Adaptive Neuro-fuzzy Inference System Technique Research Articles

A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques

The main purpose of this paper is to develop an efficient multi-stage methodology to predict carbon dioxide emissions based on two important variables including the energy consumption and economic growth using the clustering, prediction machine learning techniques, and dimensionality reduction. To do so, we use the self-organizing map clustering algorithm to cluster the data and the adaptive neuro-fuzzy inference system and artificial neural network to construct the prediction models in each cluster of the self-organizing map to predict carbon dioxide emissions considering a set of input parameters including economic growth and energy consumption in Group 20 nations. Furthermore, we use the singular value decomposition for dimensionality reduction and missing values’ prediction in the dataset. The results of the analysis of a real-world dataset found that the developed multi-stage approach was capable of predicting the carbon dioxide emissions on two indicators. To validate the proposed method, the results are compared with other existing methods. The outcomes demonstrate that the adaptive neuro-fuzzy inference system and artificial neural network techniques combined with the self-organizing map and singular value decomposition technique provide 0.065 accuracy in terms of the mean average error. In addition, when comparing singular value decomposition-self-organizing map-adaptive neuro-fuzzy inference system method with the singular value decomposition-self-organizing map-adaptive-artificial neural network method, the singular value decomposition-self-organizing map-adaptive neuro-fuzzy inference provides with 0.104 accuracy in predicting CO2 emissions. Moreover, the multiple linear regression provides the worst accuracy (0.522) results compared with the artificial neural network and adaptive neuro-fuzzy inference system techniques. The analysis regarding the relationship between economic development, carbon dioxide emissions, and the energy consumption is extremely vital from the energy and economic policy-making aspects in Group 20 countries given that the primary focus of this group has been the governance of the global economy.

Personalised healthcare model for monitoring and prediction of airpollution: machine learning approach

ABSTRACT The drastic increase in atmospheric pollutants has resulted in the prevalence of hazardous diseases like Asthma, Ischaemic heart disease, and Pulmonary disease around the world. IoT technology has the capability to acquire and monitor air quality parameters in the ambient environment of an individual. Inspired from these aspects, this paper proposes an IoT-based automated framework for monitoring and predicting air quality parameters like benzene using machine learning technology. Specifically, this study incorporates an Adaptive Neuro-Fuzzy Inference System (ANFIS) technique to predict the air quality in the form of Level of Pollutant (LoP) and modified Air Quality Index (m-AQI). Moreover, for the optimisation of ANFIS technique, Differential Evolution (DE)-inspired algorithm has been proposed for overall enhancement of system accuracy. In order to validate the proposed model, numerous experimental simulations were performed over four challenging datasets and results were compared with several state-of-the-art models. Comparative analysis shows improved statistical values for the presented model in terms of Accuracy (97.3%), Coefficient of Determination (94.01%), and Root Mean Square Error (2.4%). In addition to these, enhanced values of performance estimators like Reliability (92.36%) and Stability (76.00%) were estimated for depicting overall efficiency of the proposed system.

Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective.

IntroductionThe imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent.MethodsThis study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model.ResultsThe results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development.ConclusionThe proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.

Control on Temperature of Hybrid Nanofluid in Evacuated Tube Solar Collector Using Smart Curtain

This research was revealed control on temperature hybrid nanofluid in evacuated tube solar collector system, where the hybrid nanofluid utilized as working fluid in ETC for enhancement the thermal performance for heating device. The smart curtain was used for purpose shadow the evacuated tube solar collector for controlling on temperature of the hybrid nanofluid in heating or cooling conditions. In addition, Adaptive neuro fuzzy inference system (ANFIS) a way of artificial intelligence was applied to control on the shadow the evacuated solar tube collector. Where the main principle of the curtain is to control the polarization of the sun radiation, the curtain work depends on three parameters were applied in ANFIS technique, two parameters are input variable like as (temperature of hybrid nanofluids (inlet, outlet) and ambient temperature, and one output parameter represents by (distance parameter).

