Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

This study presents an adaptive neuro-fuzzy inference system (ANFIS) approach performed to estimate the number of adverse events where the dependent variables are adverse events leading to four types of variables: number of people killed, wounded, hijacked and total number of adverse events. Fourteen infrastructure development projects were selected based on allocated budgets values at different time periods, population density, and previous month adverse event numbers selected as independent variables. Firstly, number of independent variables was reduced by using ANFIS input selection approach. Then, several ANFIS models were performed and investigated for Afghanistan and the whole country divided into seven regions for analysis purposes. Performances of models were assessed and compared based on the mean absolute errors. The difference between observed and estimated value was also calculated within \({\pm }1\) range with values around 90 %. We included multiple linear regression (MLR) model results to assess the predictive power of the ANFIS approach, in comparison to a traditional statistical approach. When the model accuracy was calculated according to the performance metrics, ANFIS showed greater predictive accuracy than MLR analysis, as indicated by experimental results. As a result of this study, we conclude that ANFIS is able to estimate the occurrence of adverse events according to economical infrastructure development project data.

Enhancing aircraft crack repair efficiency through novel optimization of piezoelectric actuator parameters: A design of experiments and adaptive neuro-fuzzy inference system approach

In today’s competitive world, most organizations need to successfully develop new products. The success of new products can be a competitive weapon and advantage for a firm to survive in the current dynamic markets. The most important aspect of the product design is identifying customers’ needs of products. One of the most common methods to satisfy customers’ needs is improvement of customers’ satisfaction. The present paper applies the aspects of the 4P marketing mix (product, price, place, and promotion) for modeling the relationship between customers’ satisfaction and new product design with handling nonlinearity as well as fuzziness. A methodology based on the adaptive neuro-fuzzy inference systems (ANFIS) approach is presented to improve customers’ satisfaction while setting products’ design attributes in a fuzzy environment. The intelligent approach of the present study is then applied to predict customers’ satisfaction and design new product through 4P marketing mix concept in an actual case in the freezer refrigerator industry. A complete sensitivity analysis is run to assess significance or influence of each input variable on customers’ satisfaction. The superiority of ANFIS is proved by error analysis. The proposed approach would help practitioners within the field of marketing and new product design teams to enhance customers’ satisfaction and set new products’ attributes.

The energy and draft requirements of a disk plow have been recognized as essential factors when attempting to correctly match it with tractor power. This study examines the possible of using an adaptive neuro-fuzzy inference system (ANFIS) approach and its performance compared to a multiple linear regression (MLR) model to determine the energy and draft requirements of a disk plow. A total of 133 data patterns were obtained by conducting experiments in the field and from the literature. Of these 133 data points, 121 were arbitrarily selected and used for training, and the remaining 12 were used for testing the models. The input variables were plowing depth, plowing speed, soil texture index, initial soil moisture content, initial soil bulk density, disk diameter, disk angle, and disk tilt angle, and output variable was draft of the disk plow. Four membership functions were used with ANFIS: a triangular membership function, generalized bell-shaped membership function, trapezoidal membership function, and Gaussian curve membership function. An evaluation of the outcomes of the ANFIS and MLR modeling shows that the triangular membership function performed better than the other functions. When the ANFIS model draft predictions were compared to the measured values, the average relative error was –1.97%. A comparison of the ANFIS model with other approaches showed that the energy and draft requirements of the disk plow could be estimated with satisfactory accuracy. Keywords: ANFIS, MLR, disk plow, draft, tillage DOI: 10.25165/j.ijabe.20201302.4077 Citation: Al-Dosary N M N, Al-Hamed S A, Aboukarima A M. Application of adaptive neuro-fuzzy inference system to predict draft and energy requirements of a disk plow. Int J Agric & Biol Eng, 2020; 13(2): 198–207.

