Adaptive Neuro-fuzzy Inference System Model Research Articles

Hybrid model for microgrid short term load forecasting based on machine learning

Short-term load forecasting (STLF) is crucial for microgrid (MG) operators to optimize energy generation and storage schedules based on anticipated load variations. Accurately predicting peak demand periods enables operators to ensure sufficient power supply while minimizing reliance on expensive backup sources, resulting in cost savings, and improved overall system efficiency. Residential MG power demand is highly dynamic due to external factors like residents’ lifestyles, behaviors, and weather responses, leading to significant irregularity and management challenges. To deal with these challenges, we propose a hybrid STLF model that combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), referred to as the Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was trained and tested using real power consumption data. We evaluated the performance of the ANFIS model by comparing it with another ANN model using three evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The trained ANFIS model achieved an MAPE of 8.7528% and an RMSE of 0.4752kw, while the ANN model achieved an MAPE of 8.8123% and an RMSE of 0.4816kw. These results confirm the accuracy of the hybrid ANFIS model compared to ANN. The ANFIS model demonstrated its ability to capture complex and nonlinear relationships between various factors affecting load demand, making it suitable for handling the dynamic nature of MG load.

Heat and Mass Transfer of Boundary Layer Jeffrey Ternary Hybrid Nanofluid Flow Over Porous Wedge With Surface‐Catalysed Chemical Reactions: An ANFIS Model

The current study employs machine learning (ML) techniques to investigate the heat and mass transport of Jeffrey ternary hybrid nanofluid (THNF). Various influential factors, including magnetic field, thermal radiation, viscous dissipation, activation energy, thermophoresis, Brownian motion and surface‐catalysed chemical reactions, are considered in the analysis. The porous moving wedge supports and influences the flow pattern. For thermal enhancement, three different nanoparticles, namely, Ag (silver), CuO (copper oxide) and SWCNT (single − walled carbon nanotubes), diluted in the regular fluid (blood). Initially, the mathematically modelled partial differential equations (PDEs) are solved numerically by the help of the shooting method. The suitable nondimensional similarity transformations are implied to convert the dimensional PDEs to nondimensional ordinary differential equations (ODEs). The reduced dimensionless nonlinear coupled ODEs are solved using ode‐45 in MATLAB. The impact of φAg, φCuO, φSWCNT in Cfx, Nux, Shx, is displayed in cone plots. It is found that, on increasing φAg, φCuO, φSWCNT, the heat transfer and mass transfer rate gets enhanced in THNF than the hybrid nanofluid (HNF) and nanofluid (NF). The influence of nondimensional parameters over f″(0), θ′(0), ϕ′(0), G′(0) and H′(0) is displayed in 3D contour plots for THNF and HNF. The radiation parameter (R), Brownian motion parameter (Nb), thermophoresis parameter (Nt) and volume fraction parameters φAg, φCuO, φSWCNT boost the energy transfer rate. Finally, the obtained numerical results are split into training and validation datasets that are used in designing the adaptive neuro‐fuzzy inference system (ANFIS) ML model. Five different ANFIS models are developed with the help of nondimensional parameters affecting f″(0), θ′(0), ϕ′(0), G′(0) and H′(0) to predict the physical quantities of dragging force (Cfx), energy transfer rate (Nux), the rate of mass transport (Shx) and mass fluxes for chemical species and , respectively. The training and checking errors attained the convergence less than 2 × 10−4 and 0.2, respectively, for all the five factors. The present ANFIS models have good balance between fitting the training data and generalising to new data, which can make accurate predictions irrespective of variations. The numerical and predicted ANFIS f″(0), θ′(0), ϕ′(0), G′(0) and H′(0) for training data, checking data and results are demonstrated in regression plots. From regression plots, we can observe that the Pearson correlation coefficient attains the positive correlation with R value closer to one. Hence, the developed ANFIS model shows the minimal error in predictions with high degree of accuracy of forecast in THNF flow.

