Artificial Neural Network Approach Research Articles

Bio-hydrogen production from crude glycerol: Optimisation through response surface methodology and artificial neural network approach

Crude glycerol (CG) is the main by-product of biodiesel production from the transesterification of vegetable or animal oils. The CG's high energy content makes it an effective carbon source for bio-hydrogen production. However, the purity of CG derived from the transesterification process may vary due to impurities such as methanol, soap, salt, etc., which influence the hydrogen production yield. The present study investigated the impacts of initial glycerol, methanol, and soap concentration of CG on the hydrogen production by dark fermentation, and the effect of various factors was evaluated and optimised with the response surface methodology (RSM) and artificial neural network (ANN) approach to maximise the yield of hydrogen production. Both the results of these approaches showed good fitting. The correlation coefficient between actual and predicted values from RSM and ANN models is closer to ∼1, and the lower error shows the applicability of these models. A maximum of 17.67 mmol/L of hydrogen production was obtained via RSM with optimum methanol, soap and glycerol concentrations of 2.75 g/L, 6.07 g/L, and 20.45 g/L, respectively. Besides, the 3-D response surface graphs illustrated the suppressive impact of increased initial concentration of methanol and soap, along with higher initial concentrations of glycerol, on hydrogen production. Further, the outcome of the controlled experiment shows that the presence of impurities (methanol and soap) in glycerol reduces the cumulative hydrogen production by 5 %–17 % compared to the control for over five days.

Can smart supply chain bring agility and resilience for enhanced sustainable business performance?

PurposeSupply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.Design/methodology/approachThe study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.FindingsThe results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.Originality/valueThis study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

Artificial neural networks for model identification and parameter estimation in computational cognitive models.

Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.

The interplay effects of digital technologies, green integration, and green innovation on food supply chain sustainable performance: An organizational information processing theory perspective

Green integration (GIT) and green innovation (GIO) in food supply chains (FSCs) have the potential to attain sustainability with the facilitation of digital technologies (DTs). Nevertheless, few studies have been conducted regarding the effectiveness of DTs and GIT in determining GIO and sustained FSC. Furthermore, an unsustainable food supply chain is a globally recognised problem that needs immediate, multidimensional holistic measures. Therefore, the present study tests the interplay effects of GIT, GIO, and DTs in FSC sustainable performance (FSCSP) under the strategic configuration for information-processing fit, i.e., organizational information processing theory (OIPT) perspective. A combined approach of structural equation modelling (SEM) and artificial neural network (ANN) was used to examine the collected responses from different related industries and validate the robustness of the proposed hypothesis through ANN. The findings demonstrate that the usage of DTs has a favourable impact on GIO and GIT. Furthermore, GI has a positive influence on GIO. Finally, both GIO and GI have a positive impact on FSCSP. Theoretically, under the strategic alignment for information-processing fit, the study's findings suggest that GIT and GIO improve their FSC strategies and practically keep the FSC ahead of the competition for the long haul by ensuring sustainability. It is clear from the results of this research that practitioners should support more DTs to promote GIT, GIO, and FSCSP. This research fills a significant gap in the literature by analyzing the unexplored connections across the FSCSP, GIT, GIO, and DTs as facilitators in the context of sustainability.

Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: Insights using a novel nickel-strontium-zirconium-aluminum catalyst

This study investigates the molecular dynamics of methane dry reforming catalyzed by a novel nickel-strontium-zirconium-aluminum (5Ni+3Sr/10Zr+Al) catalyst, leveraging both Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) for predictive optimization. Focusing on the impact of operational parameters—hourly space velocity, reaction temperature, and CO2:CH4 mole ratio—on the conversion rates and formation of reaction components, we aim to predict optimal conditions and corresponding process variables. Through a comparison of a three-layer Feed Forward Neural Network, optimized at a 3:10:1 topology, with traditional RSM approaches, our findings highlight the superior predictive capabilities of ANN models. Notably, ANN demonstrated exceptional performance with Radj2and F_Ratio values significantly surpass those of RSM, alongside lower statistical error terms. This superiority is attributed to ANN's robust handling of nonlinear relationships between inputs and outputs, asserting its potential for enhancing predictive accuracy in chemical process optimization. At optimum predicted conditions like 1 CH4/CO2,750 °C reaction temperature, 12000 cm3g−1h−1 space velocity, NiSrZrAl outperformed with > 85 % CH4 and CO2 conversion with H2/CO ∼1 up to 20 h time on stream. Our research underscores the importance of integrating advanced modeling techniques for the efficient and accurate prediction of catalytic reactions, offering valuable insights for future applications in chemical engineering and catalysis.

