Adaptive Neuro-fuzzy Inference System Algorithm Research Articles

Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator

This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, reviewstudies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determinationcoefficients (R,R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Robust hybrid learning approach for adaptive neuro-fuzzy inference systems

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a regression model that uses fuzzy logic and neural networks, making it suitable for modeling the uncertainty of regression problems. However, the non-robust loss function in ANFIS's hybrid learning algorithm can make it susceptible to the direct effects of noise and outliers. This paper introduces a new procedure that uses robust loss functions to enhance the hybrid learning performance against noise and outliers. In addition, a new robust loss function is devised that can completely ignore outliers. Furthermore, a set of robust loss functions with mathematical relations are suggested. The proposed approach is evaluated on real-world problems, including weather forecasting and stock market prediction. Results suggested that the proposed model can reduce the Mean Square Error (MSE) in regression. Moreover, the new procedure enables utilizing different loss functions based on the application.

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid

AbstractThis article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.

Application Of Adaptive Neuro Fuzzy Inference System (ANFIS) In Predicting Thermal and Electrical Efficiency of a Photovoltaic Thermal (PvT) System

The use of photovoltaic (PV/T) system that converts solar radiation to electricity and provide thermal needs concurrently stands as one of the most effective means of utilizing renewable energy. In recent times, machine-learning techniques have been extensively used in solar system applications due to their high accuracy in predicting the performances without necessarily going through physical modelling. The main objective of this study is to implement an intelligent algorithm adaptive neuro-fuzzy inference system (ANFIS) model to simulate and predict the thermal and electrical efficiencies of a water-based photovoltaic-thermal (PV/T) system. Thorough experimentation for the fabricated set-ups (conventional PV and a water-based PV/T system) was carried out. The real experimental results was validated using ANFIS model. Base on the results obtained it was confirmed that there was an excellent agreement between the predicted model outputs and the actual experimental data. However, the ANFIS model gave the highest prediction accuracy with the lowest error margin of 0.00021, 0.0089 and 0.0459 for MSE, RMSE and MAE with strong correlation (R2) of 0.9998. Base on the results obtained it was concluded that this intelligent algorithm is a reliable tool in predicting the PV/T system performances.

Industrial Application of the ANFIS Algorithm—Customer Satisfaction Assessment in the Dairy Industry

As a part of the food industry, the dairy industry is one of the most important sectors of the process industry, keeping in mind the number of employees in that sector, the share in the total industrial production, and the overall value added. Many strategies have been developed over time to satisfy customer needs and assess customer satisfaction. This paper proposes an innovative model based on adaptive neuro-fuzzy inference system (ANFIS) and elements of the ACSI (American customer satisfaction index) for assessing and monitoring the level of customer satisfaction in a dairy manufacturing company where there are no large seasonal variations. In terms of an innovative approach, the base of fuzzy logic rules is determined by applying the fuzzy Delphi technique for the application of the ANFIS algorithm and assessment of customer satisfaction. The verification of the model is delivered by testing a real sample from a company of the dairy industry. As decisions on the strategic company level may be impacted by customer satisfaction, the company management should choose the most precise methodology for customer satisfaction assessment. The results are compared with other methods in terms of mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE). Results show that ANFIS outperformed other methods used for assessing the level of customer satisfaction, such as case-based reasoning and multiple linear regression.

A Machine Learning Based Reliability Analysis of Negative Bias Temperature Instability (NBTI) Compliant Design for Ultra Large Scale Digital Integrated Circuit

NBTI is a key reliability challenge in nanoscale digital design, and it is vital to address it throughout the exploration of design space at high levels of abstraction in order to improve reliability. A prediction model of aging that is adequate for these levels ought to be faster. In addition to this, the model should be able to forecast the recently discovered stochastic consequences of growing older. The purpose of this study is to offer a model that is based on machine learning (ML) and can predict aging effects. After obtaining a training set of sufficient size using Synopsis HSPICE (MOSFET Reliability, MOSRA) in the beginning, the machine-learning-based model is then trained and built in order to forecast the aging statistical features. Evaluation is done on a number of machine learning techniques, including Adaptive Neuro-Fuzzy Inference System (ANFIS), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). The findings indicate that ANFIS algorithms are very effective in the process of age prediction. The proposed technique shows that the aging prediction runtime is reduced by more than 99% when compared to the MOSRA-based approach, and accurate predictions of the statistical properties of aging are obtained with an accuracy of more than 99% on complementary metal oxide semiconductor (CMOS) and metal gate/high-K (MGK) circuits at the 22nm technology node.