Prediction of Tropospheric Ducting for UHF Signals In Plateau State Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

A study on the occurrence of tropospheric ducting for UHF signal in Plateau State using ANFIS had been investigated using the variation of the meteorological data; temperature, pressure, and relative humidity collected from NIMET for Jos, Plateau State Nigeria. The refractivity gradient was calculated using MATLAB as computational software. The conditions of occurrence of path clearance and tropospheric ducting were utilized to checkmate the occurrence of ducting over the period. The data gotten is used to train an ANFIS model, taking 70% of the data for model training, 20% for testing and 10% for validation. The integrity of the ANFIS model was tested using root mean square error (RMSE) and maximum absolute percentage error (MAPE) which confirmed that the model is accurate to 99% considered fit for forecasting. The developed model was able to mimic the tropospheric ducting trend up to a period of 7 years (2019- 2025) after successful simulation with an assertion that the rainy months are more prevailed to chances of tropospheric ducting occurrence as higher humidity prevails during this time which has more significance on refractivity gradient. An investigation was carried out on the effect of variation of tropospheric parameters; temperature, pressure and humidity on refractivity gradient using linear regression and Spearman correlation to ascertained the degree of dependence of each parameter and the result showed that refractivity gradient is strongly influenced by relative humidity with a correlation coefficient of -0.9772 as against temperature and pressure having correlation coefficients of -0.2587 and 0.2866 respectively.

The Comparative Early Prediction Model for Cardiovascular Disease Using Machine Learning

Cardiovascular disease (CVD) is a leading cause of death and a major contributor to disability. Early detection of cardiovascular disease using ANFIS has the potential to reduce costs and simplify treatment. This study aims to develop a prediction model using ANFIS (Adaptive Neuro-Fuzzy Inference System) for early detection of cardiovascular disease. The dataset used consists of 500 data with 12 features, including various risk factors such as blood sugar levels, cholesterol, uric acid, systolic blood pressure, diastolic blood pressure, body mass index (BMI), age, smoking habits, lifestyle, genetic factors, and gender, and one label feature. This study compares cardiovascular disease prediction models using machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and ANFIS. The development of the KNN algorithm involves the value of K=5 with the Euclidian distance measure. The SVM algorithm used a kernel cache of 200 and a convergence epsilon of 0.001. The ANFIS model was built using 500 data sets divided into training (70%) and testing (30%) data, with learning rate variations of 0.01, 0.05, 0.1, 0.2, and 0.5. The results of testing the early detection model show for SVM, the accuracy value is 0.760, the precision value is 0.839, and the recall value is 0.671. For the KNN model, the accuracy value is 0.758, the precision value is 0.768, and the recall value is 0.771. As for the ANFIS model, the accuracy value reaches 0.989, precision value 0.996, and recall value 0.988. The model using ANFIS has the highest performance. Further study of the model using ANFIS with learning rate variations shows that a learning rate of 0.1 provides the most optimal performance.

A comparative analysis of predictive models for estimating the formability of stamped titanium bipolar plates for proton exchange membrane fuel cells

The microchannels on the bipolar plates (BPPs) of a proton exchange membrane fuel cell (PEMFC) have various functions. Increasing the microchannel depth leads to an increase in the efficiency of the fuel cell. In this paper, metallic BPPs of the PEMFC made of commercial pure titanium (CP-Ti) with an initial thickness of 0.1 mm are manufactured using the stamping process. First, after performing the process using experimental tests and finite elements (FE) simulation, the accuracy of the numerical results is confirmed. In the following, using the FE model and considering process parameters including die clearance, forming speed, and sheet/die friction coefficient, a set of experiments is implemented using the response surface method (RSM). The results of the filling rate are obtained from the experiments and a regression model is established to predict the filling rate of the microchannel. Then, four meta-heuristics methods including artificial bee colony optimization (ABCO), genetic algorithm (GA), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are used to estimate the formability of the CP-Ti BPPs. According to the results, the ANFIS model with an error of 0.0062 × 10−3 % compared to experiments is more reliable to predict the filling rate in the stamping of the CP-Ti BPPs.