Consumers’ receptivity to mHealth technologies: a hybrid PLS–ANN approach

PurposeMobile health (mHealth) technologies, in particular, have been sought after and advocated as a means of dealing with the pandemic situation. Despite the obvious advantages of mHealth, which include monitoring and exchanging health information via mobile applications, mHealth adoption has yet to take off exponentially. Expanding on the unified theory of acceptance and use of technology (UTAUT) model, this study aims to better comprehend consumers’ receptivity to mHealth even after the pandemic has subsided.Design/methodology/approachThrough purposive sampling, data were collected from a sample of 345 mobile phone users and analysed using partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) capture both linear and nonlinear relationships.FindingsEffort expectancy, performance expectancy, social influence, pandemic fear and trustworthiness positively influenced mHealth adoption intention, with the model demonstrating high predictive power from both the PLSpredict and ANN assessments.Research limitations/implicationsThe importance–performance map analysis (IPMA) results showed that social influence had great importance for mHealth uptake, but demonstrated low performance.Practical implicationsReferrals are an alternative that policymakers and mHealth service providers should think about to increase uptake. Overall, this study provides theoretical and practical insights that contribute to the advancement of digital healthcare, aligning with the pursuit of Sustainable Development Goal 3 (SDG 3) (good health and well-being).Originality/valueThis study has clarified both linear and nonlinear relationships among the factors influencing intentions to adopt mHealth. The findings from both PLS and ANN were juxtaposed, demonstrating consistent findings.

A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem

Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.

Predicting the fire-induced structural performance of steel tube columns filled with SFRC-enhanced concrete: using artificial neural networks approach

Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R2 threshold of 0.90 and achieves impressive R2 values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering.

Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling

Sales predictions on building material products today have applied an artificial neural network approach. One of the products of building material that need to be predicted for sales is polyvinylchloride (PVC) ceilings. Most companies haven’t implementing prediction technique for the sale of PVC ceilings, so this study aims to predict PVC ceiling sales with the backpropagation neural network (BPNN) method using the R algorithm. Unit gradients are calculated using the average absolute per cent error value (MAPE) to minimize the total square errors of network output. The results showed that the network architecture used was 4 to 6-1 and obtained an accuracy of 88% based on the lowest MAPE value.

Simulation of hybrid boiling nano fluid flow with convective boundary conditions through a porous stretching sheet through Levenberg Marquardt artificial neural networks approach

In the domain of artificial neural networks, the Levenberg-Marquardt technique stands out for its innovative approach and convergent stability. It generates a numerical approach for the progression of a hybrid Cu–Al2CO3/water nanofluid on a porous stretched sheet (HNF-PSS), employing regression plots, state transition measures, histogram representations, and mean squared errors. This work explores the analysis of the fluid flow problem associated with HNF-PSS, introducing a novel application of an intelligent computing system that utilizes the effectiveness of neural networks trained by the Levenberg-Marquardt algorithm (NN-TLMA). The initial mathematical expression, expressed in terms of partial differential equations (PDEs) for HNF-PSS, undergoes transformation into dimensionless nonlinear ordinary differential equations (ODEs). The collection of data for the envisioned NN-TLMA involves parameters influencing the velocity of the HNF-PSS system model, generated using the Lobatto IIIa formula. The efficacy of the suggested NN-TLMA for solving the HNF-PSS is robustly verified by examinations of state transition dynamics, regression analyses, mean square error, and error histogram studies. The consistent alignment of the obtained results with corresponding solutions attests to the legitimacy of the structure, achieving a high level of accuracy within the range of 10–6 to 10–8. Insightful findings demonstrate that the thermal Biot number contributes significantly to an upsurge in fluid temperature. Furthermore, an augmentation in nanofluid concentration and thermal profile is found to increase the porosity strength of the medium. Nevertheless, an attenuation in the velocity profile is noted under these conditions. The application of this simulation work can be found in optimizing the designs of heat exchanger, in biomedical application and in renewable energy processes.

