Search Results (102)

The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R²), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R² for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article

Fissured expansive soils exhibit pronounced moisture-induced swelling, posing significant risks to the stability of geotechnical structures such as dam foundations and core zones. To improve predictive capacity in such environments, this study developed a back-propagation (BP) neural network model to estimate the swelling behavior of fissured expansive soils. The model incorporated four key geotechnical parameters—fissure ratio, dry density, initial moisture content, and overburden pressure—and was implemented in MATLAB using a three-layer feedforward architecture with four inputs, five hidden neurons, and a single output neuron to predict the swelling ratio (increase in specimen height due to water-induced expansion). The model was trained on 81 laboratory-tested samples, with all variables normalized to the range [−1, 1] to ensure numerical stability. Two training algorithms were evaluated: gradient descent with momentum (traingdm) and the Fletcher–Reeves conjugate gradient method (traincgf). The optimal network configuration achieved a mean squared error (MSE) below 0.01, indicating strong predictive accuracy for expansive soil swelling behavior. Comparative results showed that the conjugate gradient algorithm converged nearly 30 times faster than the gradient descent method, while maintaining similar prediction accuracy. Validation on an independent dataset confirmed high agreement with measured swelling ratios. The proposed BP model demonstrates robust generalization and computational efficiency, offering a practical decision-support tool for expansive soil deformation control in dam engineering. Its rapid and accurate predictions make it valuable for Smart City applications such as embankment stabilization, intelligent dam core design, and real-time geotechnical risk assessment. Full article

Population growth, industrialization, excessive energy consumption, and deforestation have led to climate change and affected water resources like dams intended for public drinking water. Meteorological parameters could be used to understand these effects better to anticipate the water quality of the dam. Artificial neural networks (ANNs) are favored in hydrology due to their accuracy and robustness. This study modeled climatic effects on the water quality of Doğancı dam using a feed-forward neural network with one input, one hidden, and one output layer. Three models were tested using various combinations of meteorological data as input and Doğancı dam’s water quality data as output. Model success was determined by the mean squared error and correlation coefficient (R) between the observed and predicted data. Resilient back-propagation and Levenberg–Marquardt were tested for each model to find an appropriate training algorithm. The model with the least error (1.12–1.68) and highest R value (0.93–0.99) used three meteorological inputs (air temperature, global solar radiation, and solar intensity), six water quality parameters of Doğancı dam as output (water temperature, pH, dissolved oxygen, manganese, arsenic, and iron concentrations), and ten hidden nodes. The two training algorithms employed in this study did not differ statistically (p > 0.05). However, the Levenberg–Marquardt training approach demonstrated a slight advantage over the resilient back-propagation algorithm by achieving reduced error and higher correlation in most of the models tested in this study. Also, better convergence and faster training with a lesser gradient value were noted for the LM algorithm. It was concluded that ANNs could predict a dam’s water quality using meteorological data, making it a useful tool for climatological water quality management and contributing to sustainable water resource planning. Full article

The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R²) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact. Full article

Heart failure, a chronic and progressive condition where the heart muscle fails to pump sufficient blood for the body’s needs, leads to complications like irregular heartbeat and organ damage. It is a leading cause of death worldwide, with 17.9 million annual fatalities. Often diagnosed late due to complex, costly screenings, current treatments are less effective at advanced stages, necessitating novel early detection methods. This research develops intelligent systems using a Fuzzy Inference System and Feed Forward Back Propagation Neural Network, focusing on eleven heart-affecting parameters. The study shows artificial intelligence-based models outperform current medical diagnostics in early heart disease detection. The models were evaluated using 221 datasets. The obtained result demonstrates that the performance parameters of the FIS model provide superior results compared to the ANN model. The developed FIS system’s accuracy, precision, sensitivity, and specificity are 90.50%, 90.91%, 90.50%, and 90.31%, respectively. A graphical user interface (GUI) is developed using the MATLAB App Designer tool to facilitate the system’s practical applicability for the end users. Full article

The presented study investigates the effects of weight percentages of boron carbide reinforcement on the wear properties of aluminum alloy composites. Composites were fabricated via ball milling and the hot extrusion process. During the fabrication of composites, B₄C content was varied (0, 5, and 10 wt.%), as well as milling time (0, 10, and 20 h). Microstructural observations with SEM microscopy showed that with an increase in milling time, the distribution of B₄C particles is more homogeneous without agglomerates, and that an increase in wt.% of B₄C results in a more uniform distribution with distinct grain boundaries. Taguchi and ANOVA analyses are applied in order to investigate how parameters like particle content of B₄C, normal load, and milling time affect the wear properties of AA2024-based composites. The ANOVA results showed that the most influential parameters on wear loss and coefficient of friction were the content of B₄C with 51.35% and the normal load with 45.54%, respectively. An artificial neural network was applied for the prediction of wear loss and the coefficient of friction. Two separate networks were developed, both having an architecture of 3-10-1 and a tansig activation function. By comparing the predicted values with the experimental data, it was demonstrated that the well-trained feed-forward-back propagation ANN model is a powerful tool for predicting the wear behavior of Al2024-B₄C composites. The developed models can be used for predicting the properties of Al2024-B₄C composite powders produced with different reinforcement ratios and milling times. Full article

