scholarly journals Mammography Images Segmentation Based on Fuzzy Set and Thresholding

2019 ◽  
Vol 29 (3) ◽  
pp. 168
Author(s):  
Ali Mohammed Salih ◽  
Mohammed Y. Kamil
Keyword(s):  
Fuzzy Set ◽  
Fuzzy Inference ◽  
Early Recognition ◽  
Data Set ◽  
Inference System ◽  
Breast Masses ◽  
Qualitative And Quantitative ◽  
Breast Malignancy ◽  
The World ◽  
Quantitative Results

Breast cancer is the most widespread cancer that influences ladies about the world. Early recognition of breast tumor is a standout amongst the hugest variables influencing the probability of recuperation from the illness. Hence, mammography remains the most precise and best device for distinguishing breast malignancy. This paper presents a method for segment the boundary of breast masses regions in mammograms via a proposed algorithm based on fuzzy set techniques. Firstly, it was used data set (mini-MIAS) for evaluate algorithm. it was preprocessing the data set to remove noise and propose a fuzzy set by using fuzzy inference system by generated two input parameters (employs image gradient), then used thresholding filter. Then it was evaluated this proposed method, qualitative and quantitative results were obtained to demonstrate the efficiency of this method and confirm the possibility of its use in improving the diagnosis.

Methodology for Epilepsy and Epileptic Seizure Recognition using Chaos Analysis of Brain Signals

2013 ◽  
pp. 20-36 ◽  
Author(s):  
Seyyed Abed Hosseini ◽  
Mohammed-Reza Akbarzadeh-T ◽  
Mohammed-Bagher Naghibi-Sistani
Keyword(s):  
Epileptic Seizure ◽  
Fuzzy Inference ◽  
Largest Lyapunov Exponent ◽  
Inference System ◽  
Qualitative And Quantitative ◽  
Neuro Fuzzy ◽  
Ictal Eeg ◽  
Quantitative Results ◽  
Anfis Classifier ◽  
Two Phases

A novel combination of chaotic features and Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the Hurst exponent (H), Correlation dimension (D2), Petrosian Fractal Dimension (PFD), and the Largest lyapunov exponent (?). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H, D2, PFD, and ? measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. The inter-ictal EEG-based diagnostic approach achieves 98.6% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 98.1% accuracy. Therefore, the method can be successfully applied to both inter-ictal and ictal data.

Virtual fit evaluation of pants using the Adaptive Network Fuzzy Inference System

2021 ◽  
pp. 004051752110205
Author(s):  
Xueqing Zhao ◽  
Ke Fan ◽  
Xin Shi ◽  
Kaixuan Liu
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Test Accuracy ◽  
Adaptive Network ◽  
Data Set ◽  
Inference System ◽  
Second Stage ◽  
Simulated Environment ◽  
Two Stages ◽  
Fold Cross Validation

Virtual reality is a technology that allows users to completely interact with a computer-simulated environment, and put on new clothes to check the effect without taking off their clothes. In this paper, a virtual fit evaluation of pants using the Adaptive Network Fuzzy Inference System (ANFIS), VFE-ANFIS for short, is proposed. There are two stages of the VFE-ANFIS: training and evaluation. In the first stage, we trained some key pressure parameters by using the VFE-ANFIS; these key pressure parameters were collected from real try-on and virtual try-on of pants by users. In the second stage, we evaluated the fit by using the trained VFE-ANFIS, in which some key pressure parameters of pants from a new user were determined and we output the evaluation results, fit or unfit. In addition, considering the small number of input samples, we used the 10-fold cross-validation method to divide the data set into a training set and a testing set; the test accuracy of the VFE-ANFIS was 94.69% ± 2.4%, and the experimental results show that our proposed VFE-ANFIS could be applied to the virtual fit evaluation of pants.

scholarly journals Fuzzy Algorithm for Power Transformer Diagnostics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde
Keyword(s):  
Gas Chromatography ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Inference System ◽  
Incipient Faults ◽  
The World

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

Technology intervention for preventing COVID-19 outbreak

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prateek Pandey ◽  
Ratnesh Litoriya
Keyword(s):  
Fuzzy Inference ◽  
Inference System ◽  
Content Type ◽  
Research Article ◽  
The People ◽  
Entry Points ◽  
The World ◽  
Technology Intervention ◽  
History Of ◽  
Alternative Routes

