Dynamical behaviour and meta heuristic optimization of a hospital management software system.

The growing demand for easily available healthcare in recent years has fuelled the digitization of healthcare services. The Hospital Management System (HMS) software stands out as a comprehensive solution among the software systems and tools that hospitals and clinics are developing in tandem with this trend. In order to effectively manage many facets of hospital operations, in this paper, we propose an approach for investigating software of this kind. Thus, we characterise the HMS software as a unique sort of batch arrival retrial queueing system (QS) that can handle both ordinary and priority patient demands. Furthermore, it permits patient resistance (balk) and departure (renege) in specific circumstances. The proposed model is additionally deployed within the framework of Bernoulli working vacation. The supplementary variable technique (SVT) has been utilised to obtain the necessary results. ANFIS, a soft computing tool, is used to validate the analytical results as well. Finally, this study seeks to enhance the cost-effectiveness of software creation by employing four unique optimization methods, aiming to achieve optimal efficiency in resource utilization.

Experimental and artificial intelligence optimization of paint wastewater (PWW) coagulation using novel Phaseolus vulgaris seed extract (PVSE).

This study investigated the application of artificial intelligence algorithms (AIA) in the coagulation treatment of paint wastewater anchored by novel Phaseolus vulgaris seed extract (PVSE). Untreated wastewater discharge harms the ecosystem, and therefore harmful industrial effluent, such as paint wastewater, must be brought to safe discharge levels before being released into the environment. In addition to AIA, comprehensive characterization tests, coagulation kinetics, and process optimization were also executed. Characterization results revealed that total solid in the PWW was above allowable standard, justifying the need for effective particle decontamination. The XRD and FTIR characterization indicated that PVSE structure is amorphous with abundant amine groups. Results of analysis of variance (ANOVA) obtained from process modeling indicated that the coagulation-flocculation process was a nonlinear quadratic system (F-value = 45.51) which was mostly influenced by PVSE coagulant dosage (F-value = 222.48; standardized effect = 14.85). Artificial intelligence indicated that neural network training effectively captured the nonlinear nature of the system in ANN (RMSE = 0.00040194; R = 0.98497), and ANFIS (RMSE = 0.003961) algorithms. Regression coefficient obtained from process modeling highlighted the suitability of RSM (0.9662), ANN (0.9739), and ANFIS (0.9718) in forecasting the coagulation-flocculation process, while comparative statistical appraisal authenticated the superiority of ANN model over RSM and ANFIS models. The coagulation kinetics experiment, which used a coagulation kinetic model, revealed a constant flocculation constant (Kf-value) for all jar test batches and a strong association between the Menkonu coagulation-flocculation constant (Km) and Kf values. Best removal efficiency of 97.01 % was obtained using ANN coupled genetic algorithm optimization (ANN-GA) at PVSE dosage of 4 g/L, coagulation time of 29 min and temperature of 25.1oC.

Design and analysis of UPQC in a microgrid using model reference adaptive control ensemble with back-stepping controller.

In recent years, the power sector has shifted to decentralized power generation, exemplified by microgrids that combine renewable and traditional power sources. With the introduction of renewable energy resources and distributed generators, novel strategies are required to improve reliability and quality of power (PQ). In our proposed system, a model consisting of photovoltaics, wind energy, and fuel cells has been designed to share a network, bolstered by the integration of UPQC to rectify PQ issues. Notably, our model introduces a Back-stepping controller method featuring Model Reference Adaptive Control (MRAC) with online parameter tuning, offering superior adaptability and responsiveness. This approach not only ensures optimal grid management but also enhances efficiency and stability. Furthermore, the proposed model demands minimal additional infrastructure, leveraging existing resources to streamline implementation and maintenance, thereby promoting sustainability and cost-effectiveness. The research culminates in a comparative analysis between the MRAC-Back-stepping controller, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy controller, highlighting the efficacy and versatility of our proposed model in microgrid operations. A Matlab model has been designed along with a hardware setup to demonstrate the robustness of the model.

Numerical model of debris flow susceptibility using slope stability failure machine learning prediction with metaheuristic techniques trained with different algorithms.

In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained using novel metaheuristic methods. The application of these training mechanisms was necessitated by the need to enhance the robustness and performance of the three main machine learning methods. It was necessary to develop intelligent models for the prediction of the FOS of debris flow down a slope with measured geometry due to the sophisticated equipment required for regular field studies on slopes prone to debris flow and the associated high project budgets and contingencies. With the development of smart models, the design and monitoring of the behavior of the slopes can be achieved at a reduced cost and time. Furthermore, multiple performance evaluation indices were utilized to ensure the model's accuracy was maintained. The adaptive neuro-fuzzy inference system, combined with the particle swarm optimization algorithm, outperformed other techniques. It achieved an FOS of debris flow down a slope performance of over 85%, consistently surpassing other methods.