Artificial neural networks (ANNs) are powerful data-oriented “black-box” algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH₃, Ca, Cl, Chl-a, EC, F, CaCO₃, Mg, Mn, NO₃, pH, SO₄, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R²) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained. Full article

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional–integral–derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10⁻⁵ RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments. Full article

The fossil fuel crisis is a major concern across the globe, and fossil fuels are being exhausted day by day. It is essential to promptly change from fossil fuels to renewable energy resources for transportation applications as they make a major contribution to fossil fuel consumption. Among the available energy resources, a fuel cell is the most affordable for transportation applications because of such advantages as moderate operating temperature, high energy density, and scalable size. It is a challenging task to optimize PEMFC operating parameters for the enhancement of performance. This paper provides a detailed study on the optimization of PEMFC operating parameters using a multilayer feed-forward neural network, a genetic algorithm, and the design of a multiport high-gain DC–DC converter for hybrid electric vehicle application, which is capable of handling both a 6 kW PEMFC and an 80 AH 12 V heavy-duty battery. To trace the maximum power from the PEMFC, the most recent SFO-based MPPT control technique is implemented in this research work. Initially, a multilayer feed-forward neural network is trained using a back-propagation algorithm with experimental data. Then, the optimization phase is separately carried out in a neural-power software environment using a genetic algorithm (GA). The simulation study was carried out using the MATLAB/R2022a platform to verify the converter performance along with the SFO-based MPPT controller. To validate the real-time test bench results, a 0.2 kW prototype model was constructed in the laboratory, and the results were verified. Full article

Recently, removing dyes from wastewater by photocatalysis has been extensively studied by several researchers. However, there exists a research gap in optimizing the photocatalytic process parameters using artificial intelligence to maintain the associated techno-economic feasibility. Hence, this investigation attempts to optimize the photocatalytic degradation of methylene blue (MB) dye using an artificial neural network (ANN) model to minimize the capital and running costs, which is beneficial for industrial applications. A ZnO/MgO photocatalyst was synthesized, showing an energy band gap of 2.96 eV, crystallinity index of 71.92%, pore volume of 0.529 cm³/g, surface area of 30.536 m²/g, and multiple surface functional groups. An ANN model, with a 4-8-1 topology, trainlm training function, and feed-forward back-propagation algorithm, succeeded in predicting the MB removal efficiency (R² = 0.946 and mean squared error = 11.2). The ANN-based optimized condition depicted that over 99% of MB could be removed under C₀ = 16.42 mg/L, pH = 9.95, and catalyst dosage = 905 mg/L within 174 min. This optimum condition corresponded to a treatment cost of USD 8.52/m³ cheaper than the price estimated from the unoptimized photocatalytic system by ≈7%. The study outputs revealed positive correlations with the sustainable development goals accompanied by pollution reduction, human health protection, and aquatic species conservation. Full article

Scouring depends on several factors, including the water flow of artificial obstacles, sections, piers, and foundations, the disturbance of bed materials, and soil permeability. The other factors are the non-parallelism between piers and the water flow, the type of river activity (static or dynamic), and the existence of a waterfall or an obstacle that forms a waterfall in natural bed materials, causing the underlying bed materials to be washed away. This study fully investigated how the movement of a tree trunk affects a river’s flow by considering different flow conditions using the artificial neural network (ANN) model. A feedforward optimal network with the error back-propagation training algorithm and sigmoid transfer functions was used for four models. To determine the number of neurons in the hidden layer, one and ten neurons were selected in the hidden layer according to verification indicators. In addition, a physical model was utilized to measure data. To verify and test the models, our data were gathered in a laboratory using the physical model. Considering the network structure of one neuron in the hidden layer, a comparison was made between dimensional and dimensionless parameter models that are effective in terms of the dimensions of the scour hole. The comparison between the results of the ANN and the measured data using nonlinear regression models demonstrated that the ANN was more accurate and capable of simulating phenomena. Additionally, R and RMSE values were between 0.93 and 0.98, as well as 0.18 and 0.013, respectively. Finally, the results related to the width, height, length, and depth of the scour revealed that the modified DOT model had the best agreement with Mahdavizadeh’s measured data. Full article

