Adaptive Neuro-fuzzy Inference System Method Research Articles

Optimizing the operation of series and parallel reservoirs using the neutrosophic environment: A case study of the Karun watershed

ABSTRACT In this article, neutrosophic inference is used for the first time as a new fuzzy set theory generalization to improve the operation of series and parallel dam reservoirs. A case study was conducted on the problem of parallel and series use of multiple and multipurpose reservoirs in the dams of the Karun watershed (Iran). The problem of optimizing the operations of dam reservoirs with the objective function of minimizing the shortages and maximizing hydropower production is modeled and solved using training data. The optimization results are applied to model the problem in the adaptive neuro-fuzzy inference system (ANFIS). The behavior of the system in the neutrosophic environment is inferred by ANFIS. Finally, the conventional operation method and the ANFIS method were compared with neutrosophic inference by fuzzy performance criteria. The results indicate that the proposed neutrosophic inference model has a high capacity to infer the behavior of the dam reservoir system. With a higher sustainability index due to increased fuzzy reliability, fuzzy resilience indices, and reduced fuzzy vulnerability index, the proposed model has improved the objective function by 80 and 40% in the training data and 67 and 36% in the test data in comparison to the operation rule curve and ANFIS.

Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.

Speed controller design for turboshaft engine using a high-fidelity AeroThermal model

Abstract One of the main objectives of this research is to construct a high-fidelity aerothermal model for controlling the turboshaft engine power turbine rotational speed. Firstly, a high-fidelity aerothermal model of General Electric T700 is formed. The turboshaft engine aerothermal model is simulated and compared to engine design point data on MATLAB/Simulink environment. Thermodynamic equations and some algebraic equations are used. In predicting compressor mass flow rate, the adaptive neuro-fuzzy inference system method is preferred. A propeller model is also added to the turbo shaft engine aerothermal model and to keep the power turbine rotational speed at a constant value, a Proportional Integral Derivative controller is designed and applied. Open-loop and closed-loop simulations are conducted and successful outcomes are achieved. Results indicate that the differences between simulation and actual engine data are within acceptable limits. The suggested model can be readily updated and expanded to control additional parameters of the engine. PACS 2010 Classification: Control theory in mathematical physics, 02.30. Yy, Computer modeling and simulation, 07.05.Tp, 05.70.Ce Thermodynamic functions and equations of state.

Optimal power generation of proton exchange membrane fuel cell using ANFIS based MPPT algorithm

Fuel cells are the most promising energy source for the future energy demand. The automobile industry is looking at the integration of fuel cells with electric vehicles (EV). This integration comes with many challenges like dynamic operational behaviors. For operating the fuel cell with maximum efficiency, this work proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method. The hydrogen flow rate, pressure and stack temperature are the parameters considered to track the maximum power point of the fuel cell. The ANFIS-MPPT algorithm has been integrated with the 1.26 kW fuel cell in MATLAB/Simulink® and validated in different scenarios like dynamic variation in hydrogen pressure, stack temperature, load variation. The performance has been observed and compared with the conventional MPPT algorithms of Perturb and Observe (P&O) algorithm and Incremental Conductance (InC) algorithm. The proposed ANFIS-MPPT algorithm improves the power stability by 10–15% than the P&O and InC methods. Also, the proposed ANFIS-MPPT has 30% faster response as compared to the P&O algorithm, and 23% than the InC algorithm. From the analysis, it is observed that the ANFIS, P&O and InC methods are having the response time of 2.5 s, 3.6 s and 4.5 s respectively. Also the ANFIS method delivers the maximum power output of 1.26 kW, whereas the P&O and InC deliver 1.13 kW, 1.19 kW respectively. The detailed simulation analysis and results are presented in this paper.

An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique

AbstractSand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.

New and Highly Accurate Static Young's Modulus Model Using Machine Learning Techniques.