Eco-Friendly Nanotechnology in Rheumatoid Arthritis: ANFIS-XGBoost Enhanced Layered Nanomaterials.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and pain in the joints, which can lead to joint damage and disability over time. Nanotechnology in RA treatment involves using nano-scale materials to improve drug delivery efficiency, specifically targeting inflamed tissues and minimizing side effects. The study aims to develop and optimize a new class of eco-friendly and highly effective layered nanomaterials for targeted drug delivery in the treatment of RA. The study's primary objective is to develop and optimize a new class of layered nanomaterials that are both eco-friendly and highly effective in the targeted delivery of medications for treating RA. Also, by employing a combination of Adaptive Neuron-Fuzzy Inference System (ANFIS) and Extreme Gradient Boosting (XGBoost) machine learning models, the study aims to precisely control nanomaterials synthesis, structural characteristics, and release mechanisms, ensuring delivery of anti-inflammatory drugs directly to the affected joints with minimal side effects. The in vitro evaluations demonstrated a sustained and controlled drug release, with an Encapsulation Efficiency (EE) of 85% and a Loading Capacity (LC) of 10%. In vivo studies in a murine arthritis model showed a 60% reduction in inflammation markers and a 50% improvement in mobility, with no significant toxicity observed in major organs. The machine learning models exhibited high predictive accuracy with a Root Mean Square Error (RMSE) of 0.667, a correlation coefficient (r) of 0.867, and an R2 value of 0.934. The nanomaterials also demonstrated a specificity rate of 87.443%, effectively targeting inflamed tissues with minimal off-target effects. These findings highlight the potential of this novel approach to significantly enhance RA treatment by improving drug delivery precision and minimizing systemic side effects.

Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study.

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R2 = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.

Decoding career success: A personality-based analysis of data science Professional based on ANFIS modeling.

Career selection is one of the most important decisions every person faces in their life. Finding the right career path can be a complicated task, particularly in choosing careers with similarly required proficiencies. One of the critical factors affecting a person's career success is their personality, and taking account of this factor is of paramount importance. This study uses the NEO-FFI questionnaire to find personality patterns of software engineering and data science experts based on the Big Five personality traits: Neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Afterward, an ANFIS (Adaptive Network-Based Inference System) is conducted using the experts' personality data to match the participants of these fields with their corresponding choices. This study demonstrated that data scientists and software engineers score higher in conscientiousness and agreeableness, respectively. Also, data experts have higher scores in all traits overall. In the end, the ANFIS is tested with another similar dataset and the prediction accuracy of the model is measured.

Empirical analysis of dust and noise pollutants produced in the wood-CNC machining process.

In the manufacturing processes, consideration of sustainability is of particular importance. The current study is concerned with the influences of changing the process variables on the reduction of pollutions in the wood-CNC machining operation. Noise and dust are the studied pollutants in the present research work. Process variables include feed rate, spindle speed, step-over, and depth of cut, and the aim is to predict the behavior of aforementioned pollutants variations in the current process. The amounts of these harmful factors are measured based on existing standards. In order to analyze the findings, adaptive neuro-fuzzy inference system (ANFIS) and regression analysis methods have been employed, separately. The effects of process parameters on response variables have been comprehensively studied. The research findings demonstrated that for the present problem, ANFIS outcomes are more accurate. According to the mean absolute error (MAE) criterion, the prediction errors of ANFIS for noise and dust factors were computed to, in turn, 0.50 and 14.89. Meanwhile, the error values for prediction of noise and dust responses using regression analysis were calculated as 1.54 and 34.62, respectively.

Surfactant-modified coconut coir powder (SMCCP) as a low-cost adsorbent for the treatment of dye-contaminated wastewater: parameters and adsorption mechanism.