Development and use of a clinical decision support system for the diagnosis of social anxiety disorder

BackgroundMental disorders, according to the definition of World Health Organization, consist of a wide range of signs, which are generally specified by a combination of unusual thoughts, feelings, behavior, and relationships with others. Social anxiety disorder (SAD) is one of the most prevalent mental disorders, described as permanent and severe fear or feeling of embarrassment in social situations. Considering the imprecise nature of SAD symptoms, the main objective of this study was to generate an intelligent decision support system for SAD diagnosis, using Adaptive neuro-fuzzy inference system (ANFIS) technique and to conduct an evaluation method, using sensitivity, specificity and accuracy metrics. MethodIn this study, a real-world dataset with the sample size of 214 was selected and used to generate the model. The method comprised a multi-stage procedure named preprocessing, classification, and evaluation. The preprocessing stage, itself, consists of three steps called normalization, feature selection, and anomaly detection, using the Self-Organizing Map (SOM) clustering method. The ANFIS technique with 5-fold cross-validation was used for the classification of social anxiety disorder. Results and conclusionThe preprocessed dataset with seven input features were used to train the ANFIS model. The hybrid optimization learning algorithm and 41 epochs were used as optimal learning parameters. The accuracy, sensitivity, and specificity metrics were reported 98.67%, 97.14%, and 100%, respectively. The results revealed that the proposed model was quite appropriate for SAD diagnosis and in line with findings of other studies. Further research study addressing the design of a decision support system for diagnosing the severity of SAD is recommended.

ASSESS THE RISK LEVEL OF POWER TRANSFORMER DUE SHORT-CIRCUIT FAULTS BASED ON ANFIS

A power transformer is an electrical machine that converts electrical power at different voltage levels. Faults, occur in power transformers, inhibit electrical power distribution to the consumer. Protection, therefore, of the power transformers is essential in power systems reliability. The power system can be reliable if the protection devices work well when there is a fault. A hybrid intelligent technique, which is a combination of Artificial Neural Network (ANN) and Fuzzy known as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), was used in this research. The objective of this paper is the simulation of differential relays as a protection device on power transformers using Matlab/Simulink. Performance of differential relays for power transformers protection is carried out with internal and external fault scenarios. The input data were classified into three different input for ANFIS such as internal and external 1, internal and external 2, internal, external 1, and external 2, respectively. The error results of ANFIS training for the type of fault internal and external 1 is 9.46*10-7, and types of fault internal and external 2 is 1.09*10-6 internal, external 1 and external 2 are 8.59*10-7. The results obtained from the simulation were accurate and shows that the ANFIS technique is an efficient method that gives less error and a great value. Finally, the technique can minimize faults with power transformers. Finally, to prove this method can reduce faults in the power transformer, the assess of this model has been carried out through the RMSE that has been generated which is zero.

Application of Soft Computing Techniques for River Flow Prediction in the Downstream Catchment of Mahanadi River Basin Using Partial Duration Series, India

The delta region of the Mahanadi river basin is faced with high floods having arisen many questions like (1) due to the release of Hirakud dam, (2) due to the middle region catchment contribution (Tel tributary at Kantamal station and Ong tributary at Salebhata station) and (3) due to an increase in heavy rainfalls over the basin. To answer this, we choose the partial duration series (PDS) having an average number of flood peaks per year (λ = 3) at Naraj based on the capacity of the existing embankment which mitigates floods up to 28,400 m3/s and analyze the trends of peak discharge of PDS having λ = 3 at the Naraj, Hirakud dam and middle region to know the flow behavior which is responsible for flooding problem in the delta of Mahanadi basin. Subsequently, the trend of extreme rainfall over the entire basin and at the middle region of the basin is analyzed. Analysis of trends of extreme floods in relation to the trends of extreme rainfall to have a proper understanding of the causes of changing floods over the basin is important. Then, runoff at Naraj is estimated based on two techniques: artificial neural networks with different algorithms and an adaptive neuro-fuzzy inference system (ANFIS) using five different input combinations of peak runoff data and rainfall data of upstream locations and compare their performance based on different indices, namely correlation coefficient (R), index of agreement (d), root-mean-square error and Nash–Sutcliffe efficiency (E) to obtain the best input–output model. From the analysis, it is shown that ANFIS techniques as well as the neural network with Levenberg–Marquardt algorithm performs better for estimating runoff at Naraj and that gives the clear idea about the delta flooding. Also, the analysis revealed the answer arisen by many questions regarding the delta flood problem and which are due to the middle region catchment contribution and an increase in extreme rainfall.

