An Enhanced Adaptive Medical Diagnostic Model Driven By Genetic–Neuro-Fuzzy Algorithms

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

The present research work explores the implementation of three smart hybrid predictive models based on the adaptive neuro-fuzzy inference system (ANFIS), ANFIS and genetic algorithm (GA), and ANFIS and particle swarm optimization (PSO). All such strategies have been used to determine and compare machining key elements including material removal rate (MRR) and surface roughness (SR) during the gas-assisted electrical discharge (GAEDM) process. In this study, inert gas-based EDM with a multi-hole rotating tool has been carried out. In this experimentation, pulse-on time, peak current, duty cycle, electrode rotation, and gas discharge pressure were selected as input factors. The proposed method is to upgrade ANFIS with GA and PSO techniques. The GA and PSO algorithms are used to enhance the accuracy of the ANFIS model. The models have been trained, tested, and validated with observational results. Statistical techniques were applied to assess the effectiveness of the predictive capability models established through the ANFIS, ANFIS-GA, and ANFIS-PSO techniques. The actual and predicated estimates of MRR and SR of the GAEDM, obtained by ANFIS, ANFIS-GA, and ANFIS-PSO, were observed to be as per one another. In addition, the ANFIS-PSO framework proved to be even more responsive when compared with the ANFIS and the ANFIS-GA system. In particular, the assertion of this work is that modified algorithms such as ANFIS-GA and ANFIS-PSO are an efficient and productive approach to accurate EDM response estimation.

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (Id), embedment ratio (L/D), both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts.

Oil production estimation plays a critical role in economic plans for local governments and organizations. Therefore, many studies applied different Artificial Intelligence (AI) based methods to estimate oil production in different countries. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a well-known model that has been successfully employed in various applications, including time-series forecasting. However, the ANFIS model faces critical shortcomings in its parameters during the configuration process. From this point, this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer (AO) with the Opposition-Based Learning (OBL) technique. The main idea of the developed model, AOOBL-ANFIS, is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS. The proposed model is evaluated using real-world oil production datasets collected from different oilfields using several performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), Standard Deviation (Std), and computational time. Moreover, the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization (PSO)-ANFIS, Grey Wolf Optimizer (GWO)-ANFIS, Sine Cosine Algorithm (SCA)-ANFIS, Slime Mold Algorithm (SMA)-ANFIS, and Genetic Algorithm (GA)-ANFIS, respectively. Additionally, it is compared to well-known time series forecasting methods, namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Neural Network (NN). The outcomes verified the high performance of the AOOBL-ANFIS, which outperformed the classic ANFIS model and the compared models.

A proper and reliable estimation of bearing capacity of thin-walled foundations is of importance and necessary for accurate design of these structures. This study proposes a new hybrid intelligent technique, i.e., adaptive neuro-fuzzy inference system (ANFIS)–polynomial neural network (PNN) optimized by the genetic algorithm (GA), called ANFIS–PNN–GA, for prediction of bearing capacity of the thin-walled foundations. In fact, in ANFIS–PNN–GA system, GA was used to optimize the ANFIS–PNN structure. To achieve the aim of this study, a series of data samples were collected from literature. After establishing the database, many ANFIS–PNN–GA models were constructed and proposed to estimate the bearing capacity of the aforementioned foundations. To show capability of this advance hybrid model, two pre-developed models i.e., ANFIS and PNN were also built to predict bearing capacity. The performance prediction of the proposed models were evaluated through the use of several performance indices, e.g., correlation coefficient (R) and mean square error (MSE). The R values of (0.9825, 0.9071, and 0.9928) and (0.8630, 0.7595 and 0.9241) were obtained for training and testing data of the ANFIS, PNN and ANFIS–PNN–GA, models, respectively. Accordingly, because of the role of GA as a practical optimization algorithm in improving the efficiency of both PNN and ANFIS models, results obtained by the ANFIS–PNN–GA model are more accurate compared to other implemented methods. The proposed advance hybrid model can be introduced as a new and applicable technique for solving problems in field of geotechnics and civil engineering.

Predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds using bee algorithm and adaptive neuro-fuzzy inference system

In this paper, pioneering Adaptive Neuro Fuzzy Inference System (ANFIS) which is trained with well known traditional Proportional, Integral and Derivative (PID) controller data for Half Car (HC) model is proposed to improve the travelling comfort. The travelling performance is generally assessed at the design stage in automobile industries by simulating the vehicle response to various road excitations. In this work, the disturbance from the road is assumed to be a jerk which represents a sudden shock. Initially a PID controller is designed and tuned to give better response. Secondly the PID tuning parameters are optimized using popular stochastic global optimization technique - Genetic Algorithm (GA) by considering the body acceleration as the Performance Index (PI). At last, an ANFIS which inherits the features of Neural Network (NN) and Fuzzy Logic (FL) is used for control purpose. The modelling of system and simulation with and without controllers are carried out in MATLAB/ Simulink environment. A comparison is made among the responses obtained and it shows that, the system with PID based ANFIS gives significant reduction of the Sprung mass Displacement (SD) and thus improves the travelling comfort.