Static Young's modulus (E s) is a critical property required in numerous petroleum calculations. Various models to forecast E s have been proposed in the literature. However, existing models, by and large, lack precision and are confined to specific data set ranges. This study proposes an alternative approach for E s determination, utilizing different machine learning methods, such as an adaptive neuro-fuzzy inference system (ANFIS). In these proposed methods, the predictor variables include bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc). The models were trained on a data set comprising 1853 hydrocarbon reservoir rock samples from globally diverse locations. They were evaluated using trend, group error, and statistical error analyses. To test the efficacy of the proposed models, the optimally performing model was identified and used to detect the rock types along with the previously published models. Results indicated that ANFIS is the optimum model and can predict E s with an average absolute percentage relative error (AAPRE) of 5.1% and a correlation coefficient (R) of 0.9602. The ANFIS method has some benefits over other machine learning approaches insofar as its superiority in reaching a quicker decision about the mapped relationship between the inputs and outputs because it combines artificial neural networks and fuzzy logic in one tool. The ANFIS can perform a highly nonlinear mapping and displays a better learning ability. The proposed ANFIS model demonstrates its ability to capture accurate physical relationships between input rock properties and E s through trend analysis, which shows that increasing the RHOB increases the E s. Contrarily, increasing the DTc and DTs reduces the E s. Furthermore, the ANFIS model can accurately detect the rock types based on its E s determinations. This research demonstrates the importance of accurately predicting E s for the proper identification of rock types. Thus, this study offers potential advancements in geological assessments of hydrocarbon reservoirs and improvements in many petroleum engineering applications.

Application of ANFIS in Decision-Making on the Smart Control Early Warning System for Tornadoes

A tornado is one weather process that arises due to atmospheric instability. A tornado is a strong wind, but not all strong winds are tornadoes. Tornadoes have a short time frequency but can result in no minor disaster because they can blow objects away and uproot trees. Due to the consequences, an early warning system is needed as an anticipation for the community in the affected areas so that it can help the community by warning early on of the occurrence of a tornado. The ANFIS (Adaptive Neuro Fuzzy Inference System) method is used to forecast the event of a tornado, and the parameters used are wind speed, ambient temperature, and ambient humidity. This study will compare the ANFIS method using hybrid and backpropagation algorithms. Using the backpropagation algorithm, an error of 0.42385 was produced during training and testing, and an average error of 136.54 was obtained. When using the hybrid algorithm, the error during training is 2.0781 x 10-5, and the average error during testing is 0.015%.Keywords — ANFIS ; Anemometer; DHT22; Early Warning System; Tornado.

Klasifikasi Kebutuhan Dokter untuk Kesejahteraan Masyarakat Menggunakan ANFIS

In social life, the need for medical workers is different in each region, because the population varies. If doctors cannot treat a large enough number of patients in an area, it can have various impacts on society, such as contracting dangerous diseases if not treated as soon as possible. The effect will be a decline in people's standard of living. By classifying the need for the number of health workers (doctors) relative to the population, the level of welfare in an area can be obtained. To assist in optimizing health workers (doctors) they can use fuzzy logic and ANFIS (Adaptive Neuro Fuzzy Inference System). By using ANFIS, it is hoped that we can find the optimal value for the classification of health workers that will be needed in each region. In the ANFIS test, the RMSE error value was 0.2698 and the first accuracy value was 73%. Then by adding a membership function, an RMSE error value of 0.17698 was obtained, this second accuracy value increased by 10% to 83%. By using the ANFIS method to classify health workers according to different population sizes in each region, you can measure the level of community welfare well.

Computer-Aided Neuro-fuzzy Algorithm for Prediction of Ginger(Zingiber officinale) Rhizome Drying

Neuro-fuzzy modeling technique has been used for the development of computer-based adaptive process control systems for most postharvest operations instead of convectional mechanistic modeling technique owing to its good prediction without prior knowledge of process operation. Therefore, adaptive neuro-fuzzy inference system (ANFIS) modeling and prediction of ginger (Zingiber officinale) rhizome moisture content in a forced convective tray dryer was investigated in this study. Computer codes for an exhaustive search and grid partitioning were written in the Matlab software environment and used for the analysis of experimental data obtained from the ginger rhizome drying experiment. The data were trained and checked using the grid-partitioning ANFIS method for the development of neuro-fuzzy network for ginger drying prediction. The prediction accuracy of the network was evaluated by varying input and output Membership Function (MF) types. The exhaustive search analysis shows that drying time (DT) has the highest effect on the moisture content, followed by air drying temperature (ADT), air relative humidity (ARH), and air flow velocity (AFV). Moreover, combination of DT and ADT as two interactive variables; and combination DT, ADT and ARH as three interactive variables show highest effect on ginger moisture content removal during the process. Generalized bell (gbell) MF with two MF for each input variable and constant output MF 2 type were obtained as the best ginger drying ANFIS network with corresponding R and MSE of 0.9667 and 0.0971, respectively. Therefore, this study shows the reliability of the ANFIS model for the prediction of ginger rhizome drying in a tray dryer.

