• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Advances in nano research
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• v.13 no.2
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• pp.121-134
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• 2022

Emerge of nanotechnology impacts all aspects of humans' life. One of important aspects of the nanotechnology and nanoparticles (NPs) is in the food production industry. The safety of such foods is not well recognized and producing safe foods using nanoparticles involves delicate experiments. In this study, we aim to incorporate intelligent computer simulation in predicting safety degree of nanofoods. In this regard, the safety concerns on the nano-foods are addressed considering cytotoxicity levels in metal oxides nanoparticles using adaptive neuro-fuzzy inference system (ANFIS) and response surface method (RSM). Three descriptors including chemical bond length, lattice energy and enthalpy of formation gaseous cation of 15 selected NPs are examined to find their influence on the cytotoxicity of NPs. The most effective descriptor is selected using RSM method and dependency of the toxicity of these NPs on the descriptors are presented in 2D and 3D graphs obtained using ANFIS technique. A comprehensive parameters study is conducted to observe effects of different descriptors on cytotoxicity of NPs. The results indicated that combinations of descriptors have the most effects on the cytotoxicity.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.743-750
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• 2008

Due to unusual climate change and global warming, drought and flood happen frequently not only in Korea but also in all over the world. It leads to the serious damages and injuries in urban areas as well as rural areas. Since the concentration time is short and the flood flows increase urgently in urban stream basin, the chances of damages become large once heavy storm occurs. A decision support system for flood forecasting and warning in urban stream is developed as an alternative to alleviate the damages from heavy storm. It consists of model base management system based on ANFIS (Adaptive Neuro Fuzzy Inference System), database management system with real time data building capability and user friendly dialog generation and management system. Applying the system to the Tanceon river basin, it can forecast and warn the stream flows from the heavy storm in real time and alleviate the damages.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.5
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• pp.553-560
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• 2003

On-site diagnosis of chiller performance is an essential step fur energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for this purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

• Computers and Concrete
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• v.26 no.5
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• pp.397-409
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• 2020

The current study targets to estimate the contribution of the end-anchored FRP composites in resisting shear force using a soft computing tool i.e., adaptive neuro-fuzzy inference system (ANFIS). A total of 107 sets of data accumulated from literature was utilized for the development and evaluation of the current ANFIS model. A comparative analysis between the ANFIS predictions and the acquired experimental results has shown that the ANFIS predictions are in very good agreement with that of experimental ones. Additionally, the accuracy of the current ANFIS model has been weighed up against the estimates of nine widely adopted design guidelines. Based on various statistical parameters, it has been deduced that the effectiveness of the current ANFIS model is better than the considered design guidelines. Besides this, a parametric study was carried out to explore the combined effect of different parameters as well as the impact of individual parameters.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• Journal of the Korea Concrete Institute
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• v.28 no.6
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• pp.723-731
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• 2016

In pretensioned concrete members, a certain bond length from the end of the member is required to secure the effective prestress in the strands, which is defined as the transfer length. However, due to the complex bond mechanism between strands and concrete, most transfer length models based on the deterministic approach have uncertainties and do not provide accurate estimations. Therefore, in this study, Adaptive Neuro-Fuzzy Inference System (ANFIS), a Neuro-Fuzzy System, is introduced to reduce the uncertainties and to estimate the transfer length more accurately in pretensioned concrete member. A total of 253 transfer length test results have been collected from literatures to train ANFIS, and the trained ANFIS algorithm estimated the transfer length very accurately. In addition, a design equation was proposed to calculate the transfer length based on parametric studies and dimensional analyses. Consequently, the proposed equation provided accurate results on the transfer length which are comparable to the ANFIS analysis results.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.47-52
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• 2006

The purpose of this study was to predict successful defibrillation in ventricular fibrillation using parameters extracted by wave analysis method and neuro-fuzzy. Total 15 dogs were tested for predicting successful defibrillation. Feature parameters were extracted for return of spontaneous circulation (ROSC) and non-ROSC by wave analysis method, and these parameters are an irregularity factor, spectral moments, mean power of level-crossing spectrum, and mean of alpha-significant value. Additionally, two parameters by analyzing method of frequency were extracted into a mean of power spectrum and a mean frequency. Then extracted parameters were analyzed in which parameters result to have high performance of discriminating ROSC and non-ROSC by a statistical method of t-test. The average of sensitivity and specificity were 62.5% and 75.0%, respectively. The average of positive predictive factor and negative predictive factor were 61.2% and 75.8%, respectively.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.