Numerical treatment for radiative hybrid nanofluid flow over a stretching sheet

Artificial neural networks (ANNs) have brought a huge transformation to the machine learning regime by furnishing unparalleled abilities for modeling intricate phenomena and coping with a variety of challenges. In the oneiric field of ANNs, the essential approach for training ANNs is backpropagation. However, optimization is crucial when handling the complex fluid flow phenomenon through backpropagation. The current article implements the Levenberg Marquardt Technique with ANN backpropagation (LMT-BP-ANN) to investigate the radiative flow of a hybrid nanofluid (RHNF) addressing a Cattaneo-Christov flux system past a stretching sheet (SS). Fe2O3 with single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) hybrid nanofluid is considered. The base fluid is ethylene glycol. Physical parameters of nanofluidic system, including the magnetic parameter M, the solid volume fractions φ1 and φ2, the velocity slip parameter λ, the relaxation time parameter Ω, the Biot number ϒ, and the thermal radiation parameter Gr are evaluated through appropriate variations that effectively accomplish the dynamics of the fluid model. These are utilized to create the dataset for LMT-BP-ANNs using the renowned deterministic Adam's numerical technique in Mathematica software. In MATLAB software, 70 % of data is kept for preparing, 15 % for checking, and 15 % for validating the neural network. The performance plot, regression graphs, and error histograms are constructed to demonstrate the suggested LMT-BP neural network scheme's high efficiency, efficacy, and exactness. When ANN performs, a mean squared error (MSE) of order 10−11 is observed. The observed R squared value is 1. As the magnetic parameter estimate grows, the fluid velocity declines, although the thermal profile shows increasing behavior. As the velocity slip parameter increases, the velocity of the fluid drops. Key findings of this study are anticipated to have a noteworthy bearing on sectors that require local refrigeration and heating by contravention.

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits.

An artificial neural network approach to finding the key length of the Vigenère cipher

In this article, we create an artificial neural network (ANN) that combines both classical and modern techniques for determining the key length of a Vigenère cipher. We provide experimental evidence supporting the accuracy of our model for a wide range of parameters. We also discuss the creation and features of this ANN along with a comparative analysis between our ANN, the index of coincidence, and the twist-based algorithms.

A once-through artificial neural network approach for used nuclear fuel inverse depletion analysis: A comparative study

This work addresses the challenge of identifying the origins and burnup history of used nuclear fuel, which is crucial for safeguarding and non-proliferation analysis. It presents a comparative evaluation of two distinct computational approaches to bundles at the same in-core position: the first entails building a forward Artificial Neural Networks (ANN) surrogate model for fuel inventory prediction followed by solving the inverse depletion problem using an optimization solver. The second approach is to build an inverse ANN model that relates the used nuclear fuel inventory to its initial and burnup conditions (a once-through approach), eliminating the need for the inverse optimization solver step which results in better accuracy, less computational costs, and renders the application of such approach practical. KENO VI depletion model is utilized to deplete a VVER assembly model generating the necessary datasets for the ANN inverse model training and validation. The inverse model is then employed to estimate the initial composition and the burnup for several test cases. The results are then compared to the known initial and burnup conditions as well as to the estimations made by benchmark results from an existing ANN forward surrogate model combined with the Particle Swarm Optimization (PSO) solver. Comparative analyses across eight scenarios consistently reveal that the once-through ANN inverse model has superior accuracy and consistency in terms of estimating the actual burnup and initial fuel enrichment with a relative root mean squared error (RMSE) of 1.20×10-2 compared to the benchmark approach (ANN-PSO) which yielded a relative RMSE of 2.18×10-2. Moreover, the once-through inverse approach has a significantly lower computational cost as it requires 1 inverse model run compared to the 9,450,000 model runs required by the benchmark approach (ANN-PSO). Overall, the results demonstrate that the proposed once-through approach simplifies and enhances the efficiency of solving the inverse fuel depletion problem while outperforming the benchmark approach in terms of predictive accuracy computational cost.