Development of regional groundwater model isan important phase to understand the mechanism of groundwater resource and its sustainability. This model is extensively restricted by the requirement of large number of field data and specific interconnected constraints. A choice of intelligent soft computing technique is always constantly open for such model development. Recently ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro Fuzzy Inference System) techniques are having a massive strength to deal with such interconnected constrains model. The present study is carried outto perform the comparative prediction between ANN, ANFIS, CWTFT-ANN (Continuous Wavelet Fast Fourier Transform), CWTFT-ANFIS, WT-ANN (Wavelet Transform) and WT-ANFIS on Groundwater Level prediction at different reaches (Top, Middle and End) of Lower Bhavani River Basin (LBRB) on monthly stress basis(from 2009 to 2015). From the results, the performance of CWTFT-ANFIS is 9.36%, 13.3% and 4.45% better than the ANFIS prediction and the performance of CWTFT-ANN is 47.17%, 25.6% and 47.45% better than the ANN prediction at Top, Middle and End reaches of LBRB respectively. Overall the prediction of CWTFT-ANFIS is about15.3% better than the other identified models, which is further fed for the forecasting of groundwater level to next one time stress level.

Lean Six Sigma (LSS) is a manufacturing strategy that aims at improving the firm's competitiveness and operational performance through waste reduction and process variation. Sustainable manufacturing aims at creating manufactured products with minimal negative environmental impacts. In order to facilitate zero defects and improve their sustainable performance, manufacturing firms have started to adopt both LSS and sustainable manufacturing strategies to attain integrated benefits. This article reports a research carried out to analyse the performance of LSS system integrated with sustainability considerations using multi grade fuzzy (MGF) and adaptive neuro fuzzy inference system (ANFIS) approaches. During this research, a performance assessment model was designed. The score based on MGF approach is found to be 6.74 and that of ANFIS approach is 6.51. Based on computation, the case organisation was found to possess 'strong LSS performance with sustainability considerations'. The study could facilitate improvement in LSS performance incorporated with sustainability aspects.

The primary purpose of this study was to apply structural equation modeling (SEM) integrated with an adaptive neuro-fuzzy inference system (ANFIS) approach to model the safety culture of the petrochemical industry of Japan. Workers from five companies located in the Chugoku region of Japan completed a paper-based survey distributed by email. SEM and ANFIS methods were integrated in order to identify and model the important factors of the safety culture. The results of SEM indicate that employee attitudes toward safety, coworker’s support, work pressure, and plant safety management systems were significant factors influencing violation behavior, personnel safety motivation, and personnel error behavior. Furthermore, the application of the ANFIS modeling approach showed that employees’ attitude was the most critical predictor of violation behavior and personnel error behavior, while coworkers support was the most critical predictor in modeling personnel safety motivation.

Modeling of dimmable High Power LED illumination distribution using ANFIS on the isolated area

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

Due to the inherent highly nonlinear vehicle state error dynamics obtained from low-cost inertial navigation system (INS) and global positioning system (GPS) along with the unknown statistical properties of these sensors, the optimality/accuracy of the classical Kalman filter for sensor fusion is not guaranteed. Therefore, in this paper, low-cost INS/GPS measurements integration is optimized based on different Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures to achieve high-accuracy vehicle state estimates. The proposed approaches involve the use of NN based architectures as well as ANFIS architectures with overlapping windows for delayed input signals. Both the NN approaches and the ANFIS approaches are used once with overlapping position windows as the input and once with overlapping position and velocity windows as the input. Experimental tests are conducted to evaluate the accuracy of the proposed AI approaches. The obtained results are presented and discussed. The study concludes that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. This Input Delayed ANFIS (IDANFIS) approach is further analyzed at the end of the paper.