Estimation of Atmospheric Water Vapor from ANFIS Technique and Its Validation with GPS Data

Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data

Experimental study and artificial intelligence-based modeling of discharge coefficient of converging ogee spillways

ABSTRACT Converging ogee spillway is employed as an emergency spillway constructed to control natural disasters such as instant floods. This paper presents an experimental study and artificial intelligence-based modeling of the discharge coefficient (Cd ) of the converging ogee spillway (with a curve axis). The ogee spillway structure was tested in both symmetrical and asymmetrical convergence of training walls ranging from 0° to 120°. Through the visual observation, it was found that the tailwater submergence, the depth of approach, the crest and spillway geometry, the head different from design head, and downstream floor position are the important factors that alter the Cd of the converging ogee spillway. Moreover, two different methods (e.g. gene expression programming (GEP) and adaptive neuro fuzzy inference systems (ANFIS)) were assessed for modeling the Cd using original experimental data. Based on the obtained results, both the GEP and ANFIS models have reliable performance in simulating the Cd . Moreover, the performance of GEP model was found to be slightly better than the ANFIS technique. According to the outcome of sensitivity analysis, the ratio of the total upstream head on the spillway to design head (H/Hd ) is the most important parameter for the optimum modeling of the Cd of the converging ogee spillway.

A Hybrid Algorithm Between the Artificial Immunological Network and the ANFIS Technique with Application

In this research, we have taken one of the artificial immune intelligence algorithms, namely the Artificial Immune Network, was developed using the Adaptive Neuro Fuzzy Inference System (ANFIS) by using the Immune Memory, The immunological network as an input to ANFIS technology to increase the chance of training a model based on the pattern of simulation of data patterns, as the immune memory resulting from the immunological network makes copies of data with characteristics similar to the original data. The proposed algorithm proved to have better and more efficient results rack on the patterns compared with the usual artificial immune system.

Prediction of specific grinding forces and surface roughness in machining of AL6061-T6 alloy using ANFIS technique

PurposeOptimisation of grinding processes involves enhancing the surface quality and reducing the cost of manufacturing through reduction of power consumptions. Recent research works have indicated the minimum quantity lubrication (MQL) system is used to achieve near dry machining of alloys and hard materials. This study aims to provide an experimental analysis of the grinding process during machining of aluminium alloy (Al6061-T6). MQL nanofluid was used as the lubricant for the grinding operations. The lubricant was formed by suspending silicon dioxide nanoparticles in canola vegetable oil. The effect of input parameters (i.e. nanoparticle concentration, depth of cut, air pressure and feed rate) on the grinding forces and surface quality was studied. Adaptive neuro-fuzzy inference system (ANFIS) prediction modelling was used to predict the specific normal force, specific tangential force and surface quality, the ANFIS models were found to have prediction accuracies of 97.4, 96.6 and 98.5 per cent, respectively. Further study shows that both the specific grinding forces and surface roughness are inversely proportional to the nanofluid concentration. Also, the depth of cut and table feed rate were found to have a directly proportional relationship with both the grinding forces and surface roughness. Moreover, higher MQL air pressure was found to offer better delivery of the atomised nanofluid into the grinding region.Design/methodology/approachGrinding experiments were performed using MQL nanofluid as the lubricant. The lubricant was formed by suspending silicon dioxide nanoparticles in canola vegetable oil. The effect of input parameters (i.e. nanoparticle concentration, depth of cut, air pressure and feed rate) on the grinding forces and surface quality has been studied.FindingsThe grinding process parameters were optimised using Taguchi S/N ratio analysis, whereas the prediction of the response parameters was done using ANFIS modelling technique. The developed ANFIS models for predicting the specific normal force, specific tangential force and surface quality were found to have prediction accuracies of 97.4, 96.6 and 98.5 per cent, respectively. Further findings show that both the specific grinding forces and surface roughness are inversely proportional to the percentage of nanoparticle concentration in the lubricant. Also, the depth of cut and table feed rate were found to exhibit a direct proportional relationship with both the grinding forces and surface roughness, while high MQL air pressure was observed to offer more efficient delivery of the atomised nanofluid into the grinding region.Practical implicationsThe work can applied into manufacturing industries to prevent unnecessary trials and material wastages.Originality/valueThe purpose of this study is to develop an artificial intelligent model for predicting the outcomes of MQL grinding of the aluminium alloy material using ANFIS modelling technique.

