Feed Forward Back Propagation Research Articles

FFBP Neural Network Optimized with Woodpecker Mating Algorithm for Dynamic Cluster-based Secure Routing in WSN

Feed Forward Back Propagation Neural Network (FFBPNN) Optimized with Woodpecker Mating Algorithm is proposed in this manuscript for Dynamic Cluster-Based Secure Routing in Wireless Sensor Networks (FFBPNN-WMA-ECHC-WSN). The proposed method contains three different stages: first stage is dynamically clustered sensor network formation based on FFBPNN, second stage is Key generation for data encryption and decryption using Elliptic Curve-based Hill Cipher (ECHC), third stage is Homomorphic encryption scheme, which is used to deliver the aggregated data in-time. Woodpecker Mating Algorithm (WMA) is proposed to optimize the parameter values of FFBPNN. The proposed FFBPNN-WMA ECHC-WSN method was implemented in NS2 tool and its efficiency is evaluated under some metrics, like alive nodes, packet drop, network lifetime, delay, throughput, and energy consumption, Packet Delivery Ratio (PDR). The performance of FFBPNN-WMA-ECHC-WSN method provides lower delay 99.01%, 98.34%, 95.23% and 97.45%, and higher throughput 97.25%, 90.12%, 89.39% and 95.47% compared with existing models, such as EHCERA-SDT-WSN, DSA-ECC-PSO-SDT-WSN, IPECC-PDF-ABC-SDT-WSN and IBFA-LDCSN-BSHHO-SDT-WSN, respectively.

Simultaneous spectrophotometric determination of Co (II) and Co (III) in acidic medium with partial least squares regression and artificial neural networks

This study aims at the application of two chemometric techniques to visible spectra of acetic acid solutions of Co (II) and Co (III) for simultaneous determination thereof. Spectral data of 145 samples in the range of 400–700 nm were used to build the models. Partial least squares regression models were developed for which latent variables were determined using internal cross-validation with a leave-one-out strategy and 3 and 2 latent variables were selected for Co(II) and Co(III) based on root mean square error of cross-validation. For these models, root mean square errors of prediction were 1.16 and 0.536 mM and coefficients of determination were 0.975 and 0.892 for Co (II) and Co (III). As an alternate method, artificial neural networks consisting of three layers, with 10 neurons in hidden layer, were trained to model spectra and concentrations of cobalt species. Levenberg-Marquardt algorithm with feed-forward back-propagation learning resulted root mean square errors of prediction of 0.316 and 0.346 mM for Co (II) and Co (III) respectively and coefficients of determination were 0.996 and 0.988.

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

Advanced prediction of soil shear strength parameters using index properties and artificial neural network approach

This study embarks on developing predictive models for soil shear strength parameters, cohesion (c) and angle of internal friction (ϕ), in Bishoftu town, employing Artificial Neural Networks (ANN). It aims at offering a cost-effective and time-saving alternative to traditional, often expensive, and labor-intensive laboratory methods. The research utilizes soil index properties such as Sand %, Fines %, Liquid Limit, Plastic Limit, and Plasticity Index to construct separate ANN models for c and ϕ. These models use a multi-layer perceptron network with feed-forward back propagation, varying the number of hidden layers to optimize performance. The study's dataset comprises 316 soil test results, encompassing both primary and secondary data, conforming to ASTM Standards. Soil cohesion and internal friction angle were determined using the direct shear box method. The models demonstrated remarkable success in predicting shear strength parameters, evidenced by correlation values of approximately 0.99 for cohesion and 0.98 for internal friction angle, surpassing the capabilities of existing empirical methods. Further examination of the models included comparison with existing correlation techniques and cross-validation using primary soil test data. This validation process confirmed the ANN method's superior accuracy and fit for predicting shear strength parameters over selected empirical methods. This research substantiates the efficiency of ANN in geotechnical engineering, particularly for areas with limited resources for extensive soil testing. It establishes ANN as a powerful, efficient tool for estimating soil shear strength parameters, with significant implications for future planning, design, and construction projects in similar environments.