Computer-Aided Neuro-fuzzy Algorithm for Prediction of Ginger(Zingiber officinale) Rhizome Drying

Neuro-fuzzy modeling technique has been used for the development of computer-based adaptive process control systems for most postharvest operations instead of convectional mechanistic modeling technique owing to its good prediction without prior knowledge of process operation. Therefore, adaptive neuro-fuzzy inference system (ANFIS) modeling and prediction of ginger (Zingiber officinale) rhizome moisture content in a forced convective tray dryer was investigated in this study. Computer codes for an exhaustive search and grid partitioning were written in the Matlab software environment and used for the analysis of experimental data obtained from the ginger rhizome drying experiment. The data were trained and checked using the grid-partitioning ANFIS method for the development of neuro-fuzzy network for ginger drying prediction. The prediction accuracy of the network was evaluated by varying input and output Membership Function (MF) types. The exhaustive search analysis shows that drying time (DT) has the highest effect on the moisture content, followed by air drying temperature (ADT), air relative humidity (ARH), and air flow velocity (AFV). Moreover, combination of DT and ADT as two interactive variables; and combination DT, ADT and ARH as three interactive variables show highest effect on ginger moisture content removal during the process. Generalized bell (gbell) MF with two MF for each input variable and constant output MF 2 type were obtained as the best ginger drying ANFIS network with corresponding R and MSE of 0.9667 and 0.0971, respectively. Therefore, this study shows the reliability of the ANFIS model for the prediction of ginger rhizome drying in a tray dryer.

A novel method for accurate pressure drop prediction in horizontal and near horizontal pipes using adaptive neuro fuzzy inference system based model

Effective flow line and piping network design depends on the accurate prediction of pressure drop in multiphase flow for horizontal and near horizontal pipes. Since early 1950, several empirical correlations and mechanistic models have been developed to predict pressure drop. All correlations used by the industry, in addition to their applicability limitations, fall short of providing the necessary precision of pressure drop predictions. However, compared to empirical correlations, the recently developed mechanistic models improved pressure drop prediction. To design and construct more dependable and economical surface piping networks and wells, it is still necessary to improve prediction accuracy. This study uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a model that predicts pressure drop in horizontal and near-horizontal pipelines with greater accuracy and simplicity. Using the ANFIS method, the fuzzy modelling procedure can gather knowledge about a set of data to determine the membership function parameters that will enable the associated fuzzy inference system to track input/output data most effectively. The model was created and tested using field data encompassing various variables. The model was developed using 450 different data sets that were collected from the Asian continent. 113 data sets were used for testing, and a total of 337 data sets were used for training. Trend analysis was carried out during the model development phase prior to the model’s completion. This is performed to make sure the model is stable and to make sure the created model is physically sound and accurately simulates the real physical process. To determine the percentage of error between the predicted value and the actual measured data, statistical analysis was carried out. To compare the performance of the new ANFIS model to earlier empirical correlations and mechanistic models, graphical and statistical techniques were also used. The new model outperformed known correlations and the most recent mechanistic models by a significant margin in producing incredibly accurate pressure drop predictions. The Dukler et al. empirical correlation, Beggs and Brill empirical correlation, Xiao mechanistic model, and Gomez mechanistic model had values of 25.284, 20.940, 30.122, and 20.817, respectively, while the ANFIS model had a value of 13.256 for the lowest average absolute percentage error. Additionally, the Duckler and Beggs & Brill models came in second and third, with values of 0.908 and 0.906, respectively, and the ANFIS model had the highest coefficient of determination at 0.955.

