Adaptive Neuro-fuzzy Inference System Approach Research Articles

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

This study addressed the critical problem of repairing cracks in aging aircraft structures, a safety concern of paramount importance given the extended service life of modern fleets. Utilized a finite element (FE) method enhanced by the design of experiments (DOE) and adaptive neuro-fuzzy inference system (ANFIS) approaches to analyze the efficacy of piezoelectric actuators in mitigating stress intensity factors (SIF) at crack tips—a novel integration in structural repair strategies. Through simulations, we examined the impact of various factors on the repair process, including the plate, actuator, and adhesive bond size and characteristics. In this work, initially, the SIF estimation used the FE approach at crack tips in aluminum 2024-T3 plate under the uniform uniaxial tensile load. Next, numerous simulations have been performed by changing the parameters and their levels to collect the data information for the analysis of the DOE and ANFIS approach. The FE simulation results have shown that changing the parameters and their levels will result in changing of SIF. Several DOE and ANFIS optimization cases have been performed for the depth analysis of parameters. The current results indicated that optimal placement, size, and voltage applied to the piezoelectric actuators are crucial for maximizing crack repair efficiency, with the ability to significantly reduce the SIF by a quantified percentage under specific conditions. This research surpasses previous efforts by providing a comprehensive parameter optimization of piezoelectric actuator application, offering a methodologically advanced and practically relevant pathway to enhance aircraft structural integrity and maintenance practices. The study innovation lies in its methodological fusion, which holistically examines the parameters influencing SIF reduction in aircraft crack repair, marking a significant leap in applying intelligent materials in aerospace engineering.

Effect of magnetic field on the thermal conductivity and viscosity of magnetic manganese Oxide/Ethylene glycol Nanofluids: An experimental and ANFIS approach

The magnetic manganese oxide (Mn3O4) nanowires were prepared by using solvothermal method and characterized by using X-ray diffraction, X-ray photoelectron spectroscopy and vibrating sample magnetometer techniques. The stable Mn3O4/ethylene glycol nanofluids were prepared in the particle volume loadings ranging from 0 % to 1.25 %. The thermal conductivity and viscosity were measured in the temperatures ranging from 20 °C to 60 °C and in the magnetic field ranging from 0 to 2350 Gauss. An adaptive neuro-fuzzy inference system (ANFIS) algorithm was used to correlate the measured thermal conductivity and viscosity values. Experiments were shown that at ϕ = 1.25 % vol. of nanofluid, the thermal conductivity is enhanced by 18.8 % at 60 °C; moreover, the viscosity of ϕ = 1.25 % vol. of nanofluid is raised by 89.4 % at 20 °C, compared to base fluid. Further results showed that, nanofluid at ϕ = 1.25 % with magnetic field of 1150 Gauss, and at 60 °C the thermal conductivity is enhanced by 52.4 %; however, nanofluid at ϕ = 1.25 % with magnetic field of 1450 Gauss, and at 20 °C, the viscosity is enhanced by 207 % against the same nanofluid without magnetic field. The used algorithm was successfully predicted the target data with a root mean square error of 0.0013744 and 0.311183 for thermal conductivity and viscosity data.

An Explanatory Model: The Relationship between Ergonomic Climate and Job Stress Using Adaptive Neuro-Fuzzy Inference System Approach in One of Urmia Hospitals

Background and purpose: Job stress is a growing problem worldwide that affects not only the health and well-being of employees but also the productivity of organizations. Ergonomics climate shows employees' understanding of the organization's support and emphasis on job design and modification in order to improve "operational performance" and "employee well-being". The present study aims to provide a model to explain Job stress with a focuson ergonomics climate was made using the adaptive neuro-fuzzy inference system approach. Materials and methods: This research is applied in terms of purpose and descriptive and correlational in terms of data collection method. The statistical population of the current research is the healthcare staff of one of the hospitals in Urmia city. model through evaluation criteria RMSE[1], MAE[2] and R2[3] were validated. Findings : The correlation coefficient between ergonomic climate and job stress for 376 data is - 0.629. which indicates an inverse relationship. The obtained results indicate the high validity of the model of Adaptive Neuro-Fuzzy - Inference System (RMSE=0.02) for evaluating and predicting Job stress centered on ergonomics climate. Conclusion: Ergonomics climate and its dimensions including operational performance and employee well-being have an inverse relationship with job stress of employees. The presented model can predict with high reliability the level of job stress of employees using ergonomics climate.