The dye-contaminated wastewater discharged from various industries such as dye manufacturing, paint, textile, paper, and cosmetic is a prime source of surface water pollution having serious detrimental effects on both the environment and human beings. These hazardous dyes when exposed to water obstruct the penetration of sunlight into the water and thus restrain aquatic plants from generating photosynthetic compounds. Moreover, some dyes are potential cancer-causing and also negatively impact the human nervous and respiratory systems. In this current study, modification of coconut coir powder (CCP) was carried out through cationic surfactant treatment and was successively utilized as the adsorbent for decoloring anionic dye (acid blue 185 (AB 185)) containing waste stream. Further, a comparative investigation of the dye removal efficiency of raw CCP and surfactant-modified coconut coir powder (SMCCP) as the adsorbent was studied. On surfactant treatment, using a very minimal SMCCP dosage of 8.3 g/L, a very high percentage dye removal of 98.4% is possible, whereas with raw CCP, even after using a higher dosage of 14 g/L, only 70.1% dye removal can be achieved. Characterization of SMCCP adsorbent was done by Fourier transform infrared, thermogravimetric, X-ray, and scanning electron microscope analyses. Furthermore, the optimization of critical operating parameters was investigated for the effective adsorption of AB 185 dye in batch mode. The adsorption of AB 185 onto SMCCP was a thermodynamically spontaneous endothermic process, following the Langmuir isotherm and pseudo-second-order kinetic model. Moreover, regeneration of exhausted SMCCP by 0.1 (M) NaOH was achieved with a satisfactorily high recovery of 97% in the first cycle. Subsequently, SMCCP can be successfully reutilized for five consecutive cycles with a loss of 17.6% in the total adsorption capacity. With all such advantages, the present study delivers a new paradigm to utilize the novel adsorbent SMCCP as a promising eco-friendly adsorbent aided by its advantage of regeneration and reusability for the treatment of dye-contaminated wastewater.

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose.

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6 m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80 nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10 mg/L), contact time (t, 10-1440 min), initial pH (pHi, 2-10), temperature (T, 10-55 °C), and initial concentration of nitrate (C0, 10-500 mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector.

This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system's robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

Evaluation of student failure in higher education by an innovative strategy of fuzzy system combined optimization algorithms and AI.

This research suggests two novel metaheuristic algorithms to enhance student performance: Harris Hawk's Optimizer (HHO) and the Earthworm Optimization Algorithm (EWA). In this sense, a series of adaptive neuro-fuzzy inference system (ANFIS) proposed models were trained using these methods. The selection of the best-fit model depends on finding an excellent connection between inputs and output(s) layers in training and testing datasets (e.g., a combination of expert knowledge, experimentation, and validation techniques). The study's primary result is a division of the participants into two performance-based groups (failed and non-failed). The experimental data used to build the models measured fourteen process variables: relocation, gender, age at enrollment, debtor, nationality, educational special needs, current tuition fees, scholarship holder, unemployment, inflation, GDP, application order, day/evening attendance, and admission grade. During the model evaluation, a scoring system was created in addition to using mean absolute error (MAE), mean squared error (MSE), and area under the curve (AUC) to assess the efficacy of the utilized approaches. Further research revealed that the HHO-ANFIS is superior to the EWA-ANFIS. With AUC = 0.8004 and 0.7886, MSE of 0.62689 and 0.65598, and MAE of 0.64105 and 0.65746, the failure of the pupils was assessed with the most significant degree of accuracy. The MSE, MAE, and AUC precision indicators showed that the EWA-ANFIS is less accurate, having MSE amounts of 0.71543 and 0.71776, MAE amounts of 0.70819 and 0.71518, and AUC amounts of 0.7565 and 0.758. It was found that the optimization algorithms have a high ability to increase the accuracy and performance of the conventional ANFIS model in predicting students' performance, which can cause changes in the management of the educational system and improve the quality of academic programs.

Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: A case study Minia Governorate, Egypt.

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater.

Effects on quality characteristics of ultrasound-treated gilaburu juice using RSM and ANFIS modeling with machine learning algorithm.

Gilaburu (Viburnum opulus L.) is a red-colored fruit with a sour taste that grows in Anatolia. It is rich in various antioxidant and bioactive compounds. In this study, bioactive compounds and ultrasound parameters of ultrasound-treated gilaburu water were optimized by response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). As a result of RSM optimization, the independent ultrasound parameters were determined as an ultrasound duration of 10.7 min and an ultrasound amplitude of 53.3, respectively. The R2 values of the RSM modeling level were 99.93%, 98.54%, and 99.80%, respectively, and the R2 values of the ANFIS modeling level were 99.99%, 98.89%, and 99.87%, respectively. Some quality parameters of gilaburu juice were compared between ultrasound-treated gilaburu juice (UT-GJ), thermal pasteurized gilaburu juice (TP-GJ), and control group (C-GJ). The quality parameters include bioactive compounds, phenolic compounds, minerals, and sensory evaluation. Bioactive compounds in the samples increased after ultrasound application compared to C-GJ and TP-GJ samples. The content of 15 different phenolic compounds was determined in Gilaburu juice samples, and the phenolic compound of UT-GJ samples increased compared to TP-GJ and C-GJ samples, except for gentisic acid. Ultrasound treatment applied to gilaburu juice enabled its bioactive compounds to hold more in the juice.