PurposeThe purpose for writing this article is derived from the misery and chaos prevalent in the world due to the coronavirus pandemic – since late 2019 and still continuing as of December 2020.Design/methodology/approachA blockchain-based solution to verify the country visit trail and disease and treatment history of the passengers who arrive at the immigration counters located at various national borders and entry points is proposed. A fuzzy inference based suspect identifier system is also presented in this article that could be utilized to make further decisions based on the degree of suspicion observed on a particular passenger.FindingsThis paper attempted to put forth a blockchain-based system which consumes the healthcare and visit trail summary of a passenger (appearing for an interview before an immigration officer) and forwards it to a fuzzy inference system to reach to a conclusion that the passenger should be advised to self-quarantine, detained, or should be allowed to enter. Such a system would help to make correct decisions at the immigration counters to check pandemic diseases, like COVID-19, right at the entry points.Research limitations/implicationsThe implications of this work are manifold. First, the proposed framework works independent of the type of pandemic and is a readymade tool to check the spread of disease through infected human carriers. Second, the proposed framework will keep the mortality rates under check, which would give ample time for the authorities to save the lives of the people with co-morbidities and age vulnerabilities (Vichitvanichphong et al., 2018). Third, it is a general phenomenon to restrict the flights from the country where the first few cases of infection are discovered; however, the infected person, at the same time, might travel through alternative routes. The blockchain-enabled proposed framework ensures the detection of such cases at no other cost. Finally, the solution may appear costly in the first place, but it has the potential to hold back the revenue of the countries that would otherwise be spent on reactive measures.Originality/valueAs of now no other study or research article provides the solution to the biggest problem persists in the world in this way. The contribution is original and worth applying.

scholarly journals Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey

2007 ◽  
Vol 4 (3) ◽  
pp. 1369-1406 ◽  
Author(s):  
M. Firat
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
River Flow ◽  
Fuzzy Inference ◽  
Flow Data ◽  
Data Set ◽  
Inference System ◽  
Forecasting Models ◽  
River Flow Forecasting ◽  
Best Fit

Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe (1818) on Seyhan River between the years 1986 and 2000. The data set are divided into three subgroups, training, testing and verification. The training and testing data set include totally 5114 daily river flow data and the number of verification data points is 731. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN methods. The results of ANFIS, GRNN and FFNN models for both training and testing are evaluated and the best fit forecasting model structure and method is determined according to criteria of performance evaluation. The best fit model is also trained and tested by traditional statistical methods and the performances of all models are compared in order to get more effective evaluation. Moreover ANFIS, GRNN and FFNN models are also verified by verification data set including 731 daily river flow data at the time period 1998–2000 and the results of models are compared. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily River flow forecasting.

Fault Diagnosis of Roller Bearing Conditions Using ANFIS

2009 ◽  
Vol 16-19 ◽  
pp. 886-890 ◽  
Author(s):  
Wen Tao Sui ◽  
Dan Zhang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Roller Bearing ◽  
Back Propagation ◽  
Selection Criterion ◽  
Data Set ◽  
Inference System ◽  
Roller Bearings

This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

Investigation of Silica-Supported Preyssler Nanoparticles as Nanocatalysts in Alkylation of Benzene With 1-Decene Using Artificial Intelligence Approach

2011 ◽  
Vol 2 (4) ◽  
Author(s):  
Ali Hafizi ◽  
Ali Ahmadpour ◽  
Majid M. Heravi ◽  
Fatemeh F. Bamoharram
Keyword(s):  
Fuzzy Inference ◽  
Predictive Ability ◽  
Weight Percent ◽  
Production Yield ◽  
Molar Ratio ◽  
Catalyst Loading ◽  
Data Set ◽  
Inference System ◽  
Linear Alkylbenzene ◽  
Alkylation Of Benzene

Silica-supported Preyssler nanoparticles were synthesized and tested in alkylation of benzene with 1-decene. Adaptive network based fuzzy inference system (ANFIS) was successfully applied for studying the operating parameters of this catalytic reaction. The reaction was carried out at a constant temperature of 80 °C for 2 h, while catalyst loading, catalyst weight percent, and benzene to 1-decene molar ratio (Bz/C10) were chosen as independent variables. Prediction of 1-decene conversion and linear alkylbenzene (LAB) production yield were performed by applying ANFIS method. The predictive ability and accuracy of ANFIS model were examined using unseen experimental data set and R2 was obtained to be 0.994 and 0.995 for 1-decene conversion and LAB production yield, respectively. Experimental results revealed that catalyst loading, Bz/C10 molar ratio, and catalyst weight percent have positive effect on 1-decene conversion, while increase in catalyst loading tends to decrease LAB production yield.

scholarly journals Vibration Analysis and Time Series Prediction for Wind Turbine Gearbox Prognostics

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Sajid Hussain ◽  
Hossam A. Gabbar
Keyword(s):  
Wind Turbine ◽  
Fuzzy Inference ◽  
Health Management ◽  
Time Series Prediction ◽  
Health Condition ◽  
Wind Turbine Gearbox ◽  
Data Set ◽  
Inference System ◽  
Monitoring Index ◽  
Prediction Techniques

Multiple premature failures of a gearbox in a wind turbine pose a high risk of increasing the operational and maintenance costs and decreasing the profit margins. Prognostics and health management (PHM) techniques are widely used to assess the current health condition of the gearbox and project it in future to predict premature failures. This paper proposes such techniques for predicting gearbox health condition index extracted from the vibration signals. The progression of the monitoring index is predicted using two different prediction techniques, adaptive neuro-fuzzy inference system (ANFIS) and nonlinear autoregressive model with exogenous inputs (NARX). The proposed prediction techniques are evaluated through sun-spot data-set and applied on vibration based health related monitoring index calculated through psychoacoustic phenomenon. A comparison is given for their prediction accuracy. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation, and their progression.

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