The aerodynamic force estimation of a swept-wing UAV using ANFIS based on metaheuristic algorithms

Abstract In this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. Metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.

Priority queue-based switching matrix algorithm for adaptive neuro-fuzzy inference system assisted MPPT controlled PV system

The photovoltaic array output is substantially mitigated by regularly occurring, inevitable shadowing conditions. Subsequently, the array's characteristics exhibit several peaks, which causes the traditional maximum power point tracking (MPPT) controllers to inevitably get trapped at the local optimum. Therefore, an adaptive neuro-fuzzy inference system (ANFIS) approach has been proposed for predicting the optimal duty ratio to track the global maximum power among numerous peaks. To dispense the shading impact for improving the GMP and minimization of multiple peaks, a novel priority queue-based reconfiguration algorithm is proposed. The efficacy of the proposed algorithm has been validated for 46 distinct uniform, non-uniform, and dynamic shading conditions for symmetrical 10 × 10, 9 × 9 and unsymmetrical 6 × 4, 6 × 20, 6 × 21 arrays and its performance is compared with the 20 existing algorithms. Further, the efficacy of the combined ANFIS-MPPT with the proposed algorithm has also been validated. The performance of the proposed ANFIS has been compared to the conventional perturb and observe algorithm with and without reconfiguration. Additionally, the ease of global power tracking using a conventional MPPT due to the alleviation of peaks after reconfiguring the array has been presented in detail. Upon reconfiguration, the output is improved by 47.39%, 31.41%, 31.08%, and 9.48% employing the MPPT controller for the various cases. The combined reconfiguration and ANFIS-based methodology effectively tracks the global power time within 0.06 sec with minimal steady-state oscillations yielding a higher tracking efficiency of 99.49%. The proposed methodology is further validated experimentally using a laboratory prototype model.

Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network

Serious health issues can result from exposure to the nitrogenous pollutant like 2,4,6-trinitrotoluene (TNT), which is emitted into the environment by the munitions and military industries, as well as from TNT-contaminated wastewater. The TNT removal by extended aeration activated sludge (EAAS) was optimized in the current study using artificial neural network modeling. In order to achieve the best removal efficiency, 500 mg/L of chemical oxygen demand (COD), 4 and 6 h of hydraulic retention time (HRT), and 1–30 mg/L of TNT were used in this study. The kinetics of TNT removal by the EAAS system were described by the calculation of the kinetic coefficients K, Ks, Kd, max, MLSS, MLVSS, F/M, and SVI. Adaptive neuro fuzzy inference system (ANFIS) and genetic algorithms (GA) were used to optimize the data obtained through TNT elimination. ANFIS approach was used to analyze and interpret the given data, and its accuracy was around 97.93%. The most effective removal efficiency was determined using the GA method. Under ideal circumstances (10 mg/L TNT concentration and 6 h), the TNT removal effectiveness of the EAAS system was 84.25%. Our findings demonstrated that the artificial neural network system (ANFIS)-based EAAS optimization could enhance the effectiveness of TNT removal. Additionally, it can be claimed that the enhanced EAAS system has the ability to extract wastewaters with larger concentrations of TNT as compared to earlier experiments.

Application of ANFIS and ANN for Partial Discharge Localization in Oil Through Acoustic Emission

This paper presents an examination on the acoustic partial discharge (PD) localization in oil based on adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches. Impedance matching circuit was used to measure the electrical PD. The acoustic PD was obtained through an acoustic emission (AE) sensor and pre-amplifier gain unit. In total, 112 coordinates for each of the AE sensors were utilized to evaluate the location of the PD. Once the voltage reached 30 kV, the electrical and acoustic PDs were recorded. Next, the data were pre-processed by moving average and analyzed by time-of-arrival (TOA), ANFIS and ANN. The distance between PD and AE sensor were calculated based on TOA to determine the PD location. These information were used as an input to train the network by optimizing epoch and neuron for ANFIS and ANN in order to locate PD. ANFIS has the best percentage of PD source prediction based on root mean square error (RMSE) and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) as compared to ANN. Meanwhile, the computation time for ANN is 1.75 s faster than ANFIS to perform PD localization based on acoustic emission PD signals.

Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS).

In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this study, the breast dose of 50 adult female patients who underwent chest CT examinations was measured directly by TLDs. Then, the ANFIS model was developed with four inputs including dose length product (DLP), volumetric CT dose index (CTDIvol), total mAs, and size-specific dose estimate (SSDE), and one output (TLD dose). Additionally, multiple linear regression (MLR) as a traditional prediction model was used for linear modeling and its results were compared with the ANFIS. The TLD reader results showed that the breast dose value was 12.37 ± 2.46mGy. Performance indices of the ANFIS model, including root mean square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 0.93 for the testing dataset, respectively. Also, the ANFIS model had superior performance in predicting the breast dose than the MLR model (R = 0.805). This study demonstrates that the proposed ANFIS model is efficient for patient dose prediction in CT scans. Therefore, intelligence models such as ANFIS are suggested to estimate and optimize patient dose in CT examinations.

Design of a decision support system to achieve condition-based maintenance in ship machinery systems

A decision support system is proposed for the condition-based maintenance of ship machinery systems based on the adaptive neuro-fuzzy inference system (ANFIS) approach. A case study is conducted for a container ship's main diesel engine. Within the scope of the methodology, the main engine power is predicted based on exhaust gas outlet temperatures of cylinders and the main engine shaft RPM. In this regard, firstly, two different strategies such as the creation of the cylinder-basis model and the development of the overall system model are developed to determine the ANFIS model structure for the analysis. Then, comparative analyses are carried out to select a suitable ANFIS structure and its specific membership functions. In addition, the estimation process is also performed by the artificial neural network (ANN) model, and its results are compared with the findings of the best ANFIS structure. The success of the constructed models is evaluated by some error metrics. The overall ANFIS model with 5 membership functions is determined as the best approach by scores of 0.9806 for R2, 1.6588 MW for RMSE, and 3.2703 for MAPE. As a result of the estimation procedure, a decision support system to assist marine operators in maintenance operations is developed. The proposed strategy can be applied to different types of systems in the ship engine room such as the fuel oil system and can be improved by including more parameters obtained from the system's significant points in the analysis.

Unveiling the impact of managerial traits on investor decision prediction: ANFIS approach

AbstractInvestment decisions are influenced by various factors, including personal characteristics and managerial issues. In this research, we aimed to investigate the impact of managerial traits on investment decisions by using adaptive neuro-fuzzy inference system (ANFIS) to develop a personalized investment recommendation system. We collected data from potential investors through a survey, which included questions on investment-types, investment habits, and managerial traits. The survey data were used to create an ANFIS model, which is a hybrid model that combines the strengths of both artificial neural networks and fuzzy logic systems. The ANFIS model was trained using 1542 survey data pairs, and the model's performance was evaluated using a validation set. The results of the ANFIS model showed that the model had a minimal training root mean square error of 0.837341. The ANFIS model was able to effectively capture the relationship between managerial traits and investment decisions and was able to make personalized investment recommendations based on the input data. The results of this research provide valuable insights into the impact of managerial traits on investment decisions and demonstrate the potential of ANFIS in developing personalized investment recommendation systems. In conclusion, this research aimed to investigate the impact of managerial traits on investment decisions using ANFIS. The results of this study demonstrate the potential of ANFIS to personalize investment recommendations based on the input data. This research can be used as a foundation for future research in the field of investment recommendations and can be helpful to investors to take their decision-making.

Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

Performance of Statistical and Intelligent Methods in Estimating Rock Compressive Strength

