A Novel Fuzzy Rule Guided Intelligent Technique for Gray Image Extraction and Segmentation

2013 ◽  
pp. 163-181 ◽  
Author(s):  
Koushik Mondal
Keyword(s):  
Fuzzy Systems ◽  
Domain Knowledge ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Fuzzy Rule ◽  
Absolute Error ◽  
Rule Base ◽  
Process Uncertainty ◽  
Ann Models ◽  
Artificial Neural Network Ann

Image segmentation and subsequent extraction from a noise-affected background, has all along remained a challenging task in the field of image processing. There are various methods reported in the literature to this effect. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods, et cetera. Providing an extraction solution working in unsupervised mode happens to be even more interesting a problem. Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). Literature suggests that effort in this respect appears to be quite rudimentary. This chapter proposes a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, the author takes recourse to effective metrices like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR).

A Novel Fuzzy Rule Guided Intelligent Technique for Gray Image Extraction and Segmentation

2013 ◽  
pp. 303-321
Author(s):  
Koushik Mondal
Keyword(s):  
Fuzzy Systems ◽  
Domain Knowledge ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Fuzzy Rule ◽  
Absolute Error ◽  
Rule Base ◽  
Process Uncertainty ◽  
Ann Models ◽  
Artificial Neural Network Ann

Image segmentation and subsequent extraction from a noise-affected background, has all along remained a challenging task in the field of image processing. There are various methods reported in the literature to this effect. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods, et cetera. Providing an extraction solution working in unsupervised mode happens to be even more interesting a problem. Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). Literature suggests that effort in this respect appears to be quite rudimentary. This chapter proposes a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, the author takes recourse to effective metrices like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR).

scholarly journals Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

2020 ◽  
Vol 51 (3) ◽  
pp. 423-442
Author(s):  
Naser Dehghanian ◽  
S. Saeid Mousavi Nadoushani ◽  
Bahram Saghafian ◽  
Morteza Rayati Damavandi
Keyword(s):  
Flood Control ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ann Model ◽  
Runoff Generation ◽  
Source Areas ◽  
Walnut Gulch ◽  
Artificial Neural Network Ann ◽  
Semi Arid ◽  
Flood Source

Abstract An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

2013 ◽  
pp. 157-165
Author(s):  
SONALI R. MAHAKALE ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Comparative Study ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
Research Work ◽  
Absolute Error ◽  
Image Filtering ◽  
Squared Error ◽  
The Comparative Study ◽  
Work Done

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time.

Wavelet-Based Image Compression Encoding Techniques — A Complete Performance Analysis

2020 ◽  
Vol 20 (02) ◽  
pp. 2050008
Author(s):  
S. P. Raja
Keyword(s):  
Image Compression ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Set Partitioning ◽  
Complete Analysis ◽  
Structural Content ◽  
Image Quality Index

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).

Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

2009 ◽  
Vol 46 (8) ◽  
pp. 955-968 ◽  
Author(s):  
Yusuf Erzin ◽  
S. D. Gumaste ◽  
A. K. Gupta ◽  
D. N. Singh
Keyword(s):  
Hydraulic Conductivity ◽  
Absolute Error ◽  
Silty Sand ◽  
Coefficient Of Determination ◽  
Performance Indices ◽  
Fine Grained ◽  
Centrifuge Tests ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.

scholarly journals Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay

2021 ◽  
Vol 11 (20) ◽  
pp. 9760
Author(s):  
Zhongkai Huang ◽  
Dongmei Zhang ◽  
Dongming Zhang
Keyword(s):  
Pressure Coefficient ◽  
Earth Pressure ◽  
Numerical Data ◽  
Absolute Error ◽  
Ann Model ◽  
Soil Stiffness ◽  
Wall Deflection ◽  
Proposed Model ◽  
Ann Models ◽  
Artificial Neural Network Ann

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

scholarly journals Predicting Displacement Data of Three-Dimensional Reinforced Concrete Frames with Different Strengthening Applications Using ANN