Predictability of the bending rigidity of cotton plain-woven fabrics from their structural parameters has been investigated using an adaptive neuro-fuzzy inference system (ANFIS) approach. A set of cotton grey fabrics meant for apparel end use was desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. Fabric weight, fabric thickness, and fabric cover constituted the input parameters for the model, whereas overall bending rigidity of the fabric was the single output parameter. Fabric data-set has been modeled using ANFIS and its prediction potential is compared with that of an artificial neural network (ANN) model. A sensitivity analysis was also carried out to investigate the robustness of developed model. Results show that the learning capability of the ANFIS model is superior and its generalization ability is slightly better than that of a standalone ANN model.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

<p>The release of dangerous chemicals in flood crises is a common and recurring phenomenon. Because due to the breakdown of the infrastructure in the year, the possibility of release of dangerous pollutants is expected more than before [1]. The present research has chosen a chemical factory with Glycerin tanks as a case study in the city of Liberec (Czech Republic). As acute exposure to these compounds leads to effects such as headaches, dizziness, bloating, nausea, vomiting, thirst, and diarrhea [2], the control of this pollution can significantly reduce the health risk impacts especially during floods. In the first step of this study, all the physics coordinates of the reservoirs were examined and evaluated in different conditions. In the next step, according to the experimental calculations, the volume of Glycerin release from the reservoirs and in the flood flow was evaluated. Risk analysis was done using the HAZard & OPerability study (HAZOP) technique. Risk Number in the HAZOP method is computed based on two factors containing Risk Possibility (RP) and Risk Intensity (RI) [3]. Both the values are determined according to prediction of Adaptive Neuro Fuzzy Inference System (ANFIS) [4] based on previous studies in different countries. The results demonstrated that the integrated HAZOP-ANFIS model has different performance in various flood flow conditions.</p> <p>RP value is predicted abased on three parameters include; rainfall value, mass of contaminant, and flood flow. Likewise, the RI is estimated in ANFIS method according to self-assimilative factor and continuity of floods [4]. The computations demonstrated that ANFIS technique has more than 0.9 correlation coefficient for prediction of both flood risk factors (RP and RI). Likewise, the sensitivity analysis of the prediction system is examined as per all the declared physical features which effects on RP and RI. Also, it should be mentioned that in the Glycerin content is in the range of 45%- 65% in the case study. Numerical analysis illustrated the performance of designed framework has more efficiency in the higher concentrations of the contamination. The suggested structure can be used as an early qualitative framework for acute effects of hazardous material emissions.</p> <p><strong>Keywords:</strong> <em>HAZOP risk assessment; ANFIS; Flood; Glycerin emissions; Chemical plant</em></p> <p>

In this paper, breast cancer detection using convolutional neural network for mammogram imaging system is proposed to classify mammogram image into normal, benign(non-cancerous abnormality) and malignant (cancerous abnormality). Breast Cancer detection Using Convolutional Neural Networks (BCDCNN) is aimed to speed up the diagnosis process by assisting specialist to diagnosis and classification the breast cancer. A series of mammogram images are used to carry out preprocessing to convert a human visual image into a computer visual image and adjust suitable parameter for the CNN classifier. After that, all changed images are assigned into CNN classifier as training source. The CNN classifier will then produce a model to recognize the mammogram image. By comparing BCDCNN method with Mammogram Classification Using Convolutional Neural Networks (MCCNN), BCDCNN has improved the accuracy toward classification on the mammogram images. Thus, the results show that the proposed method has higher accuracy than other existing methods, mass only and all argument have been increased from 0.75 to 0.8585 and 0.608974 to 0.8271 accuracy.

Thispaperproposes the AdaBoost metalearning methodology to combine the outcomesof tree-based models of classification and the regression tree (CART)algorithm for estimating the equilibrium dissociation temperatureof clathrate hydrates. In addition to the AdaBoost-CART models, modelsbased on the adaptive neuro-fuzzy inference system (ANFIS) and artificialneural network (ANN) approaches were also developed. Training andtesting of the models were done utilizing a gathered database of morethan 3500 experimental data on incipient dissociation conditions ofCO2 and other hydrate systems. With the average absoluterelative deviation percent (AARD%) between 0.03 and 0.07, 0.04 and1.09, and 0.09 and 1.01, which were obtained by the presented AdaBoost-CART,ANFIS, and ANN models, respectively, the targets were reproduced withsatisfactory accuracy. However, for all of the studied clathrate hydratesystems, the proposed AdaBoost-CART models provide more reliable results.Indeed, the obtained AARD% values for tree-based models are lowerthan those of other models.