Hybrid method for analyzing air thermal conditions in underground mines

Thermal prediction of underground mine air is required to develop control measures against heat problems. Researchers have extensively studied this topic using numerical simulations; however, these require long processing times. Recently, researchers have begun using artificial intelligence algorithms; however, the predictive capabilities of the model are still limited because an intelligent system is formed from field measurement data. This study presents a fast and accurate prediction of the Wet Bulb Globe Temperature (WBGT) in underground mines using a hybrid integrated numerical method and an adaptive neuro fuzzy inference system (ANFIS) method. The mine air thermal conditions in various scenarios were analyzed by numerical simulation, and the results were utilized to develop intelligence for ANFIS model-based predictors. A case study was conducted in two underground gold mine areas to demonstrate the effectiveness of this method. The ANFIS model was trained and tested with 81 scenarios generated from numerical simulations. Accurate predictors were obtained, with a coefficient of determination (R2) of 0.98 and 0.97. In addition to predicting the WBGT, the developed ANFIS model optimized the selection of the auxiliary fan power, minimizing the power consumption while simultaneously providing a comfortable WBGT.

Ultrasound assisted phytochemical extraction of red cabbage by using deep eutectic solvent: Modelling using ANFIS and optimization by genetic algorithms

The present investigation studied the effect of process parameters on the extraction of phytochemicals from red cabbage by the application of ultrasonication and temperature. The solvent selected for the study was deep eutectic solvent (DES) prepared by choline chloride and citric acid. The ultrasound assisted extraction process was modeled using adaptive neuro-fuzzy inference system (ANFIS) algorithm and integrated with the genetic algorithm for optimization purposes. The independent variables that influenced the responses (total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content) were ultrasonication power, temperature, molar ratio of DES, and water content of DES. Each ANFIS model was formed by the training of three Gaussian-type membership functions (MF) for each input, trained by a hybrid algorithm with 500 epochs and linear type MF for output MF. The ANFIS model predicted each response close to the experimental data which is evident by the statistical parameters (R2>0.953 and RMSE <1.165). The integrated hybrid ANFIS-GA algorithm predicted the optimized condition for the process parameters of ultrasound assisted extraction of phytochemicals from red cabbage was found to be 252.114 W for ultrasonication power, 52.715 °C of temperature, 2.0677:1 of molar ratio of DES and 25.947 % of water content in DES solvent with maximum extraction content of responses, with fitness value 3.352. The relative deviation between the experimental and ANFIS predicted values for total phenolic content, antioxidant activity, total anthocyanin activity, and total flavonoid content was found to be 1.849 %, 3.495 %, 2.801 %, and 4.661 % respectively.

A novel approach for precipitation modeling using artificial intelligence-based ensemble models

Precipitation is the primary component of the hydrologic water cycle and its accurate prediction plays a significant role for the planning, management and design of hydraulic structures. The objective of the study intended to explore a new approach to increase prediction efficiency of precipitation in the arid, semiarid and humid zones. The approach was implemented using relevant data from stations in Iraq and Nigeria. Support vector regression (SVR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system models (ANFIS) were applied for a single modeling of 30 years monthly average precipitation data. Thereafter, a nonlinear ensemble and 2 linear ensemble techniques were applied to improve prediction efficiency and reliability of the single models. Error measures as well as a goodness of fit measure were the criteria employed to assess the performance potential of the models. Based on the results of this work it was found that ensemble modeling could improve prediction accuracy of the single models as much as 38% in the validation phase. It was also confirmed that the performance of artificial intelligence-based models can efficiently be improved by the application of ensemble modeling in the arid, semiarid and humid study stations of Iraq and Nigeria.

Amaranthus hybridus waste solid biofuel: comparative and machine learning studies.