This current investigation aims to explore the significance of induced magnetic fields and double-diffusive convection in the radiative flow of Carreau nanofluid through three distinct geometries. To simplify the fluid transport equations, appropriate self-similarity variables were employed, converting them into ordinary differential equations. These equations were subsequently solved using the Runge–Kutta–Fehlberg (RKF) method. Through graphical representations like graphs and tables, the study demonstrates how various dynamic factors influence the fluid’s transport characteristics. Additionally, the artificial neural network (ANN) approach is considered an alternative method to handle fluid flow issues, significantly reducing processing time. In this study, a novel intelligent numerical computing approach was adopted, implementing a Levenberg–Marquardt algorithm-based MLP feed-forward back-propagation ANN. Data collection was conducted to evaluate, validate, and guide the artificial neural network model. Throughout all the investigated geometries, both velocity and induced magnetic profiles exhibit a declining trend for higher values of the magnetic parameter. An increase in the Dufour number corresponds to a rise in the nanofluid temperature. The concentration of nanofluid increases with higher values of the Soret number. Similarly, the nanofluid velocity increases with higher velocity slip parameter values, while the fluid temperature exhibits opposite behavior, decreasing with increasing velocity slip parameter values. Full article

Monitoring electricity energy usage can help to reduce power consumption considerably. Among load monitoring techniques, non-intrusive load monitoring (NILM) provides a cost-efficient solution to identify individual load consumption details from the aggregate voltage and current measurements. Existing load monitoring techniques often require large datasets or use complex algorithms to obtain acceptable performance. In this paper, a NILM technique using six non-redundant current waveform features with rule-based set theory (CRuST) is proposed. The architecture consists of an event detection stage followed by preprocessing and framing of the current signal, feature extraction, and finally, the load identification stage. During the event detection stage, a change in connected loads is ascertained using current waveform features. Once an event is detected, the aggregate current is processed and framed to obtain the event-causing load current. From the obtained load current, the six features are extracted. Furthermore, the load identification stage determines the event-causing load, utilizing the features extracted and the appliance model. The results of the CRuST NILM are evaluated using performance metrics for different scenarios, and it is observed to provide more than 96% accuracy for all test cases. The CRuST NILM is also observed to have superior performance compared to the feed-forward back-propagation network model and a few other existing NILM techniques. Full article

Most industrial processes have nonlinear and time-delay characteristics leading to difficulty in prediction modeling. In addition, the working conditions of most industrial processes are complex, which results in multiple modes. Testing and modeling for each mode is a waste of time and resources. Therefore, it is urgent to complete model migration between different modes. In this work, a new prediction model, a nonlinear autoregressive model with exogenous inputs and back propagation neural network (NARX-BP), is proposed for the nonlinear and time-delay processes, where the input data order of the model is determined by the feedforward neural network (FNN) method, and the nonlinear relation is realized by the BP neural network. For the multi-mode characteristic, a new migration optimization algorithm, input–output slope/bias correction and differential evolution (IOSBC-DE), is provided for using a small amount of data under a new mode to correct the slope and bias of the relationship between the input and output variables through DE. The modeling and migration methods are applied to a wind tunnel system, and the simulation result shows the effectiveness of the proposed method. Full article

This research shows the development of a miniaturized solid-phase extraction method with UV-Vis detection for simultaneous determination of dibenzofuran, fluoranthene and phenanthrene using chemometrics approaches. After synthesis of Fe₃O₄ nanoparticles (Fe₃O₄ NPs), the surface of the nanoparticles was modified by Triton X100 coating. The influence of extraction solvent and volume, concentration of Triton X100, extraction time, and sample pH were studied and optimized. Due to high spectral overlapping, resolving ternary mixtures for simultaneous determination of targets with classical analytical methods is impossible. Therefore, the recorded UV-Vis spectra were transformed using continuous wavelet transform and then subjected to artificial neural networks (ANNs). The Db4 mother wavelet was used as the better mother wavelet. For simultaneous detection of analytes, a comparison of feed-forward back-propagation and radial basis function networks was accomplished. The calibration graphs showed linearity in the ranges of 2.4–250 ng mL⁻¹, 50–3750 ng mL⁻¹, and 48–5000 ng mL⁻¹ with a limit of detection of 0.58, 9.5 ng mL⁻¹, and 12.5 ng mL⁻¹ under optimal conditions for phenanthrene, fluoranthene, and dibenzofuran, respectively. The limit of quantitation was achieved at 3.52 ng mL⁻¹, 16.35 ng mL⁻¹, and 31.3 ng mL⁻¹ for phenanthrene, fluoranthene and dibenzofuran, respectively. The method involving TX-100-coated Fe₃O₄ NPs in a liquid sample phase for analyte extraction, followed by ethanol desorption and UV-Vis detection, was successfully applied for the determination of polycyclic aromatic hydrocarbons in oil-field water and algae samples. Full article