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.

scholarly journals DRCE Maintainability Model for Component based Systems using Soft Computing Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 2552-2560 ◽  
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Maintenance Cost ◽  
Learning Capability ◽  
Software Designers ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Software Maintainability

Effective software maintainability is one of the most significant and challenging activity in the field of component based software. Several maintainability models are proposed by the researchers to reduce the maintenance cost, to improve the quality and life span of the software product. The proposed model will assist the software designers to develop maintainable softwares. This paper discusses a maintainability model, which selects four crucial factors that highly affect maintainability of component based software system. Soft computing techniques are employed to demonstrate strong correlation of these factors with maintainability. MATLAB’s Fuzzy logic toolbox is used for predicting the maintainability level of component (such as Excellent, Fair, Good, Bad and worst). Data generated by fuzzy model are provided as input to artificial neural network model. Experimental results shows mean absolute error (MAE) to be .028 and Relative Error (RE) to be .045.To further improve the performance of the model; neuro-fuzzy tool was employed. With the use of self learning capability of this tool, MAE and RE are now improved to the value .0029 and .039. It means that the model was sound enough to provide satisfactory outcomes in comparison to neural network.

scholarly journals A Fuzzy MLP Approach for Identification of Nonlinear Systems

2019 ◽  
Vol 65 (1) ◽  
pp. 44-53 ◽  
Author(s):  
A R Marakhimov ◽  
K K Khudaybergenov
Keyword(s):  
Linear Systems ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Neuro Fuzzy ◽  
Non Linear ◽  
Non Linear Systems ◽  
And Performance ◽  
Non Linear System

In case of decision making problems, identification of non-linear systems is an important issue. Identification of non-linear systems using a multilayer perceptron (MLP) trained with back propagation becomes much complex with an increase in number of input data, number of layers, number of nodes, and number of iterations in computation. In this paper, an attempt has been made to use fuzzy MLP and its learning algorithm for identification of non-linear system. The fuzzy MLP and its training algorithm which allows to accelerate a process of training, which exceeds in comparing with classical MLP is proposed. Results show a sharp reduction in search for optimal parameters of a neuro fuzzy model as compared to the classical MLP. A training performance comparison has been carried out between MLP and the proposed fuzzy-MLP model. The time and space complexities of the algorithms have been analyzed. It is observed, that number of epochs has sharply reduced and performance increased compared with classical MLP.

scholarly journals Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

2020 ◽  
Vol 12 (10) ◽  
pp. 4045
Author(s):  
Muhammad Muhitur Rahman ◽  
Md Shafiullah ◽  
Syed Masiur Rahman ◽  
Abu Nasser Khondaker ◽  
Abduljamiu Amao ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Quality ◽  
Soft Computing ◽  
Air Quality Modeling ◽  
Atmospheric Environment ◽  
Quality Data ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Quality Modeling ◽  
Computing Models

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

On the Use of Soft Computing Techniques for Web Personalization

2011 ◽  
pp. 2381-2402
Author(s):  
G. Castellano ◽  
A.M. Fanelli ◽  
M.A. Torsello
Keyword(s):  
Soft Computing ◽  
Domain Knowledge ◽  
User Preferences ◽  
Web Personalization ◽  
Web Based ◽  
Hybrid Schemes ◽  
Web Usage ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Usage Data

Due to the growing variety and quantity of information available on the Web, there is urgent need for developing Web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web personalization that finds in soft computing (SC) techniques a valid tool to handle uncertainty in Web usage data and develop Web-based applications tailored to user preferences. The main reason for this success seems to be the synergy resulting from SC paradigms, such as fuzzy logic, neural networks, and genetic algorithms. Each of these computing paradigms provides complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. In this chapter, we emphasize the suitability of hybrid schemes combining different SC techniques for the development of effective Web personalization systems. In particular, we present a neuro-fuzzy approach for Web personalization that combines techniques from the fuzzy and the neural paradigms to derive knowledge from Web usage data and represent the knowledge in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used to dynamically suggest interesting links to the user of a Web site.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

Fuzzy Systems ◽  
2017 ◽  
pp. 682-714 ◽  
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

2017 ◽  
pp. 293-325
Author(s):  
Swati Aggarwal ◽  
Venu Azad
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Hybrid System ◽  
Early Stage ◽  
Data Set ◽  
Hybrid Neural Network ◽  
Mlp Neural Network ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Continuous Attribute

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

scholarly journals A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

2020 ◽  
Vol 10 (2) ◽  
pp. 635 ◽  
Author(s):  
Yingli LV ◽  
Qui-Thao Le ◽  
Hoang-Bac Bui ◽  
Xuan-Nam Bui ◽  
Hoang Nguyen ◽  
...  
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Soft Computing Techniques ◽  
Residuals Analysis ◽  
Computing Models

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals

2018 ◽  
Vol 2672 (12) ◽  
pp. 32-39 ◽  
Author(s):  
Aksel Seitllari ◽  
M. Emin Kutay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Embedment Depth ◽  
Test Results ◽  
Multilinear Regression ◽  
Neuro Fuzzy ◽  
Chip Seal ◽  
Regression Techniques ◽  
Artificial Neural

In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of chip seal percent embedment depth ( Pe). The model uses inputs such as cumulative equivalent traffic volume, Vialit test results, dust content of aggregates, and initial embedment depth. Multilinear regression, adaptive neuro-fuzzy system, and artificial neural network techniques were used to estimate the Pe. The contribution of the variables affecting Pe was evaluated through a sensitivity analysis. The results indicate that while most of the proposed models were able to predict the Pe reasonably, the artificial neural network model performed the best.

scholarly journals A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Nikolaos Karkalos ◽  
Nikolaos Efkolidis ◽  
Panagiotis Kyratsis ◽  
Angelos Markopoulos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Model ◽  
Soft Computing ◽  
Regression Models ◽  
Fuzzy Inference ◽  
Process Conditions ◽  
Neural Network Models ◽  
Inference System ◽  
Computing Models

Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

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