The diminishing supply of fossil fuels, their detrimental environmental effects, and the challenges associated with the disposal of agro-waste necessitated the development of renewable and sustainable alternative energy sources. This study aims at developing bio-briquettes from Amaranthus hybridus waste, with cassava starch as a binder; both are agricultural wastes. Before and following delignification, alkali-treated Amaranthus hybridus (TAHB) and untreated (UAHB) briquettes were evaluated in terms of combustion and physicochemical parameters. FTIR and SEM were utilized to monitor the morphological transformation and bond restructuring of TAHB and UAHB samples. EDXRF was used to assess the Potential Toxic Elements (PTEs) composition and environmental friendliness of both TAHB and UAHB. Furthermore, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy c-means (FCM) clustering machine learning models were used to optimize the production process and predict the efficiency of bio-briquettes. After delignification, a lower lignin value of 11.47 ± 0.00% in TAHB compared to 12.31 ± 0.01% (UAHB) was recorded. Calorific values of 10.43 ± 0.25 MJ kg-1 (UAHB) and 12.53 ± 0.30 MJ kg-1 (TAHB) were recorded at p < 0.05. EDXRF results showed a difference of 0.016% in Pb concentration in both samples. SEM reveals morphological restructuring, while FTIR reveals a 4 cm-1 difference in the C-O stretch. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) gave values of 0.0249, 2.104, and, 0.0249; (MAE, training) and 0.0223 (MAE, testing) respectively. This shows that the model's predictions match the reality, thereby suggesting a strong agreement between the predicted and experimental data. The finding of this study shows that delignification-disruption improved the solid biofuel's ability to burn cleanly and sustainably.

Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System for Predicting Financial Crisis

Financial technology (Fintech) is paramount in driving advanced technologies, economies, society, and several other sectors. Smart Fintech is the new-generation Fintech, primarily stimulated and endowed by compuational technology. Smart Fintech syndicates DSAI and renovates economies and finance for dynamic, smart, customized, automated services and systems, economies and financial companies, and the industry. The strength and development of the country’s economies are assessed by the correct forecasting. Financial crisis prediction (FCP) has the substantial consequence on the economies. Previous studies mainly emphasise statistical, DL, and ML methodologies for predicting the financial well-being of the business. Therefore, this article develops a new Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System (AOA-ANFIS) technique for Predicting Financial Crisis. The presented AOA-ANFIS technique aims to predict the presence of financial crises or not. The model incorporates three major elements: Arithmetic Optimization Algorithm (AOA) for feature selection, Adaptive Neuro-Fuzzy Inference System (ANFIS) as the classification algorithm, and Bat Optimization Algorithm (BOA) for parameter tuning. The AOA feature selection model effectively detects the important attributes from a large proportion of financial indicators, augmenting the model's prediction capability while decreasing computational difficulty. Subsequently, the ANFIS classifier exploits the features selected for capturing the intricate non-linear relations intrinsic in financial data, permitting accurate crisis calculation. Additionally, the BOA parameter tuning model augments the ANFIS model's parameters, ensuring robustness and optimum performance. Experimental outcomes on varied financial databases validate the higher efficiency of the AOA-ANFIS technique over underlying processes, demonstrating its effectiveness in forecasting financial crises with great reliability and precision.

WHALE SWARM OPTIMIZATION BASED ANFIS FOR PREDICTION IN FORECASTING APPLICATION

This paper proposes a novel approach for solar power forecasting using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Whale Swarm Optimization (WSO). The synergy between ANFIS and WSO aims to overcome the limitations of traditional forecasting models by controlling the collective intelligence of a whale-inspired swarm algorithm. The WSO optimizes the parameters of the ANFIS, leading to improved accuracy in solar power predictions. The integration of WSO introduces a parallelism and exploration-exploitation balance inspired by the natural behaviors of whales, enhancing the ANFIS model’s adaptability to dynamic solar power generation patterns. The experimental results demonstrate the superiority of the proposed Whale Swarm-based ANFIS over conventional methods, showcasing its ability to handle non-linear and complex relationships in solar power data. This research contributes to the field of renewable energy forecasting by presenting an innovative hybrid model that leverages the strengths of both ANFIS and WSO for more reliable and precise solar power predictions.