Prediction of Augmented Efficiency between Electric Pulses and Bleomycin on K562 and MIA-PACA2 Cancer Cells Using Adaptive Neuro-Fuzzy Inference System Method

Prediction of Augmented Efficiency between Electric Pulses and Bleomycin on K562 and MIA-PACA2 Cancer Cells Using Adaptive Neuro-Fuzzy Inference System Method

Prediction of Downpull Force Coefficient on the Underflow-Type Floating Gate

The experiment was conducted to compare the hydraulic openness by the buoyancy theory of the hydrostatic pressure and the hydraulic openness by the measurement, and the difference between the theory and the measured hydraulic openness was confirmed to be the effect by the downpull force. As a result of using the existing theoretical formulae to examine the downpull force of the buoyancy-type floating gate, it was found that the existing formulae were difficult to apply to the buoyancy-type floating gate. Therefore, this paper proposed the use of the multiple regression analysis and adaptive neuro-fuzzy inference system method for predicting downpull force coefficient of the buoyancy-type floating gate. The experimental results indicate that the downpull force coefficient decreased as the opening ratio increased. In addition, the downpull force increased as the opening ratio increased below the opening ratio of 0.300, however the downpull force decreased as the opening ratio increased from the opening ratio exceeding 0.300. Finally, prediction results by using the proposed relationship indicated that the errors are much smaller compared to the results of the existing theoretical formulae. In addition, the application of the adaptive neuro-fuzzy inference system method improved the prediction significantly, reducing the errors to the half of that by the existing theoretical formulae.

KONTROL FREKUENSI BEBAN MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) PADA SISTEM HYBRID WIND MIKROHIDRO DAN DIESEL

This study aims to analyze and compare the performance of three frequency load controller methods in a hybrid generator consisting of three types of energy generation, namely microhydro, diesel and wind. The controller methods being compared are the PID (Proportional Integral Derivative) method, ANFIS (Adaptive Neuro-Fuzzy Inference System), and Fuzzy. This analysis was carried out to improve the efficiency and stability of the hybrid generating system by optimizing the frequency load controller. Frequency instability can occur due to load fluctuations, changes in environmental conditions, or system disturbances. Therefore, the use of an effective and adaptive controller method is very important in maintaining the stability and reliability of the hybrid generator system. The analytical method used in this study involved data collection and computer modeling. The required data includes information on load variations, energy production, and environmental conditions. Furthermore, the PID, ANFIS, and Fuzzy methods are applied to the data to build a frequency load controller model. The results of the analysis show that the three controller methods have different performance in maintaining frequency stability in the hybrid generator system. The PID method provides a fast and accurate response to load fluctuations, but is less adaptive to environmental changes. While the ANFIS method is able to adapt well to changes in system conditions, it requires time for model training. Fuzzy methods can provide control that is more adaptive and tolerant to disturbances, but may require more complex tuning. However, in general, the use of intelligent controller methods such as ANFIS and Fuzzy can improve the performance of hybrid generators by producing more adaptive and stable controls. This research has important implications for the development of more efficient and reliable hybrid power systems. By choosing the right controller method, hybrid power plants can provide energy in a more stable and environmentally friendly manner, and reduce dependence on limited fossil resources. Therefore, this research can make a significant contribution to the development of renewable energy and sustainable development in the future.

Academic Performance Assessment Prediction Model Using the Adaptive Neuro-Fuzzy Inference System Method

Performance evaluation of Human Resources is an important part of an organization or company. One of the HR in tertiary institutions that must be evaluated is academic performance. The author conducted research on the factors of academic performance assessment. Therefore, we need a system that can classify academic performance optimally in order to improve the quality of academic performance. In this research, we construct a predictive model for academic ability evaluation using Adaptive Neuro-Fuzzy Inference System method. The ANFIS method shows a very good data accuracy of 92.20%. From each variable motivation to work appraisal show is a Good, competence to work appraisal = Good, compensation to work appraisal show is a Good, responsibility to work appraisal show is a Good, and job satisfaction to work assessment show is Good.

Leaf area estimation in lettuce: Comparison of artificial intelligence-based methods with image analysis technique

Artificial intelligence (AI) as a simulation approach is increasingly being used in precision agriculture. The current study evaluated the performance of five methods for estimating lettuce leaf area (LA), including simple linear regression (SLR), artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), support vector regression (SVR), and image analysis technique. The standardized Ranking Performance Index (sRPI) was used to determine the overall ranking of the simulation approaches. Image analysis and ANFIS methods were found to have superior performance to other methods evaluated, according to the evaluation criteria of R2 values of 0.98 and 0.92, and RRMSE values of 4.3 and 2.32, respectively. The SLR, on the other hand, was found to be the least ranked method. Overall, the results indicated that the image analysis method could be a cost-effective option, and the ANFIS method could serve as a non-destructive alternative to conventional LA determination methods.

