Database Mining Using Soft Computing Techniques. An Integrated Neural Network−Fuzzy Logic−Genetic Algorithm Approach

2001 ◽  
Vol 41 (2) ◽  
pp. 281-287 ◽  
Author(s):  
Thomas R. Cundari ◽  
Marco Russo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Database Mining ◽  
Soft Computing Techniques ◽  
Genetic Algorithm Approach

scholarly journals Kansei Information Processing in Multimedia Applying Intelligent Soft Computing Techniques

2000 ◽  
Vol 4 (6) ◽  
pp. 428-436 ◽  
Author(s):  
Takehisa Onisawa ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Information Processing ◽  
Soft Computing ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Interactive Genetic Algorithm ◽  
Fuzzy Measures ◽  
Soft Computing Techniques ◽  
Genetic Algorithm Approach

This paper mentions the concept of Kansei information that must be dealt with in multimedia. Kansei information has subjectivity, ambiguity, vagueness and situation dependence. This piece of information is not dealt with by the conventional natural science techniques. This paper also introduces soft computing techniques such as a neural network model, fuzzy set theory, a fuzzy measures and fuzzy integrals model, and the interactive genetic algorithm approach that are applied to Kansei information processing or some related problems.

Soft case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 267-269 ◽  
Author(s):  
WILLIAM CHEETHAM ◽  
SIMON SHIU ◽  
ROSINA O. WEBER
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Network Theory ◽  
Probabilistic Reasoning ◽  
Case Based Reasoning ◽  
Case Representation ◽  
Soft Computing Techniques ◽  
Neural Network Theory ◽  
Case Based

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India

2013 ◽  
Vol 68 (12) ◽  
pp. 2521-2526 ◽  
Author(s):  
A. R. Senthil kumar ◽  
Manish Kumar Goyal ◽  
C. S. P. Ojha ◽  
R. D. Singh ◽  
P. K. Swamee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Correlation Function ◽  
Decision Tree ◽  
Soft Computing ◽  
Auto Correlation ◽  
Soft Computing Techniques ◽  
Tree Algorithms ◽  
Persistence Model

The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.

scholarly journals SOFT COMPUTING SYSTEM FOR THE DIAGNOSIS OF HORMONAL IMBALANCE

2020 ◽  
Vol 7 (6) ◽  
pp. 30-42
Author(s):  
Victor Ekong
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Medical Condition ◽  
Real Life ◽  
Computing System ◽  
Human Existence ◽  
Life Problems ◽  
Hormonal Imbalance ◽  
Soft Computing Techniques

Soft computing, as a science of modelling systems, applies techniques such as evolutionary computing, fuzzy logic, and their hybrids to solve real life problems. Soft computing techniques are quite tolerant to incomplete, imprecise, and uncertainty when dealing with complex situations. This study adopts a hybrid of genetic algorithm and fuzzy logic in diagnosing hormonal imbalance. Hormones are chemical messengers that are vital for growth, reproduction, and are essential for human existence. Hormones may sometimes not be balanced which is a medical condition that often go unnoticed and it’s quite difficult to be diagnosed by medical experts. Hormonal imbalance has several symptoms that could also be confused for other ailments. This proposed system serves as support for medical experts to improve the precision of diagnosis of hormonal imbalance. The study further demonstrates the effective hybridization of genetic algorithm and fuzzy logic in resolving human problems.

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

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