ANFIS-based soft computing models for forecasting effective drought index over an arid region of India

Abstract Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSO-ANFIS show better performance results with R2 = 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R2 = 0.78. The results are presented suitably with the aid of scatter plots, Taylor's diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model.

Synthesis, Characterization, and Evaluation of Co-MOF Based ZIF-67 for CO2 Corrosion Inhibition of X65 Steel: Insights from Electrochemical Studies and a Machine Learning Algorithm

Co-MOF based metal organic framework was synthesized by reacting a metal ion (cobalt nitrate hexahydrate) with an organic ligand (2-methylimidazole) via a wet chemical method. The resulting material was characterized using detailed analytical methods and further was used as a self-assembly corrosion inhibitor in sweet corrosive environment. The empirical data set via electrochemical studies was modeled using adaptive neuro fuzzy inference system (ANFIS). The observed results showed that Co-MOF could significantly impede the corrosion rate of X65 steel and protect it from CO2 corrosion. Increasing the concentration of Co-MOF in the test solution increased the inhibition efficiency up to 97% at 0.1 wt % Co-MOF with a mixed-type inhibition mechanism. In addition, the DFT/MD-simulation approach evidenced the adsorption disposition of Co-MOF in aqueous and gas phase which complement with the empirical findings. Also, the prognostic capability of the proposed algorithm based on the statistical parameters such as root-mean-square error (RMSE), chi square (χ2), model predictive error (MPE) and coefficient of determination (R2) were appraised. From the viewpoint of statistics, the explanatory model aligned credibly with the ANFIS algorithm. The overall findings confirmed a dense hybrid coating of the synthesized Co-MOF on X65 steel as responsible for the inhibition of the sweet corrosion.

Modeling and control of a photovoltaic-wind hybrid microgrid system using GA-ANFIS

This paper aims to model a PV-Wind hybrid microgrid that incorporates a Battery Energy Storage System (BESS) and design a Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) controller to regulate its voltage amid power generation variations. Two microgrid models have been developed; a scalable Simulink Case Study Model from underlying mathematical equations and a nested voltage-current loop-based Transfer Function model. The proposed GA-ANFIS controller has been used as a Maximum Power Point Tracking (MPPT) algorithm to optimize the converter outputs and provide voltage regulation. The performance of the GA-ANFIS algorithm was compared with the Search Space Restricted-Perturb and Observe (SSR-P&O) and the Proportional-plus-Integral-plus-Derivative (PID) controllers using a simulation model built in MATLAB/SIMULINK. Results indicated that the GA-ANFIS controller is superior to the SSR-P&O and PID in terms of reduced rise time, settling time, overshoot, and the ability to handle non-linearities in the microgrid. In future work, the GA-ANFIS microgrid control system can be replaced with a three-term hybrid artificial intelligence algorithms controller.

Polyphenolic Extract Recovery from Azadirachta indica Leaves via Green Microwave-Assisted Extraction Technology: GA-ANFIS Prediction, Cuckoo Search Algorithm Optimization and ASPEN-Based Scale-Up Techno-economics