Optimization of ultrasonic-excited double-pipe heat exchanger with machine learning and PSO

Two intelligent optimization methods, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), with particle swarm optimization (PSO), were employed in this study to forecast the heat transfer rate, Nusselt number, number of transfer units (NTU), and effectiveness of a double-pipe counter-flow heat exchanger. The experiments are related to hot flow rate (113–257 l/h), hot fluid inlet temperature (40–60 °C), ultrasonic excitation power level (0 or 60 watts), and volume fraction of nanoparticle (0–0.8%). The findings of the ANN, ANFIS, ANN-PSO, and ANFIS-PSO methods were verified through experimental studies. The ANN model, the multilayer perceptron/backpropagation with three different learning algorithms, the Levenberg Marquardt (LM), the scaled conjugate gradient (SCG), the Bayesian regularization algorithm (BR), and the ANFIS model were developed with backpropagation and the optimal hybrid method. Finally, these two models were optimized using the PSO algorithm. The experimental and predicted data were compared, and both models were found to be accurate. The correlation coefficients of both models were greater than 94.84%, with the ANN-PSO model slightly outperforming the ANFIS-PSO model. Also, the maximum amount of MSE is equal to 2.592, which shows the model has good performance in predicting results.

Multi-Extremum Adaptive Fuzzy Network Method for Dynamic Reliability Estimation Method of Vectoring Exhaust Nozzle

To enhance the accuracy and efficiency of reliability analysis for an aero-engine vectoring exhaust nozzle (VEN), a multi-extremum adaptive fuzzy network (MEAFN) method is developed by absorbing an adaptive neuro-fuzzy inference system (ANFIS) into the multi-extremum surrogate model (MESM) method. In the proposed method, the MERSM is used to establish the surrogate models of many output responses for the multi-objective integrated reliability analysis of the VEN. The ANFIS method is regarded as the basis function of the MESM method and adopted to improve the modeling precision of the MESM by introducing the membership degree into the input parameters and weights to improve the approximation capability of the neural network model to the high nonlinear reliability analysis of the VEN. The mathematical model of the MEAFN method and reliability analysis thoughts of the VEN is provided in this study. Then, the proposed MEAFN method is applied to conduct the dynamic reliability analysis of the expansion sheet and the triangular connecting rod in the VEN by considering the aerodynamic loads, operation temperature, and material parameters as the random input variables and the stresses and deformations as the output responses, compared with the Monte Carlo method and the extremum response surface method. From the comparison of the methods, it is indicated that the MEAFN method is promising to improve computational efficiency while maintaining accuracy. The efforts of this study provide guidance for the optimization design of the VEN and enrich the reliability theory of the flexible mechanism.

Comparison of performances of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) for estimating employee labor loss

As a result of artificial intelligence research that started in the 1950s, the need for human beings in all sectors and labor markets constantly decreases. The increase in the total cost of the labor force increases the productivity pressure on the labor. For this reason, the workforce participating in production is expected to be more efficient and productive. For this reason, the loss of labor is carefully monitored and tried to be reduced as much as possible. However, with each passing day, labor losses are inevitable due to personnel turnover, work accidents, dismissals, and absenteeism. Humanity is still struggling, mainly due to the contagious covid-19 virus, which has recently affected the world. Since it is a condition that affects human health, its adverse effects have been observed in many areas where people are present. Especially in this period, unpredictable workforce losses have occurred in the production and service sectors since people are mostly the primary workforce. Since there is no plan and measure for such a situation in most risk planning, it also brings labor losses and costs. In this study, In order to examine the relationship between health problems and loss of labor, the amount of lost labor due to employees who could not come to work due to health-related reasons was tried to be estimated by Fuzzy Logic and ANFIS methods. This study examined three-year absenteeism data of employees in a courier company, and twenty-eight reasons for absenteeism were determined. The amount of labor loss was estimated using Fuzzy Logic and ANFIS methods, using five factors that cause absenteeism. Estimated and actual values were statistically compared with MAD MAPE, MSE, and RMSE performance measurement values. With fuzzy logic, the MAD value is 4.76; the MAPE value is 155.7; The MSE value was calculated as 52.7, and the RMSE value as 7.26. In ANFIS, the MAD value is 3.2, the MAPE value of 86.24, MSE of 27.5; The RMSE value was calculated as 5.25. When the results are compared, it has been seen that the ANFIS method obtains closer estimations than the fuzzy logic method.

A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.

This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data.