This research was conducted to forecast the uniaxial compressive strength (UCS) of rocks via the random forest, artificial neural network, Gaussian process regression, support vector machine, K-nearest neighbor, adaptive neuro-fuzzy inference system, simple regression, and multiple linear regression approaches. For this purpose, geo-mechanical and petrographic characteristics of sedimentary rocks in southern Iran were measured. The effect of petrography on geo-mechanical characteristics was assessed. The carbonate and sandstone samples were classified as mudstone to grainstone and calc-litharenite, respectively. Due to the shallow depth of the studied mines and the low amount of quartz minerals in the samples, the rock bursting phenomenon does not occur in these mines. To develop UCS predictor models, porosity, point load index, water absorption, P-wave velocity, and density were considered as inputs. Using variance accounted for, mean absolute percentage error, root-mean-square-error, determination coefficient (R2), and performance index (PI), the efficiency of the methods was evaluated. Analysis of model criteria using multiple linear regression allowed for the development of a user-friendly equation, which proved to have adequate accuracy. All intelligent methods (with R2 &gt; 90%) had excellent accuracy for estimating UCS. The percentage difference of the average of all six intelligent methods with the measured value was equal to +0.28%. By comparing the methods, the accuracy of the support vector machine with radial basis function in predicting UCS was (R2 = 0.99 and PI = 1.92) and outperformed all the other methods investigated.

Modeling and Optimization of Hexavalent Chromium Adsorption by Activated Eucalyptus Biochar Using Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System

Due to its excellent textural features, non-toxicity, low cost and high uptake capacity, biochar has been synthesized from various biomasses and utilized as a biosorbent to remove hexavalent chromium (Cr6+) from contaminated water. Herein, activated eucalyptus biochar (AEB) was prepared via a pyrolysis-chemical activation process and then used as a less expensive biosorbent to adsorb Cr6+ ions from an aqueous solution. Proximate, ultimate, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analyses were employed in appraising the biosorbent characteristics. Furthermore, response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) were applied to establish the best operating conditions. Based on the results obtained, there was little discrepancy between the observed data and the data predicted by RSM and ANFIS approaches. In terms of prediction accuracy, ANFIS (MAE = 2.512 and R2=0.9200) was superior to RSM (MAE = 2.512 and R2=0.9002). Under best-optimized conditions (initial Cr6+ concentration = 38.14 mg/L, biosorbent dosage = 1.33 g/L and pH = 4.35), which were offered by the ANFIS–ACO technique, the maximum percentage removal of 99.92 ± 0.18% was achieved. The AEB performed exceptionally well due to its better textural characteristics, well-developed porous framework, and dominance of active surface functional groups, which were confirmed by BET, SEM, and FTIR analyses. The comparison of RSM, ACO and GA for process parameter optimization has not been reported in the open literature for Cr6+ adsorption by AEB and hence has been shown in this study.

Predictive modeling of tool wear in rotary tool micro-ultrasonic machining

In the current research endeavor, the predictive model of tool wear in rotary tool micro-ultrasonic machining (RTMUSM) process using artificial intelligence has been reported for the first time. An adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches are adopted for modeling of total volumetric wear (TVW) in RTMUSM process. The models were developed after conducting the exhaustive experimentation. Six RTMUSM input process parameters (i.e. abrasive size, rotation speed, depth of channel, slurry concentration, feed rate, and power rating) were selected as input data with TVW of tool as output data. The developed model has been validated through experimental results. Further, statistical analysis by calculating variance account for (VAF), coefficient of correlation ( R-value), mean absolute percentage error (MAPE), and root mean square error (RMSE) was carried out to estimate the potential of the models. The results obtained by statistical analysis proclaimed that the performance of ANFIS model ( R-value = 0.9966, MAPE = 1.26%, RMSE = 1.11%, and VAF = 99.94) was superior as compared to ANN model and it can be used to measure the TVW of tool in RTMUSM. Furthermore, genetic algorithm based optimization was performed and required wear compensation of tool is recommended.

Modeling Soil Water Retention Under Different Pressures Using Adaptive Neuro-Fuzzy Inference System

Soil Water Retention (SWR) is an important process in drainage, surface, and groundwater partitioning, hydrological modeling, water supply for irrigation, etc. Assessment of SWR characteristics is complex and difficult to conduct spatially in varied locations. Therefore, Pedotransfer Functions (PTF) which are empirical relations with easily available physical properties are commonly used. In the present study, the evaluation of soil moisture at different suction pressure using the adaptive neuro-fuzzy inference systems (ANFIS) approach based on soil texture (percentage of gravel, sand, silt, and clay) and compare with the PTF approach. The analysis was conducted for a total of eleven sites of two adjoining commands in India. The pressure plate apparatus along with coarse and fine sieve analysis, titration, and other tests were carried out to determine SWR, texture, organic carbon, and bulk density. The comparative analysis of Nash–Sutcliffe efficiencies of the best-fitted PTF models and ANFIS model confirmed that the ANFIS model can capture all variations of soil texture across all sites with Nash–Sutcliffe efficiency of nearly 1.0 indicative of an exact match, while no single PTF-based model can be used for all the sites. Therefore, the ANFIS model can be used to model soil water retention for the central India region using easily available texture properties of soils.