The effectiveness of multivariate and univariate spectrophotometric techniques for the concurrent estimation of ornidazole and ciprofloxacin HCl in tablet formulation and spiked serum: estimating greenness and whiteness profile

In this manuscript, the effectiveness of multivariate and univariate tools in conjunction with spectrophotometric techniques was evaluated for the concurrent analysis of ciprofloxacin (CI) and ornidazole (OR) in prepared mixtures, tablets, and human serum. The artificial neural network was chosen as the multivariate Technique. Bayesian regularization (trainbr) and Levenberg–Marquardt algorithms (trainlm), were constructed and trained using feed-forward back-propagation learning. The optimal logarithm was determined based on mean recovery, mean square error of prediction (MSEP), relative root mean square error of prediction (RRMSEP), and bias-corrected MSEP (BCMSEP) scores. Trainbr outperformed trainlm, yielding a mean recovery of 100.05% for CI and 99.84% for OR, making it the preferred algorithm. Fourier self-deconvolution and mean-centering transforms were chosen as the univariate Techniques. Fourier self-deconvolution was applied to the zero-order spectra of ciprofloxacin and ornidazole by electing an appropriate full width at half maximum, enhancing peak resolution at 380.1 nm and 314.2 nm for CI and OR, respectively. Mean centering transform was applied to CI and OR ratio spectra to eliminate constant signals, enabling accurate quantification of CI and OR at 272.0 nm and 306.2 nm, respectively. The introduced approaches were optimized and validated for precise CI and OR analysis, with statistical comparison against the HPLC method revealing no notable differences. The sustainability of these approaches was confirmed through the green certificate (modified eco-scale), AGP, and whiteness-evaluation tool, corroborating their ecological viability.

NEURAL NETWORK BASED ESTIMATION OF ECOLOGICAL FOOTPRINT IN TURKEY WITH ECONOMIC AND ENVIRONMENTAL FACTORS

The globe is currently confronted with escalating ecological challenges as a result of the exponential growth of the population, the expansion of industrial activities, the rapid urbanization process, and the heightened levels of consumption. Due to the prevailing ecological issues, there has been an unbridled surge in the demand for natural resources. The emergence of the Ecological Footprint was driven by the observable escalation of environmental degradation, with the primary objective of promoting sustainability. In this study, an Artificial Neural Network (ANN) model was developed for the estimation of ecological footprint. In the first stage, the variables affecting the ecological footprint were determined. It was concluded that there are strong relationships between the independent variables of Gross Domestic Product (GDP), KOF Globalization Index (KOFGI), and Natural Resource Rent (NRR) and the dependent variable of ecological footprint. In the light of this information, the data of GDP (% annual increase), NRR (% GDP) and KOF Globalization Index and ecological footprint data of Turkey between 1970 and 2016 were used to present the ANN model. Feed-Forward Backprop Method, which is one of the multi-layer network models, was applied in the modeling of the ANN. Levenberg-Marquardt optimization, which updates the weight and bias values of the network, was used as the network training function. The study's results suggest that there was a strong correlation between the whole dataset and the model's accuracy, with a close match of 99.316%. Based on the findings, it can be deduced that the developed artificial neural network (ANN) model has a significant level of precision in forecasting the Ecological Footprint.

Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.

Prediction of quality characteristics of micro-channels by deep learning algorithm during laser transmission ablation of PMMA

Underwater laser transmission micro-channelling of thick transparent polymethyl methacrylate (PMMA) using a neodymium-doped yttrium aluminium garnet (Nd: YAG) laser is the focus of this study. Experiments are conducted to determine the effect of three significant variables, namely pulse frequency, lamp current, and cutting speed, on heat-affected zone (HAZ) width, cut depth, and kerf width. Experiments are designed systematically utilising the full factorial design method. The purpose of constructing empirical models, specifically non-linear ones, is to establish and clarify the relationship between control factors and corresponding responses. A feed forward backpropagation neural network (FF-BPNN) is employed for predicting the measured response. Additionally, an assessment is made between the FF-BPNN results and the values predicted by the model. On the basis of the anticipated results, it can be concluded that the FF-BPNN model is preferable in predicting both the depth of cut and kerf width. In contrast, when specifically contemplating the measurement of HAZ width, the application of a non-linear model is more advantageous. As a measure of accuracy, the mean absolute error is computed for both the feedforward backpropagation neural network (FF-BPNN) and the nonlinear modelling technique.