Adaptive neuro-fuzzy inference system-genetic algorithm vs. response surface methodology: A case of optimization of ferric sulfate-catalyzed esterification of palm kernel oil

A comparative workflow, including linear and non-linear QSAR models, was carried out to evaluate the predictive accuracy of models and predict the inhibition activity of a series of aryl-substituted isobenzofuran-1(3H)-ones. The data set consisted of 34 compounds was classified into the training and test sets, randomly. Molecular descriptors were selected using the genetic algorithm (GA) as a feature selection tool. Various linear models based on multiple linear regression (MLR), principle component regression (PCR) and partial least square (PLS) and non-linear models based on artificial neural network (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM) methods were developed and compared. The accuracy of the models was studied by leave-one-out cross-validation (Q_LOO^2), Y-randomization test and group of compounds as external test set. Six descriptors were selected by GA to develop predictive models. With respect to the linear models, GA-PCR method was more accurate than the reset with statistical results of 〖 R〗_train^2=0.883, R_test^2=0.897,〖 R〗_(adj,train)^2=0.829,〖 R〗_(adj,test)^2=0.849,〖 F〗_train=24.07 and F_test=34.17. In case of non-linear models, GA-SVM (R_train^2=0.992 and R_test^2=0.997) showed high predictive accuracy for the inhibitory activity. It was found that the selected descriptors have the major roles in interpretation of biological activities of the compounds.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function’s variables and multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system model. Also, the singular value decomposition method is applied to compute the linear parameters of the adaptive neuro-fuzzy inference system method. The important conflicting objectives of developed adaptive neuro-fuzzy inference system, namely, training error and prediction error, are obtained by dividing date sets into two parts. Hence, various optimal choices of adaptive neuro-fuzzy inference system model are provided which are non-dominated states from each other. Moreover, optimal Pareto front of such model leads to trade-off between the conflicting pair of considered objectives for two series of experiments. The results of this work indicate that multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system predicts central deflection of plates with a good accuracy. In addition, the comparison between the adaptive neuro-fuzzy inference system model and exiting one demonstrates superior performance of the present approach in simulating central deflection of plates.

The liver is a vital organ and the largest gland in the human body, responsible for numerous essential functions such as metabolism, digestion, protein synthesis, and the removal of harmful toxins. It plays a central role in maintaining overall health and ensuring the body's proper functioning. Malfunction or damage to the liver can lead to a variety of serious health issues, including liver failure, cirrhosis, and fatty liver disease, all of which can significantly impact a person's quality of life and life expectancy. Early detection and accurate diagnosis of liver diseases are crucial for timely treatment, prevention, and minimizing the risk of further complications. Traditional diagnostic methods for liver diseases often involve invasive procedures, which can be expensive, time-consuming, and uncomfortable for patients. This research explores an innovative, non-invasive approach to diagnose and assess the severity of fatty liver disease by analyzing blood test results. The study applies various advanced techniques, including the Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS combined with Particle Swarm Optimization (PSO), and ANFIS with Genetic Algorithms (GA), to a dataset collected from the Tabriz University research center. These methods are compared in terms of their accuracy and efficiency in predicting the severity of fatty liver disease, providing valuable insights into the potential of machine learning algorithms for medical diagnostics. The findings aim to contribute to the development of more accessible, cost-effective, and non-invasive diagnostic tools for liver diseases.

Digital images of the soybean and three species of weeds with different backgrounds were acquired for image processing. An adaptive neuro-fuzzy inference system (ANFIS) from MATLAB® 6.1 was used to obtain a preliminary separation of plant and background. To train the fuzzy inference system (FIS), red-green-blue (RGB) values were first converted to HSV color space (hue, saturation, value), and samples from plant and background were visually selected from the images to be used as inputs. These were then mapped to a binary output that indicated the presence or absence of plant. Subtractive clustering was used to generate the FIS with a total of 100 epochs. Three clusters, and therefore three rules, were found by the ANFIS. This resulted in a total of 9 membership functions. Source code for a genetic algorithm was written using the toolbox of MATLAB® 6.1 to adjust the membership functions to reduce misclassification and improve segmentation. The membership function parameters were used to generate chromosome genes. The initial population of 50 chromosomes was generated using 30% noise perturbation of the membership functions originally given by ANFIS. A final segmentation of background and plant was achieved after 10 generations. Between evaluations crossover and mutations were applied. The results showed that the adaptive neuro-fuzzy inference system and genetic algorithm segmentation system was capable of eliminating areas that were misclassified as plant and also generally the final segmentation.

A study is presented to model surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs). The machining parameters, namely, the spindle speed, feed rate, depth of cut and the workpiece-tool vibration amplitude have been used as inputs to model the workpiece surface roughness. The number and the parameters of membership functions used in ANFIS along with the most suitable inputs are selected using GAs maximising the modelling accuracy. The ANFIS with GAs (GA-ANFIS) are trained with a subset of the experimental data. The trained GA-ANFIS are tested using the set of validation data. The procedure is illustrated using the experimental data of a CNC vertical machining centre in end-milling of 6061 aluminum. Results are compared with other soft computing techniques like genetic programming (GP) and artificial neural network (ANN). The results show the effectiveness of the proposed approach in modelling the surface roughness.

An assessment of metaheuristic approaches for flood assessment