This study presented hybrid genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) modelling, cuckoo search algorithm optimization (CSAO), and scale-up techno-economics of Azadirachta indica leaves’ microwave-assisted extraction (MAE). Box-Behnken design (BBD) of Design-Expert software was used to design MAE experiment using microwave power (520–1040 watts), extraction time (2–10 min), and solid–liquid ratio (0.4–1 g/ml) with two dependent variables: total phenolic content (TPC) and total extractible yield (TEY). Predictive GA-ANFIS and CSAO models for the MAE were implemented in Matlab 2019 environment. ASPEN base-case simulation and economic analysis for the phenolic extract recovery were achieved using CSAO optimal conditions. Process profitability uncertainty analyses were conducted with Monte Carlo simulation (MCS). BBD model results gave coefficient of determination (R2) 0.949 and 0.952 for TEY and TPC, respectively. GA-ANFIS results gave training R2 0.989, testing R2 0.999 and training R2 0.995, testing R2 0.992 for the TEY and TPC prediction, respectively. CSA optimal region for the extract recovery gave microwave power 1040 W, extraction time 4 min, S/L ratio 0.4, TEY 33.3%, and TPC 10 mg GAE/g dw. ASPEN base-case techno-economic results gave batch size (1.49 kg), batch time (135 min), total capital investment ($211,172), annual production cost ($42,386), net present value ($150,298), payback time (5.84 years), and rate of return (17%). The MCS profitability risk analysis revealed that the associated uncertainty for the process is less than 1%. Therefore, this work shows that Azadirachta indica leaves extract production using green technology is feasible.

Improved ANFIS combined with PID for extractive distillation process control of benzene–isopropanol–water mixtures

Real-time measurement and control of product composition in extractive distillation continues to remain a challenge; however, composition control has demonstrated the ability to stabilise control systems. In this study, a dynamic control scheme is proposed that uses an improved PID-fused adaptive neuro-fuzzy inference system (ANFIS); herein, ANFIS replaces composition controllers, improving the real-time performance of the control system. The ANFIS algorithm, however, suffers from problems such as easily falling into a local optimum solution. Therefore, the genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are separately used to optimise the network parameters of ANFIS, and the impact of different number of clusters (NC) on the performance of the control scheme is evaluated. The control scheme of PSO-optimised PID-ANFIS exhibits superior dynamic performance compared to other control schemes; moreover, it does not require the use of component controllers and has a faster response time and smaller transient values.

Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool

Carbon-reinforced nylon composite material (PA12CF20), i.e., polyamide, has been widely used in the automotive, medical, and electronics industries for fabricating functional parts, especially in gear manufacturing, due to its excellent wear property. The present work investigates the optimal parameters of the fused filament fabrication (FFF) 3D printing technique using PA12CF20 as feedstock to fabricate parts for enhancing the sliding wear performance. The test specimens are fabricated as per the American society for testing and materials (ASTM) G99 standard. The hybrid heuristic tool, i.e., genetic algorithm-adaptive neuro fuzzy inference system (GA-ANFIS), has been used to train, predict, and optimize the sliding wear performance of the fabricated parts using carbon reinforced polyamide composite material. The experimental runs have been performed on 30 combinations of input factors based on face centered central composite design (FCCCD) and further used for training and optimization for the hybrid GA-ANFIS approach. Scanning electron microscopy has been used to characterize the wear crack of the PA12CF20. It is observed that the optimal parameters retrieved using GA-ANFIS have increased the wear performance of PA12CF20 fabricated specimens, and the same has been validated experimentally. Further, a lathe machine spur gear was fabricated using optimal parameters of the FFF technique and checked the suitability for end-use applications.

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.

Estimating flashpoints of fuels and chemical compounds using hybrid machine-learning techniques

Flashpoint of organic materials is a crucial physical property in industrial applications and laboratory experiments, which provides information on safety standards and needed precautions in handling various organic materials. Proposed methods to determine the flashpoint of an organic material suffer from dependency on other physical properties of the chemical or demand complicated calculations which are time-consuming. In this work, a direct system model is proposed to anticipate the flashpoints of organic materials for a wide range of chemical compounds. The following models of genetic algorithm-adaptive neuro-fuzzy inference system, the least-squares version of the support vector machine, particle swarm optimisation-adaptive neuro-fuzzy inference system, and artificial neural network were applied to develop the model. This system model can speed up the flashpoint determination process and its accuracy. It can also anticipate the flashpoints of new organic materials. 79 functional groups were gathered to form a group contribution method to estimate the flashpoints of various chemical compounds. The functional groups were correlated with the model, known as the committee machine intelligent system, which performs accurately to prognosticate the flashpoint for every compound. 1,378 chemical compounds of different chemical categories were used to develop the model, making it suitable to estimate the flashpoints. This system model can determine or anticipate the flashpoints of organic materials with high accuracy and provide useful information for safety considerations in laboratory and industrial applications.

Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians.

Age-related macular degeneration (AMD) is one of the most common reasons for blindness in the world today. The most common treatment for wet AMD is the intravitreal injections for inhibiting vascular-endothelial-derived growth factor (VEGF). This treatment usually involves multiple injections and thus multiple clinic visits, which not only causes increased cost on national health services but also causes exposure to the hospital environment, which is sometimes high risk considering current COVID crisis. The treatment, in spite of the above concerns, is usually effective. However, in some cases, either the medicine fails to produce the anticipated favourable outcome, resulting in waste of time, medication, efforts, and above all, psychological distress to the patients. Hence, early predictability of anatomical as well as functional effectiveness of the treatment appears to be a very desirable capability to have. A machine learning approach using adaptive neuro-fuzzy inference system (ANFIS) of two-sample prediction model has been presented that requires only the baseline measurements and changes in visual acuity (VA) as well as macular thickness (MAC) after four months of treatment to estimate the values of VA and MAC at 8 and 12months. In contrast to most of the AI techniques, ANFIS approach has shown the capability of the algorithm to work with very small dataset as well, which makes it a perfect candidate for the presented solution. The presented model has shown to have a very high accuracy (> 92%) and works in near-real-time scenarios. It has been converted into a smart phone App, OphnosisAMD, for convenient usage. With this App, the clinician can visualize the progression of the patient for a specific treatment and can decide on continuing or changing the treatment accordingly. The complete AI engine developed with the ANFIS algorithm is localized to the phone through the App, implying that there is no need for internet or cloud connectivity for this App to function. This makes it ideal for remote usage, especially under the current COVID scenarios. With a smart AI-based App on their fingertips, the presented system provides ample opportunity to the doctors to make a better decision based on the estimated progression, if the same drug is continued with (good/fair prognosis) or alternate treatment should be sought (bad prognosis). From a functional point of view, a prediction algorithm is triggered through simple entry of the relevant parameters (baseline and 4months only). No internet/cloud connectivity is needed since the algorithm and the trained network are fully embedded in the App locally. Hence, using the App in remote and/or non-connected isolated areas is possible, especially in the secluded patients during the COVID scenarios.

Analyse the surface quality in the milling process by soft computing approaches

In recent years manufacturing industries move towards the automatic inspection process for achieving higher efficiency and maximum profit. These papers proposed the methodology of analysing the surface roughness in the milling process through soft computing approaches. The Adaptive Neuro-Fuzzy inference system (ANFIS) model was developed with the input parameters (Spindle Speed, Depth of Cut, Feed Rate) and output parameters (Surface Roughness). The surface roughness value is measured by the traditional stylus probe method,which values consider a reference value for the training process. Finally, the surface roughness output which is produced by the ANFIS algorithm was compared with the reference value for calculating the accuracy range. The outcome of the accuracy level is above 97% by using ANFIS model.

Implementation of Genetic Algorithm and Adaptive Neuro Fuzzy Inference System in Predicting Survival of Patients with Heart Failure

Purpose: Heart failure is a disease that is still a global threat and plays a major role as the number one cause of death worldwide. Therefore, accurate predictions are needed to determine the survival of heart failure patients. One technique that can be used to predict a decision is classification. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an algorithm that can be used in the classification process in making predictions. Genetic Algorithms can help improve the performance of classification algorithms through the feature selection process. Methods/Study design/approach: In this study, predictions or diagnoses were made on the survival of heart failure patients based on the heart failure clinical record dataset obtained from the UCI Machine Learning Repository. The data used is 299 data with 12 attributes and 1 class. The result of this research is the comparison of the accuracy of the ANFIS algorithm before and after using the Genetic Algorithm. Result/Findings: The ANFIS algorithm produces the highest accuracy of 94.444%. While the ANFIS algorithm after attribute selection using the Genetic Algorithm produces the highest accuracy of 96.667%. This shows that the Genetic Algorithm is able to improve the performance of the ANFIS classification algorithm through the attribute selection process.