Biogas production by integrating lava rock, red clay & ceramic bio ring as support carrier in treatment of landfill leachate with liquidised food waste

To evaluate the feasibility of co-digestion of landfill leachate and liquidised food waste, batch experiments were conducted to explore the optimal mixing ratio and support carrier integration in up-flow anaerobic sludge blanket reactors. Continuous experiments were conducted to evaluate the long-term effects of support carriers on methane generation and digestate characteristics. To compare biogas accuracy, Biomethane Potential Tests 1 and 2 data were used to build artificial neural networks using feed-forward backpropagation and support vector machines using linear function. Red clay bio ring, ceramic bio ring and lava rock support carriers were used in the modified reactors. Significant improvement in the anaerobic process was observed during the co-digestion, along with the addition of a ceramic bio ring with biogas production (2150 mL/d), specific methane production (293.30 mL CH4/gCODadded, >66.10% compared to the conventional reactor), and chemical oxygen demand removal (>80%) at an organic loading rate of 6 g/L/day. The Brunauer-Emmett-Teller method demonstrates that ceramic bio ring has a higher specific surface area (7.87 m2/g). The support vector machine model had superior predicted accuracy in biogas production with an R2 of 0.986, indicating it is more robust for large-scale applications. Thus, utilising a suitable proportion of substrates and selecting an appropriate support carrier can stimulate methanogenic activity, which can boost methane production, a promising renewable energy source, and produce higher-quality effluent.

Synergism of Artificial Intelligence and Techno-Economic for Sustainable Treatment of Methylene Blue Dye-Containing Wastewater by Photocatalysis

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.

Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application

Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg–Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.

Prediction and optimization of Nd: YAG laser transmission micro-channelling on PMMA employing an artificial neural network model

In the present research work, performance enhancement which plays pivotal role in the modern machining techniques during machining of advanced engineering materials has been attributed. An investigation is conducted on the laser transmission micro-channeling of thick transparent PMMA using Nd: YAG laser. The objective is to examine the impact of three key factors, namely pulse frequency, lamp current and cutting speed, on several quality aspects including depth of cut, kerf width, and heat-affected zone (HAZ) width. General full factorial design is used to design the experiments and empirical models (non-linear) are developed to establish the relationship between the control factors and responses. A feed forward back-propagation neural network (FF-BPNN) is used to model the process and subsequently to predict the responses. The successful capability of FF-BPNN in predicting and enhancing the characteristics of micro-channels fabricated on PMMA has been observed. Furthermore, it has been shown that feedforward backpropagation neural networks (FF-BPNN) can serve as a highly effective tool for acquiring a comprehensive model and determining the ideal configuration of process parameters in laser transmission micro-channeling operations. Additionally, FF-BPNN results and model predicted values are compared with non-linear modelling technique. Finally, mean absolute error is calculated to find out the accuracy of both the techniques.

Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model

Uncontrolled development at the upstream of the river catchment have led to detrimental effect to the environment, including degradation of river water quality. River Water Treatment Plant (RWTP) technology was introduced to reduce the contamination loading into the river water system, worldwide. The technology offers the best biological treatment process including simplicity and stable removal efficiency. However, the plant performance plan is difficult task to predict, thus might have influence the operational control. Recently, artificial neural network (ANN) models have been widely applied in environmental engineering area due to the ability to skip the complexity process to assume of the unknown variables compare to conventional physical based model. In this study, the results of 3-yrs performance using ANN of RWTP were developed. Feed-forward back-propagation using Levenberg–Marquardt (trainlm) used as for this predictive approach. The ideal configuration involves utilizing the tangent sigmoid transfer function (Tansig) in the hidden layer and a linear transfer function (Purelin) in the output layer, with 25 neurons. This configuration yields an R2 value of 0.963 and the most least mean square error (MSE) of 30.39. From the comparison between two model (bio-kinetic and ANN), performance indicator for ANN model shows the best and the most optimum model. Ultimately, RWTP optimization using black-box model ANN is more reliable and timesaving as compared to conventional bio-kinetic model. The development of the proposed model can be implemented and used for various water quality improvement facilities and predict the effluent target parameter in RWTP with higher degree of accuracy.