Adaptive Neuro-Fuzzy Inference System (ANFIS) Method for Developing a Decision Support System for Determining Landslide Susceptibility

Landslide catastrophes are one of the disasters that frequently occur in Indonesia owing to the weather and climatic features, regional terrain, and geological formations that make this nation prone to landslides. The primary goal of this research is to compare the application of the fuzzy logic technique and the adaptive neuro-fuzzy inference system (ANFIS) approach to landslide detection sensors based on prior research in order to identify landslide-prone locations more easily. The Adaptive Neuro-Fuzzy Inference System (ANFIS) technique analyzes the landslide area using three factors. Rainfall, land slope, and soil moisture are examples of these factors. This variable is used to assess the area's level of vulnerability to landslides: very safe, relatively safe, relatively potential, potential, and very potential. In the study, each piece of data is subjected to a training and testing procedure to identify landslide vulnerability, with the factors and weighting methods aligned with current government standards. This study compares the rules outcomes to those of past studies as well as the system results. Based on the studies findings, it can be stated that the decision support system for the degree of landslide vulnerability utilizing the ANFIS approach is superior to the fuzzy logic method, with an accuracy rate of 86.21%.

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

Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified into normal, benign, and malignant types using the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach in this paper. This mammogram classification process consists of a noise filtering module, spatial-frequency transformation module, feature computation module, and classification module. The Gaussian Filtering Algorithm (GFA) is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation module. The statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this paper. Finally, Probability Histogram Segmentation Algorithm (PHSA) is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram images. This proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM datasets. From the extensive analysis of the proposed tumor detection methods stated in this work with other works, the proposed work significantly achieves a higher performance. The methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.

Adaptive neuro-fuzzy inference system based data interpolation for particle image velocimetry in fluid flow applications

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) approach for recovering the missing velocity vectors that commonly occur during fluid flow measurements in fluid mechanics. The capability of ANFIS in refilling the missing data is demonstrated with two case studies. First, the ANFIS is applied to estimate the velocity field data within the masked region of an available particle image velocimetry (PIV) experiment. Then, the ANFIS is trained using the data from outside the masked region and learns the relationship between the velocity vectors. Thus, it predicts the fluid patterns within the gappy area based on its understanding. ANFIS is also applied in another study to capture the small feature in the fluid flow. The vortices within a small area behind an obstacle are removed, and the rest of the data is introduced into the ANFIS model. The efficacy of the proposed ANFIS algorithm in predicting the missing velocity vectors is compared to both artificial neural network (ANN) and 2D cubic interpolation algorithms. It is found that intelligent algorithms (ANFIS and ANN) can predict the presence of vortices in the fluid flow even when there is no information about a circulating flow in the training dataset. The performance of ANFIS (R2= 0.91) in accurately predicting the velocity vectors is superior to the conventional ANN (R2= 0.86).

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model.

Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique's goal is to identify osteosarcoma's existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.

Assessments of Acoustical Performance of Classrooms and Teachers’ Acoustical Comfort in The School Environment – A Case Study

In recent times, damage identification based on vibration methods are emerging as common approaches, in which the techniques apply the vibration response of a monitored structure, such as modal frequencies and damping ratios, to evaluate its condition and detect structural damage. The basis of the vibration-based health monitoring method is that when there are alterations in the physical characteristics of a structure, there will also be changes in its vibration properties. This paper proposed a neuro-fuzzy artificial intelligence method, called adaptive neuro-fuzzy inference system (ANFIS), to detect damage using modal properties. To generate the modal characteristics of the structures, experimental study and finite element analysis of I-beams with single damage cases were performed. The results showed that the ANFIS approach was able to detect the magnitude and location of the damage with a significant degree of precision, and notably reduced computational time.