Measuring BIM Implementation: A Mathematical Modeling and Artificial Neural Network Approach

Measuring BIM Implementation: A Mathematical Modeling and Artificial Neural Network Approach

Gastrotourists' profiles through their motivations, opinions, and perceptions: An artificial neural networks approach

AbstractCurrently, tourists consider their satisfaction by keeping in mind their gastronomic experience in the place, especially in destinations with historical and cultural symbolism. This research aims to elaborate an artificial neural network that estimates the changes in the sociodemographic characteristics of gastronomic tourists (gender, age, educational level and income) from questions about motivations, perceptions and opinions about gastronomic tourism. This model can build ‘composite sketches’ or estimations of the profiles based on their replies on the topic incorporated into the artificial neural network model, which can be easily customised by the researcher and is helpful in the development and adaptation of gastronomic tourism products and services. Knowing the relationships between the different opinions, motivations and perceptions that gastrotourists have and their sociodemographic profile is essential to better understand the phenomenon of gastronomic tourism and optimise the management of businesses dedicated to it, as well as public agents promoting local gastronomy.

Prediction of Delay Occurrence in Disputed Real Estate Projects: An Artificial Neural Network Approach

Prediction of Delay Occurrence in Disputed Real Estate Projects: An Artificial Neural Network Approach

A review on machinability and optimization of machining parameters of metal matrix composites

The advancement in the state-of-the-art materials that are harder and difficult to cut till date have led to the upgradation towards cutting-edge technologies for machining them. In recent times, utilization of advanced materials in various domains such as aerospace, automobile industries etc. is significantly increases due to the requirement in the aspect of lighter in weight and hardness. At present study, investigations by many researchers on various machining processes and optimization techniques used so far in several metal matrix composites are mentioned. This includes conventional and unconventional machining methods and the user-friendly optimization techniques such as response surface methodology, artificial neural network, fuzzy logic, and Taguchi approach. In addition to these, this study emphasizes on the modelling of the machining processes in MMCs. Finally, open issues and conclusions drawn on the machinability and optimization of tailor-made composites are illustrated with reference to surface finish.

Artificial Neural Network-based model to predict the International Roughness Index of national highways in Nepal

Reliable predictions of pavement performance are crucial for road maintenance, rehabilitation, and reconstruction planning. To facilitate predictions of the International Roughness Index (IRI) changes over time on national highways in Nepal, this study develops a comprehensive overall model, along with regional models that consider climatic and traffic variations among the highways. The study models IRI over time using the Artificial Neural Network (ANN) approach and compares the results with those obtained from a multiple linear regression-based model. The models are developed using pavement IRI, traffic, and climatic (rainfall and temperature) data specific to national highways of Nepal, encompassing 1745 sections and 3710 total observations. The ANN-based overall model has a coefficient of determination (R2) value of 0.82 and outperforms the regression-based model, which has an R2 value of 0.76. The regional models developed for the Terai, Hill, high volume Terai and low volume Terai highways have R2 values of 0.87, 0.91, 0.85 and 0.88, respectively, indicating a good fit. Analysis of the IRI trend over time, as observed from the performance curves generated from the ANN-based model, revealed an S-shaped pattern and lower Root Mean Square Error (RMSE) compared to the regression-based model. Sensitivity analysis highlighted the initial pavement IRI as the most significant parameter in all cases. High temperature days emerged as the second most influential parameter in most models, except for the high volume Terai model, where the number of commercial vehicles serves as the second most sensitive parameter after the initial IRI.

The artificial neural network approach for the transmission of malicious codes in wireless sensor networks with Caputo derivative

AbstractThe current manuscript investigates a six compartmental mathematical model for malicious Codes in Wireless Sensor Network is consider for investigation under the fractional operator of Caputo along with their numerical scheme. The six agent nodes of the network sensors are transferable like in infection with in their community of different nodes. With the help of fixed point theory the presentation of existence and uniqueness of solution of the said model are also given. The scheme of numerical solution under fractional format is developed with the choice of fractional orders which increasing the degree of freedom for such type of network analysis. The numerical simulation of all the six agents are given on different fractional orders along with sensitivity of the fractional orders and some used parameters. The new analysis artificial neural network (ANN) method has been utilized for the considered model and compared with Adams–Bashforth (AB) method. We divided the data set into three categories training, testing and validation with ANN method and the analysis is presented in this work.