Machine learning techniques for the prediction of polymerization kinetics and polymer properties

AbstractIn the current study, the ability of two data‐driven machine learning tools, the extreme learning machine (ELM) and the adaptive neuro‐fuzzy inference system (ANFIS), to predict the polymerization rate and melt flow index of linear low‐density polyethylene produced in a gas phase process was investigated. The level of interaction between the input variables (ethylene, 1‐butene, isopentane pressures, and reaction temperature) on the outputs (melt flow index and activity) was also examined. It was found that both outputs are impacted by the presence of isopentane as an induced condensing agent. Various statistical indicators, including the coefficient of correlation (R2) and root mean square error (RMSE), were used to quantitatively evaluate both developed models. The ANFIS model outperformed the developed ELM model in terms of predicting the MFI and the catalyst activity. A sensitivity analysis of the ANFIS and ELM models showed that all the input variables under investigation had a sizable impact on the responses and none of them could have been discarded. The present study showed that machine learning tools could be employed to adequately develop empirical models to predict polymerization kinetics as well as the final polymer properties.

An Intelligent Adaptive Neuro-Fuzzy for Solving the Multipath Congestion in Internet of Things

The Internet of Things (IoT) has recently become a significant focus in research circles. IoT facilitates the integration of numerous physical entities with the Internet. Adhering to a standardized structure is imperative to manage the vast amount of information effectively. Although many researchers in the field of IoT have proposed various layered architectural designs, none have yet fulfilled all the requisite architectural criteria. Network congestion occurs when the volume of data packet traffic surpasses the network's handling capacity. Apart from addressing congestion issues, it is crucial to harmonize network resources like energy, bandwidth, and latency. The Quality of Service (QoS) in IoT applications chiefly depends on proficient congestion management, which is the central subject of this research. The research employs the Adaptive Neuro-Fuzzy Inference System (ANFIS) to regulate congestion, while the Membership Function (MF) undergoes adjustments through the application of the Modified Squirrel Search Algorithm (MSSA). This ANFIS amalgamates the advantages of Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to form a unique framework. Utilizing ANFIS, adaptive analysis services are available to interpret complex patterns and nonlinear interactions, featuring quick learning capabilities. The MSSA aids in tweaking the Membership Function within the ANFIS model, achieving a successful global convergence rate. An adaptive method considering predator presence probability is employed to harmonize the algorithm's exploration and exploitation functionalities, further bolstered by a dimensional search approach. The simulation results demonstrate that the proposed Swarm Intelligence Adaptive Neuro-Fuzzy Inference System (SI-ANFIS) method significantly reduced traffic overhead and attained an impressive accuracy rate of 93.58%.

Micro-ECDM Performances Analysis Using Fuzzy Logic and ANFIS During Micro-Channel Fabrication on Silica Glass

Higher accuracy and meticulousness are highly demandable in modern industrial field during micro-machining performances by electrochemical discharge machining (ECDM) process. The paper deals with the experimental fuzzy logic control (FLC) analysis as well as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) analysis during micro-channel fabrication on silica glass. A comparative analysis of FLC, ANN as well as ANFIS has been performed and experimental error prediction has been propounded to estimate minimum error possibilities such as mean absolute error (MAE), root mean square error (RMSE) and regression value (R). Computational time training dataset, minimum sample size and prediction time are also illustrated in this paper. In this paper, ANN, FLC and ANFIS models are analyzed for tool wear rate (TWR) and heat-affected zone (HAZ). 3D Rule Viewer and regression analysis as well as validation of test results and characteristics graph between performances with number of epochs of ANN model also are included in this paper for TWR and HAZ. Influence of process parameters like voltage, duty ratio, pulse frequency and electrolyte concentration on Surface Viewer of TWR and HAZ is also illustrated. It is found that ANFIS has great effectiveness of prediction of error during micro-ECDM process.