Improved Air Quality Forecasting-Based on Machine Learning and Multivariate Regression Techniques

Hourly-based improved prediction of present air quality state (PM10, PM2.5, and NO2) with 15 input independent variables (3 hours\' earlier PM, gas and meteorological data of a Korean city affected by 2 days\' earlier PM and gas data of Beijing city in the Yellow Dust route was performed using Machine Learning (ANN-Tanh) and Multivariate Regression techniques. ANN-tanh model of multilayer perception (MLP) structure with a feed-forward input data and backpropagation training process for error calculation with 15 nodes in a single hidden layer was adopted to calculate present urban air quality. Root mean square error (RMSE) and the coefficient of determination (R2; Pearson R) evaluated the performance ability of the model between the predicted and measured values before, during, and after the Yellow Sand event at the Korean coastal city. Multivariate regression technique was also used to predict the urban air quality with the same input data. The prediction accuracies of the two models were compared before, during, and after the Yellow Dust period and regardless of the event, Pearson R correlation coefficients using the ANN-tanh model (multivariate regression model) were quite high as 0.935 (0.961), 0.943 (0.948) and 0. 947 (0.920) for PM10, 0.942 (0.909), 0.969 (0.977) and 0.938 (0.947) for PM2.5, and 0.925 (0.860), 0.853 (0.875) and 0.886 (0.903) for NO2, respectively. Their prediction ability overall was quite excellent, showing a slightly higher R in the ANN-Tanh. Their hourly distributions of predicted and measured values of 3 outputs were drawn to compare their model performance.

Smart solutions for dissolved oxygen control in semi-batch fermenters: A machine learning approach

The monitoring and measurement of dissolved oxygen (DO), plays a significant role in industrial effluent, and disposal of effluents has been a serious challenge to researchers. Aeration is the primary step in effluent treatment. The basic method for determining DO is direct measurement. This study has been carried out in effluent from the paper industry to estimate the performance of dissolved oxygen in a semi-batch lark hygiene fermenter and was oxygenated till saturation at 25 °C under real-time study. This study has been carried out using Multi-Layered Feed Forward Back Propagation Artificial Neural Network (ML-FFBPANN) and the experimental results were optimized with fourteen different machine learning algorithms such as Polak-Ribiere Conjugate Gradient (CGP), Conjugate Gradient with Powell/Beale Restarts (CGB), Bayesian Regularization (BR), Gradient Descent (GD), BFGS Quasi-Newton (BFG), GD with Momentum (GDM), Gaussian Discriminate Analysis(GDA), Fletcher-Powell Conjugate Gradient (CGF), Resilient Backpropagation (RP), Variable Learning Rate Gradient Descent (GDX), One Step Secant (OSS), Regression (R), Levenberg-Marquardt (LM), Scaled Conjugate Gradient(SCG). Parameters Root Mean Squared Error (RMSE) and the correlation coefficient (R-square) were used to evaluate the performance of each model and mathematical modeling for correlation co-efficient of all algorithms are also presented. LM and R are the best algorithms but the ML-FFBPANN-LM algorithm provided the best optimization results of maximum correlation co-efficient. LM algorithm produced a maximum correlation coefficient under training (0.99818), testing (0.99351), valediction (1.0), and an overall correlation coefficient be 0.9568. From the study, it can be concluded that the ML-FFBPANN with LM algorithm can be used in the aeration process in effluent treatment for analysis and prediction of process parameters.

ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing

The ever-changing environmental conditions and pollution are the prime reasons for the onset of several emerging and re-merging diseases. This demands the faster designing of new drugs to curb the deadly diseases in less waiting time to cure the animals and humans. Drug molecules interact with only protein surface on specific locations termed as ligand binding sites (LBS). Therefore, the knowledge of LBS is required for rational drug designing. Existing geometrical LBS prediction methods rely on search of cavities based on the fact that 83% of the LBS found in deep cavities, however, these methods usually fail where LBS localize outside deep cavities. To overcome this challenge, the present work provides an artificial neural network (ANN) based method to predict LBS outside deep cavities in animal proteins including human to facilitate drug designing. In the present work a feed-forward backpropagation neural network was trained by utilizing 38 structural, atomic, physiochemical, and evolutionary discriminant features of LBS and non-LBS residues localized in the extracted roughest patch on protein surface. The performance of this ANN based prediction method was found 76% better for those proteins where cavity subspace (extracted by MetaPocket 2.0, a consensus method) failed to predict LBS due to their localization outside the deep cavities. The prediction of LBS outside deep cavities will facilitate in drug designing for the proteins where it is not possible due to lack of LBS information as the geometrical LBS prediction methods rely on extraction of deep cavities.

Machine learning approach of Casson hybrid nanofluid flow over a heated stretching surface

<abstract> <p>The present investigation focused on the influence of magnetohydrodynamic Gold-Fe<sub>3</sub>O<sub>4</sub> hybrid nanofluid flow over a stretching surface in the presence of a porous medium and linear thermal radiation. This article demonstrates a novel method for implementing an intelligent computational solution by using a multilayer perception (MLP) feed-forward back-propagation artificial neural network (ANN) controlled by the Levenberg-Marquard algorithm. We trained, tested, and validated the ANN model using the obtained data. In this model, we used blood as the base fluid along with Gold-Fe<sub>3</sub>O<sub>4</sub> nanoparticles. By using the suitable self-similarity variables, the partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs). After that, the dimensionless equations were solved by using the MATLAB solver in the Fehlberg method, such as those involving velocity, energy, skin friction coefficient, heat transfer rates and other variables. The goals of the ANN model included data selection, network construction, network training, and performance assessment using the mean square error indicator. The influence of key factors on fluid transport properties is presented via tables and graphs. The velocity profile decreased for higher values of the magnetic field parameter and we noticed an increasing tendency in the temperature profile. This type of theoretical investigation is a necessary aspect of the biomedical field and many engineering sectors.</p> </abstract>

Precipitation Forecast with Artificial Neural Networks Method

Events in the atmosphere from past to present – wind, precipitation, humidity, temperature – have almost always been the subject of research to create a forecast in regions. The rapid development of the technological field in terms of software and hardware brings methods and techniques to be used in research. One of them is Artificial Neural Networks. In this study, precipitation data were estimated using the Feed Forward Backpropagation method of Artificial Neural Networks method using past data of meteorological parameters, and they were compared with the data of multiple linear regression analysis. Based on these models, six different models were studied, and regression and performance evaluations were made. While the error average of multiple linear regression is 0.2413, this value is 0.076 in artificial neural networks, and the correlation average for both is 0.90. As a result of this study, the best model has a coefficient of determination of 0.95 and an error value of 0.18 in multiple linear regression, as well as a coefficient of certainty of 0.99 and an error value of 0.0438 in artificial neural networks; It has been understood that the 1st model, which has 6 data sets as the input layer, exhibits the best performance.

Yapay Sinir Ağları ile Güneş Enerjisi Santralinin Modellenmesi

This study will enable us to estimate the power of the solar power plant with measurement data such as outdoor temperature-humidity, wind and precipitation amount, to protect the system from imbalance, and to determine the instant and daily effective energy trade more easily. By taking the data from the solar power plant installed in Samsun, Turkey, estimation was made with Artificial Neural Networks for electricity generation. In this study, Levenberg-Marguardt feed-forward backprop learning algorithm was used to find the best approach in the network. The best prediction results were obtained from the 2-layer and 5-neuron Artificial Neural Networks model, and it was observed that the system gave better training results as the number of iterations increased (multiple determination coefficient, R2, 0.99818).

Predicting intrauterine growth restriction: a pilot study with feed-forward back propagation network

Predicting intrauterine growth restriction: a pilot study with feed-forward back propagation network