A Comparative Analysis of ARX and ANFIS Models for Tumor Growth Prediction Under Single and Multi-agent Chemotherapy.

BACKGROUND/AIM: Despite the advances in oncology and cancer treatment over the past decades, cancer remains one of the deadliest diseases. This study focuses on further understanding the complex nature of cancer by using mathematical tumor modeling to understand, capture as best as possible, and describe its complex dynamics under chemotherapy treatment. MATERIALS AND METHODS: Focusing on autoregressive with exogenous inputs, i.e., ARX, and adaptive neuro-fuzzy inference system, i.e., ANFIS, models, this work investigates tumor growth dynamics under both single and combination anticancer agent chemotherapy treatments using chemotherapy treatment data on xenografted mice. RESULTS: Four ARX and ANFIS models for tumor growth inhibition were developed, estimated, and evaluated, demonstrating a strong correlation with tumor weight data, with ANFIS models showing superior performance in handling the multi-agent tumor growth complexities. These findings suggest potential clinical applications of the ANFIS models through further testing. Both types of models were also tested for their prediction capabilities across different chemotherapy schedules, with accurate forecasting of tumor growth up to five days in advance. The use of adaptive prediction and sliding (moving) data window techniques allowed for continuous model updating, ensuring more robust predictive capabilities. However, long-term forecasting remains a challenge, with accuracy declining over longer prediction horizons. CONCLUSION: While ANFIS models showed greater reliability in predictions, the simplicity and rapid deployment of ARX models offer advantages in situations requiring immediate approximations. Future research with larger, more diverse datasets and by exploring varying model complexities is recommended to improve the models' reliability and applicability in clinical decision-making, thereby aiding the development of personalized chemotherapy regimens.

Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach.

This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi-Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car.

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model's R2 value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.

Adaptive neuro-fuzzy inference system for forecasting corrosion rates of automotive parts in biodiesel environment.

It is precarious to scrutinize the impacts of operational parameters on corrosion when choosing materials for the green diesel and automotive industries. This was the original study to showcase an optimization stratagem for abating corrosion rates (CRs) of automotive parts (APs) explicitly copper and brass in a biodiesel environment, adopting novel Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS).To model CRs, the RSM and ANFIS were utilized. The mechanical properties of APs were inspected, explicitly their hardness number and tensile strength, as well as their outward morphologies. The optimal CRs for copper and brass were 0.01656 mpy and 0.008189 mpy at a B 3.91 biodiesel/diesel blend and 240.9-h exposure. The ANFIS model had a higher coefficient of determination and lower values of root mean squared errors (RMSE), mean average error (MAE), and average absolute deviation (AAD) when compared to the RSM model; this authenticates the ANFIS model's superiority for predicting CRs of copper and brass. The tensile strength of brass was greater than that of copper, while the latter had a higher hardness number. The information, model, and correlations can assist APS in mitigating and slaving over for the corrosiveness of APs while utilizing green diesel.

A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments.

Autonomous vehicles (AVs) require accurate navigation, but the reliability of Global Navigation Satellite Systems (GNSS) can be degraded by signal blockage and multipath interference in urban areas. Therefore, a navigation system that integrates a calibrated Reduced Inertial Sensors System (RISS) with GNSS is proposed. The system employs a machine-learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) as a novel calibration technique to improve the accuracy and reliability of the RISS. The ANFIS-based RISS/GNSS integration provides a more precise navigation solution in such environments. The effectiveness of the proposed integration scheme was validated by conducting tests using real road trajectory and simulated GNSS outages ranging from 50 to 150 s. The results demonstrate a significant improvement in 2D position Root Mean Square Error (RMSE) of 43.8% and 28% compared to the traditional RISS/GNSS and the frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated navigation systems, respectively. Moreover, an improvement of 47.5% and 23.4% in 2D position maximum errors is achieved compared to the RISS/GNSS and the Rad/RISS/GNSS integrated navigation systems, respectively. These results reveal significant improvements in positioning accuracy, which is essential for safe and efficient navigation. The long-term stability of the proposed system makes it suitable for various navigation applications, particularly those requiring continuous and precise positioning information. The ANFIS-based approach used in the proposed system is extendable to other low-end IMUs, making it an attractive option for a wide range of applications.