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Book ChapterDOI

Soft Computing in Bioinformatics

01 Jan 2021-pp 431-446
TL;DR: In this paper, the authors explored the soft computing based techniques for bioinformatics and discussed the necessity of soft computing techniques and their compatibility for solving wide spectrum of bio-informatic related problems.
Abstract: In this chapter, we explored the soft computing based techniques for bioinformatics. Necessity of soft computing techniques and their compatibility for solving wide spectrum of bioinformatics related problems is reviewed. Basics of soft computing techniques are discussed and their relevancy in solving many bioinformatics based problems is also elaborated. Actual experimental results on two real world bioinformatics data demonstrated the efficacy of soft computing techniques over conventional one for biological data problems.
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Journal ArticleDOI
TL;DR: Experimental results show that the performance of the new face-recognition method is better than those of the backpropagation NN (BPNN) system, the hard c-means (HCM) and parallel NNs system, and the pattern-matching system.
Abstract: The face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, we present a method for face recognition based on parallel neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing efficiency decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combined to obtain the recognition result. In particular, the proposed method achieved a 98.75% recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the backpropagation NN (BPNN) system, the hard c-means (HCM) and parallel NNs system, and the pattern-matching system

73 citations

Proceedings Article
14 Feb 2007
TL;DR: A fuzzy hybrid learning algorithm (FHLA) for the radial basis function neural network (RBFNN) which determines the number of hidden neurons in the RBFNN structure by using cluster validity indices with majority rule while the characteristics of the hidden neurons are initialized based on advanced fuzzy clustering.
Abstract: This paper presents a fuzzy hybrid learning algorithm (FHLA) for the radial basis function neural network (RBFNN). The method determines the number of hidden neurons in the RBFNN structure by using cluster validity indices with majority rule while the characteristics of the hidden neurons are initialized based on advanced fuzzy clustering. The FHLA combines the gradient method and the linear least-squared method for adjusting the RBF parameters and the neural network connection weights. The RBFNN with the proposed FHLA is used as a classifier in a face recognition system. The inputs to the RBFNN are the feature vectors obtained by combining shape information and principal component analysis. The designed RBFNN with the proposed FHLA, while providing a faster convergence in the training phase, requires a hidden layer with fewer neurons and less sensitivity to the training and testing patterns. The efficiency of the proposed method is demonstrated on the ORL and Yale face databases, and comparison with other algorithms indicates that the FHLA yields excellent recognition rate in human face recognition.

67 citations

Journal ArticleDOI
TL;DR: In this article, a fuzzy hybrid learning algorithm (FHLA) was proposed for the radial basis function neural network (RBFNN) to determine the number of hidden neurons in the RBFNN structure by using cluster validity indices with majority rule while the characteristics of the hidden neurons are initialized based on advanced fuzzy clustering.

64 citations

Journal ArticleDOI
TL;DR: This paper describes an artificial neuron structure and an efficient learning procedure in the complex domain at incorporating an improved aggregation operation on the complex-valued signals based on the idea underlying the weighted root power mean of input signals.
Abstract: This paper describes an artificial neuron structure and an efficient learning procedure in the complex domain. This artificial neuron aims at incorporating an improved aggregation operation on the complex-valued signals. The aggregation operation is based on the idea underlying the weighted root power mean of input signals. This aggregation operation allows modeling the degree of compensation in a natural manner and includes various aggregation operations as its special cases. The complex resilient propagation algorithm (C-RPROP) with error-dependent weight backtracking step accelerates the training speed significantly and provides better approximation accuracy. Finally, performance evaluation of the proposed complex root power mean neuron with the C-RPROP learning algorithm on various typical examples is given to understand the motivation.

57 citations

Journal ArticleDOI
TL;DR: The implementation of a genetic algorithm is described that provides a fast method of searching for the optimal combination of transcription factor binding sites in a set of regulatory sequences.
Abstract: Summary: The implementation of a genetic algorithm is described that provides a fast method of searching for the optimal combination of transcription factor binding sites in a set of regulatory sequences. Availability: The algorithm can be used transparently as a web service from within the Toucan software. Toucan can be accessed at http://www.esat.kuleuven.ac.be/~saerts/software/toucan.php. A standalone version of the software is available upon request.

55 citations