises the features of WT-TNN approach and enhances the speed of optimisation by PSO technique. It also yields better performance of denoising as compared to WT-TNN approach. In the proposed approach, crucial problem of initialisation of thresholding parameters gets automatically sorted out besides learning time becoming independent of noise level of the image. The proposed approach also enhances edge preservation, when implemented with bior6.8 wavelet filters.
 
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This paper describes the development of an inference mechanism that is based on a hybrid combination of rule-based reasoning of double stereotypes and decision making techniques. This inference mechanism has been applied in an intelligent medical tutor for atheromatosis. The tutor is called INTATU (INTelligent Atheromatosis TUtor). INTATU provides adaptive tutoring on Atheromatosis for various classes of users depending on their interests, background medical knowledge and computer skills. The adaptivity results from user modelling that is based on stereotypical knowledge about potential users (i.e., patients, patients’ relatives, doctors, medical students, etc. For the design of the system reasoning mechanism, and, thus, the successful incorporation of the decision making theory, an empirical study was conducted. As soon as the full functionality of the reasoning mechanism of the system was implemented, it was evaluated in comparison to the reasoning of human experts. The results of the evaluation offer strong evidence about the effectiveness of the reasoning mechanism and the overall operation of the system.
 
Symbolic regression problems can be solved using grammatical evolution (GE), an evolutionary computation (EC) method, to find a function that coincides satisfactorily with the given datasets. The evolutional approach of GE is based on the grammar learning paradigm, which can translate the genotype (binary digit) into the phenotype (terminals and non-terminals). Unlike traditional codons in a genotype, the fittest codons in phenotype represented by the Backus-Naur form (BNF) are difficult for next generation genes to inherit the traits of parents, accounting for crossover and mutation. For this issue, this article presents a proposal of an advanced improvement to GE using a two-dimensional gene (GE2DG). In contrast to multi-chromosomal GE (GEMC), our proposal not only encloses the two-dimensional gene-expression for symbolic regression, but also introduces one independent gene defined as a conditional statement to express a new BNF grammar of an if-then (-else) branch. In the experiments described herein, continuous/discontinuous non-branch functions and continuous/discontinuous branch functions, four testing patterns, are considered as numerical examples. Results show that GE2DG has better performance than the original GE or GEMC. Especially for the case of branch functions, GE with hybrid chromosome (GEHC), where GE2DG is incorporated with GEMC, has faster convergence in symbolic regression than other methods.
 
ARTE270 is an innovative experimental platform that combines panoramic optical projection video and photography with surround sound. The idea is to immerse the spectator/listener into audiovisual content both in realistic and artistic terms. It initially emerged in order to perform artistic experimentation on data derived from the flourishing natural environment of the Ionian Islands. Nevertheless, in the course of time, it comes up that a very important potential application lies with sustainable development. Our platform has been tried as part of a holistic approach for capturing, preserving and promoting further forms of cultural heritage as well as cultural networking through the new rendering capabilities of digital media.
 
Multiple generations of many durable products serves the market simultaneously. When new products are introduced in generations of product, successive generations have substitution effect on the earlier generations. In this paper, we use the concept of market segmentation in diffusion model for generation product. Different advertising and pricing strategies for two generation product are considered, and the problem is formulated as an optimal control problem. The impact of technological substitution in the market on the optimal advertising and pricing policy is characterised. First, we discuss the evolution of sales dynamics under the assumption that the firm advertises in each segment independently. Further case of a single advertising channel, which reaches several segments with fixed spectrum, is also discussed. The optimal control is applied to study and solve the proposed problem. Theoretical results of two successive generation product are discussed. Differential evolution approach used to solve numerical illustration.
 
Mental state transition network which consists of mental states connected to one another is a basic concept of approximating human psychological and mental responses. It can represent transition from an emotional state to another with stimulus calculated by emotion generating calculations method. In this paper, the agent using mental state transition network can interact with humans to realise smooth communication by two kinds of reinforcement learning methods. Some experimental results can show the variance of human's delicate emotion. The proposed technique can be expected to be an emotional-oriented interface in case of treatment of mental disorder.
 
Top-cited authors
Amit Konar
  • Jadavpur University
Ajith Abraham
  • FLAME University
Swagatam Das
  • Indian Statistical Institute
Debasish Ghose
  • Indian Institute of Science
Krishnanand N. Kaipa
  • University of Maryland, College Park