Handling uncertainty issue in software defect prediction utilizing a hybrid of ANFIS and turbulent flow of water optimization algorithm

During the development cycle of software projects, numerous defects and challenges have been identified, leading to prolonged project durations and escalated costs. As a result, both product delivery and defect tracking have become increasingly complex, expensive, and time-consuming. Recognizing the challenge of identifying every software defect, it is crucial to foresee potential consequences and strive for the production of high-quality products. The goal of software defect prediction (SDP) is to identify problematic locations within software code. This study presents the first experimental investigation utilizing the turbulent flow of water optimization (TFWO) in conjunction with the adaptive neuro-fuzzy inference system (ANFIS) to enhance SDP. The TFWO_ANFIS model is designed to address the uncertainties present in software features and predict defects with feasible accuracy. Data are divided randomly at the beginning of the model into training and testing sets to avoid the local optima and over-fitting issues. By applying the TFWO approach, it adjusts the ANFIS parameters during the SDP process. The proposed model, TFWO_ANFIS, outperforms other optimization algorithms commonly used in SDP, such as particle swarm optimization (PSO), gray wolf optimization (GWO), differential evolution (DE), ant colony optimization (ACO), standard ANFIS, and genetic algorithm (GA). This superiority is demonstrated through various evaluation metrics for four datasets, including standard deviation (SD) scores (0.3307, 0.2885, 0.3205, and 0.2929), mean square error (MSE) scores (0.1091, 0.0770, 0.1026, and 0.0850), root-mean-square error (RMSE) scores (0.3303, 0.2776, 0.3203, and 0.2926), mean bias error (MBE) scores (0.1281, 0.0860, 0.0931, and 0.2310), and accuracy scores (87.3%, 90.2%, 85.8%, and 89.2%), respectively, for the datasets KC2, PC3, KC1, and PC4. These datasets with different instances and features are obtained from an open platform called OPENML. Additionally, multiple evaluation metrics such as precision, sensitivity, confusion matrices, and specificity are employed to assess the model’s performance.

Adaptive Neuro Fuzzy Inference System based classifier in diagnosis of breast cancer

Millions of individuals live longer and healthier lives due to modern medical innovations. The advancement of modern medicine to combat numerous ailments has benefited humanity a lot. Breast cancer is one of the most fatal diseases affecting women globally. Early detection is essential for lowering the fatality rate. The objective of the paper is to create a computer-aided diagnostic model that helps in the early detection of breast cancer and hence decreases the death rate. The study introduces a hybrid strategy for effectively diagnosing breast cancer by using a Novel Relief algorithm for feature selection with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The efficiency of this proposed hybrid model and the ANFIS model without using any feature selection technique is estimated using the Wisconsin Breast Cancer Data set (WBCD). The study finds that the new hybrid model has attained the highest accuracy of 99.30% and is ideal for detecting breast cancer.

Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models

While predicting parking occupancy is crucial for managing urban congestion, existing models often exhibit gaps in accuracy, uncertainty handling, and integration potential. This study introduces an innovative combination of adaptive neuro-fuzzy inference system (ANFIS) and deep learning (DL) techniques to address these shortcomings. Specifically, ANFIS is utilized for its proficiency in uncertainty representation via fuzzy set theory, whereas DL models excel in automatic feature learning, non-linear modeling, and identifying long-term dependencies in time-series parking data. By integrating ANFIS with recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), we formulated the ANFIS-RNN, ANFIS-LSTM, and ANFIS-GRU fusion models, testing them on real-world parking datasets. Subsequent experiments highlighted the dominance of these fusion models over individual and benchmark counterparts. ANFIS-RNN achieved a 30.61% improvement in MSE, 16.70% in RMSE, 21.21% in MAE, 21.58% in MAPE, and a 1.03% elevation in R2 over the standalone RNN. The ANFIS-LSTM surpassed LSTM by 34.04% in MSE, 18.76% in RMSE, 26.16% in MAE, 27.71% in MAPE, with a 1.04% R2 increment. ANFIS-GRU exceeded GRU metrics by 27.54% in MSE, 14.85% in RMSE, 19.27% in MAE, 20.01% in MAPE, and boosted R2 by 1.03%. These outcomes underline the potential of integrated models in refining prediction precision. By leveraging the combined strengths of ANFIS and DL, this research offers a significant leap in parking occupancy forecasting. Its implications extend to data-centric urban planning and traffic regulation, marking a pivotal step for future endeavors in hybrid predictive modeling incorporating soft computing and deep learning paradigms.

Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms

The bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.

Cancerous cell viability affected by synergism between electric pulses and a low dose of silver nanoparticle: An adaptive neuro-fuzzy inference system

In the current study, applying simultaneously electroporation and silver nanoparticles (SNPs) are considered. Moreover, one restriction normally assigned to such nanoparticles is their side effects on the vital organs of the body. To mitigate such deleterious effects, it is better to use lower dosages of them. However, this can result in a decline in the technique's effectiveness. To compensate for the lower dose of SNPs, one can use secondary method like electroporation to deliver SNPs directly into the cells and reinforces the effect of electric pulses due to the high electrical conductivity of SNPs while having a minimal cytotoxicity effect on normal cells that are not treated with electroporation. In the present study, synergism effects of both procedures (SNPs and electroporation) experimentally and theoretically are considered to investigate the property of each technique in increasing the performance with respect to both procedures' limitations. To investigate more, adaptive neuro-fuzzy inference system (ANFIS) is used to predict the percent cell viability of cancerous cells affected by both procedures by considering amplitude and duration as inputs affecting on change of cell viability as an output. The results obtained from both experimental and simulation procedures showed that the maximum synergism between nanoparticles and electric pulses was recorded at 700 ​V/cm strength and 100 μs duration. Also, Results indicated high correlation between observed and predicted data (r2 ​= ​0.88). Moreover, the calculated root mean square error for the results of the ANFIS model was equal to 1.1. This implies that the model has practical value and can estimate the percent cell viability of cancerous cells influenced by both procedures with varying electric field amplitude and duration. This method can be proposed for other biophysical or drug delivery applications to save time and resources by utilizing the previous experimental data rather than performing more experiments.

High Impedance Fault Detection and Location in the Nigerian 330 kV Transmission Network Using Adaptive Neuro-Fuzzy Inference System

Fundamentally, the occurrence of high impedance fault (HIF) can be established when an energy filled conductor contacts a semiconductor. The occurrence of HIF can be described to be subtle and disastrous as it results to low current signal and a signal voltage arc if undetected and without implementation of protective measures, leads to the damage of the power system equipment and in many cases fire outbreak. In this study, the occurrence of HIF on the south-south region of Nigerian 330kV transmission line was studied with the data utilized obtained from the Nigerian control center (NCC) Osogbo. The data obtained was modeled in simulink and the outcome at normal condition was obtained. The transmission line distance was split into 4 points with the current signal at each point generated in simulink and exported to the Matlab file. The data in Matlab file was split to train, test and validate at 70%, 15% and 15% respectively. The data analysis performed was sent to Adaptive Neuro-fuzzy inference system (ANFIS) with the current signal at normal condition and at 3-phase HIF was used as input data while the split distance was implemented as the target data to the model. The effectiveness of the model in detecting and locating HIF was obtained and analyzed in a comparative plot of the location distance. From the results presented, the highest error deviation for fault location with ANFIS was 10%. Hence, it was concluded that ANFIS model had a satisfactory outcome in HIF detection and as such should be utilized for the HIF detection and location.