2017 ◽  
Author(s):  
Fatih Bahadır
Keyword(s):  
Reinforced Concrete ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Maximum Load ◽  
Absolute Error ◽  
Concrete Frames ◽  
Rc Frames ◽  
Artificial Neural Network Ann ◽  
Rc Shear Wall

In this study, the artificial neural network (ANN) methodwas used to estimate unavailable displacement data of threedimensional(3D) reinforced concrete (RC) frames with differentstrengthening applications. Four 3D-RC frames wereproduced two storeys and one bay in 1/6 geometric scale withthe deficiencies commonly observed in residential buildingsin Turkey. The first specimen was a bare frame containing nobrick walls and no strengthening. The second specimen wasall brick walls and no strengthening. The third specimen wasstrengthened with an internal steel panel. The fourth specimenwas strengthened with an infilled RC shear wall. The specimenswere tested under reverse cyclic lateral loading and constantvertical loading until failure. This study investigated the estimationof displacement data when the linear variable differentialtransformer of 104 numbers is corrupted and some hystereticloop data are missing. Using the method proposed the unavailableor incorrect displacement data can be predicted by ANNwithout performing any additional experiments. Root meansquared error, coefficient determination, mean absolute error,mean squared error and normalised mean absolute error statisticalvalues were used to compare experimental results withANN model results. These statistical values usually exhibit verylow error rate until a cycle of maximum load is reached.

Hierarchical Fuzzy Rule Interpolation and its Application for Hotels Location Selection

2016 ◽  
Vol 10 (1) ◽  
pp. 55-79 ◽  
Author(s):  
Yanling Jiang ◽  
Shangzhu Jin ◽  
Jun Peng
Keyword(s):  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Practical Investigations ◽  
Curse Of Dimensionality ◽  
Fuzzy Decision Making ◽  
Rule Base ◽  
Location Selection ◽  
Fuzzy Interpolation ◽  
Practical Applications ◽  
International Tourist

Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in systems of only a sparse rule base. However in practical applications, as the application domain of fuzzy systems expand to more complex ones, the curse of dimensionality problem of the conventional fuzzy systems became apparent, which makes the already challenging tasks such as inference and interpolation even more difficult. An initial idea of hierarchical fuzzy interpolation is presented in this paper. The proposed approach combines hierarchical fuzzy systems and fuzzy rule interpolation, to overcome the curse of dimensionality problem and the sparse rule base problem simultaneously. Hierarchical fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and the sub-layer rules base is sparse. In order to demonstrate the potential of this approach, a hierarchical fuzzy decision making model for international tourist hotel location selection is provided in this paper. Criteria are acquired from literatures review and practical investigations for selecting the international tourist hotel location. These supportive systems can be directly presented to the tourists requesting a mechanism for selecting the most appropriate hotel, where lack enough information about the important indicators and factors. This model can also support the managers of hotels who are trying to make strategic decisions regarding the most optimized investments on the indicators of selecting a hotel. An empirical study for identifying the international tourist hotel location selection in Chongqing is conducted to demonstrate the computational results and effectiveness of the proposed methodology.

A Bidirectional Reasoning Based on Fuzzy Interpolation

2020 ◽  
Vol 12 (1) ◽  
pp. 1-14
Author(s):  
Shangzhu Jin
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Curse Of Dimensionality ◽  
Rule Base ◽  
Fuzzy Interpolation ◽  
Initial Idea ◽  
Conventional Methods ◽  
Hierarchical Fuzzy Systems

In order to deal with both the “curse of dimensionality” and the “sparse rule base” simultaneously, an initial idea of hierarchical bidirectional fuzzy interpolation is presented in this article, combining hierarchical fuzzy systems and forward/backward fuzzy rule interpolation. In particular, backward fuzzy interpolation can be employed to allow interpolation to be carried out when certain antecedents of observation variables are absent, whereas conventional methods do not work. Hierarchical bidirectional fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and any sub-layer rule base is sparse. The implementation of this approach is based on fuzzy rule interpolative reasoning that utilities scale and move transformation. An illustrative example and application scenario are provided to demonstrate the efficacy of this proposed approach.

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