Decentralized neuro-fuzzy controllers of nonlinear quadruple tank system

This paper presented two decentralized neuro-fuzzy controllers to control the level in lower two tanks in nonlinear quadruple tank system (QTS). The controllers are designed based on adaptive neuro-fuzzy inference system technique. The relation between inputs/outputs was proved using relative gain array, and then, we can divide the quadruple tank system into two subsystems and control each of them separately. The first controller is a neuro-fuzzy inverse nonlinear (NFIN) model, which predicts the voltage required to control the level to track the referred one. So, the voltage was fed to neuro-fuzzy forward nonlinear model (NFFN) to obtain the desired level. The second is neuro-fuzzy nonlinear gain scheduling PI controller, which is designed to control the nonlinear QTS at any operating point. The results show that the NFIN controller has a more accurate tracking level and less computational time in both minimum and non-minimum phases.

Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach

The development of Maximum Power Point Tracking (MPPT) techniques is continuing in order to increase the generated energy from photovoltaic (PV) generators. A variety of MPPT techniques have been proposed and classified based on three main categories: offline, online and hybrid techniques. This paper presents a review of the most popular techniques for offline and online tracking of the Maximum Power Point (MPP), which are the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Hill Climbing (HC) techniques, respectively. This is in addition to a review for all hybrid techniques reported in the literature demonstrating their main merits and shortcomings. Moreover, the present paper combines the ANFIS and HC as a hybrid technique for the first time. The proposed technique involves the features of the ANFIS and HC techniques and mitigates their shortcomings in order to increase the generated PV electrical energy. The proposed technique is a combination of two stages to assess the duty ratio (control signal) being applied to a boost converter for MPP tracking. The first stage includes a set point calculation loop to estimate the duty ratio. The second stage involves a fine tuning loop to determine the exact duty ratio corresponding to the MPP. This achieves maximum power transfer to the load even under nonuniform climatic conditions using a relatively simple control system. The proposed technique has been simulated in MATLAB/SIMULINK environment and compared with some other MPPT techniques (the Constant Voltage (CV), ANFIS, HC, Incremental Conductance (IncCond) techniques) for steady state and rapidly changing climatic conditions (Ropp and sine radiation tests) as well as load variations. The results reveal that the proposed hybrid MPPT technique outperforms other MPPT techniques in term of performances indicators, which include the tracking speed, tracking accuracy and energy gain factor.

Application of Adaptive Neuro-Fuzzy Inference System Techniques to Predict Water Activity in Proton Exchange Membrane Fuel Cell

This work defines and implements a technique to predict water activity in proton exchange membrane fuel cell. This technique is based on the electrochemical impedance spectroscopy (EIS) as sensor and adaptive neuro-fuzzy inference system (ANFIS) as estimator. For this purpose, a proton exchange membrane fuel cell (PEMFC) model has been proposed to study the performances of the fuel cell for different operating conditions where the simulation model for water activity behavior is in the proposed structure. The technique based on ANFIS predicts the PEM fuel cell relative humidity (RH) from the EIS. For creation of ANFIS training and checking database, a new method based on factorial design of experimental is used. To check the proposed technique, the ANFIS estimator will be compared with the output humidity relative observation.

Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area

Soil temperature is a noteworthy factor, particularly in farming and land treatment of organic wastes, since development of organic system is firmly controlled by soil temperature. Furthermore, soil temperature has impacts on the physical, substance, and microbiological forms that occur in soil. In this study, for estimation of multi-depth soil temperature, the most reliable combination of input variables for model development was achieved using gamma test technique. The most suitable training and testing data length i.e. training data set 11,529 (01 January 1975 to 25 July 2006; 81%) and testing data set 2716 (26 July 2006 to 31 December 2013; 19%) of the most efficient input variables were determined by means of M-test technique. Multi-depth soil temperature model was achieved by means of multiple linear regression (MLR), multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) techniques. After comparing results of all models, it was found that the ANFIS technique with generalized bell membership function had the best performing results when compared to MLR and MLP for all cases i.e. 5 cm, 10 cm and 20 cm depths of soil. Potential of gamma test technique was evaluated by means of sensitivity analysis. The spatial variation of land surface temperature (LST) was assessed by remote sensing (RS) and GIS technique and also the qualitative and quantitative performances were evaluated for determining the reliable and accurate spatial variation of LST. In qualitative evaluation, it was observed that the split-window (SW) approach overestimated the LST with coefficient of determination 0.8654. According to the quantitative evaluation based on root mean square error, correlation coefficient and coefficient of efficiency values between observed and estimated LST, the spatial variation of LST was found to be accurate over the Udaipur City, Rajasthan.

Classifying the malware application in the Android-based smart phones using ensemble-ANFIS algorithm

Nowadays, the Android-based smartphones are fastest gaining in the market to date. Due to its open architecture and ease of application programming interfaces (APIs), it becomes fertile dregs for hackers to deploy malware application. This result in the burglary of personal information those are stored in smartphones, without the user knowledge unauthorised sends unintentional short message, and if the infected smart phones operate remotely it leads ways to some other malware attacks. However, many defence mechanisms were introduced against Android malware; it results in inaccuracy of classification. The contribution of this paper to detect and classify the malwares in the manifest file based on ensemble adaptive neuro-fuzzy inference system (ANFIS) technique. This proposed system is divided into three main steps to detect the malware applications they are: 1) features are extraction using the method called principal component analysis (PCA) method; 2) feature selection, using Pearson correlation coefficient (PCC) method; 3) malware applications are classified, using ensemble of ANFIS technique. The proposed system produced the best detecting malware applications classification and accuracy will be highly efficient than the other classification techniques.

ANFIS modeling of surface roughness in abrasive waterjet machining of carbon fiber reinforced plastics

This study discusses the development of an Adaptive neuro–fuzzy inference system (ANFIS) model for determining the surface roughness (Ra) during machining of multidirectional woven fabric Carbon fiber reinforced plastics (CFRP) using Abrasive waterjet machining (AWM). Three variable input parameters—Jet pressure (JP), Traverse speed (TS), and Standoff distance (SOD)—were selected to assess the roughness of the CFRP along the traverse direction of the cut surface. The experimental results show that a lower JP deteriorated the finish by creating surface rupture. On the other hand, a poor surface finish was observed in the case of machining at higher TS and SOD. Further, the developed ANFIS model was used to validate the results and it was found that the predicted values were in good agreement with a 95 % confidence level. It was also evident that the ANFIS technique is helpful for better prediction of the experimental data with minimum error. Finally, the cut surface morphology was analyzed using a 3D non-contact surface profilometer and the results are discussed.

Real-time prediction of water level change using adaptive neuro-fuzzy inference system

ABSTRACTAccurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the least-squares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average.

Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm

In the present work, autonomous mobile robot (AMR) system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neuro-fuzzy inference system (ANFIS) controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.

Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system

This study deals with predicting the mass flow rate of R-134a/LPG as refrigerant inside a straight and helical coiled adiabatic capillary tube of vapor compression refrigeration system by combining dimensionless analysis and Adaptive Neuro-Fuzzy Inference System techniques. For this purpose the experimental system was designed and tested under steady state conditions, by changing the length of the capillary tube, the inner diameter of the capillary tube, the coil diameter and the degree of subcooling of the refrigerant at the capillary tube inlet. Dimensional analysis was utilized to provide generalized dimensionless parameters and to reduce the number of input parameters, while Adaptive Neuro-Fuzzy Inference System was applied as a generalized approximator of the nonlinear multi-input and single-output function. The comparison of the absolute fraction of variance (R2) (0.998 and 0.961), the root mean square error (RMSE) (0.105 kg/h and 0.489 kg/h) and the mean absolute percentage error (MAPE) (0.954% and 4.75%) demonstrated the result for combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System and dimensionless correlation model predictions respectively. The results indicated that the combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System gave the best statistical prediction efficiency.