Artificial Neural Network Technique Research Articles

MODELING COMMERCIAL BANKS' KEY DRIVERS FOR FUND MOBILIZATION IN NIGERIA USING ARTIFICIAL NEURAL NETWORKS

This study explores the core role driving the willingness of commercial banks to mobilise funds for economic development based on the Multilayer Perceptron (MLP) of the artificial neural network (ANN) technique. This approach allows for a nuanced understanding of interdependencies that traditional linear models may overlook, making it particularly suited for analyzing intricate financial systems in emerging economies like Nigeria. This study is critically supported by the Financial Intermediation Theory, which explains the role of financial institutions as intermediaries that facilitate the flow of funds from savers to borrowers. Data used for the analysis and prediction is purposively obtained from 10 commercial banks in Nigeria. The results show that financial institutions are mostly driven to mobilize funds because of their commitment to ensuring an adequate flow of money to serve the deficit sectors of the economy compared to any other underlying reasons. The prediction performs optimally with an r2 value of 86.5% with a cubic predictability model of ????. ???????????????? +????. ???????????? + ????. ???????????????? − ????. ???????????????? − ????. ???????? = ????.????????????????, and the Sum of Square Error (SSE) of 0.002 is minimal based on practice. This study is significant as it could enable financial institutions to make future role predictions relating to this concept in Nigerian settings or other settings analogous to Nigeria using the derived ANN model. These insights provide a basis for banks in Nigeria and similar economies to make strategic financial decisions, supporting the application of ANN models to predict and enhance financial institutions' roles in economic development.

Analysing the Influence of Augmented Reality on Organization Performance via Supply and Logistics Value Chain Functions: A Hybrid ANN-PLS Model Assessment in the Gulf Cooperation Council Region

Background: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited. This article aims to investigate the potential effects of AR technology on organizational performance through the mediation role of SCM and logistics value chain functions to address the existing knowledge gap. Methods: This research employed a cross-sectional design and an explanatory survey as a deductive approach for hypothesis development. The primary data collection method involved the self-administration of a questionnaire to furniture suppliers located in the Gulf Cooperation Council (GCC), including six countries. Of the 656 questionnaires submitted to suppliers, 483 were considered usable, yielding a response rate of 73.6%. The research utilized partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques to evaluate the gathered data. Results: The current paper’s statistical evidence demonstrates that AR implementation has a positive impact on the supply and logistics value chain activities and organizational performance of furniture suppliers in the GCC region. Moreover, it illustrates that the design and planning variable of supply chain value dominates as the primary predictor of organization performance. The results indicated that the ANN strategy provided a more comprehensive explanation of internally generated constructs compared to the PLS-SEM technique. Conclusions: This study demonstrates its usefulness by advising furniture industry decision-makers on what to avoid and what aspects to consider when creating plans and regulations. The report also suggests operations managers apply machine learning (ANN) for prediction and decision-making in supply and operations value chains. This essay looks at how the AR and resource-based supply value chain view may affect company performance across countries, firm sizes, and ages.

Optimized punching shear design in steel fiber-reinforced slabs: Machine learning vs. evolutionary prediction models

This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved R² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes.

Free Vibration of Axially Functionally Graded Porous Beams with Varying Cross-Section

This study aims to determine the natural frequencies of axially functionally graded porous material (FGPM) beams with non-uniform cross-sections under a variety of boundary conditions. Two types of pore distribution were used: even and uneven. Initially, an analytical method was used to determine the natural frequencies of FGPM beams with different cross-sections. The Fredholm integral approach was used to obtain the characteristic equations. Furthermore, the collected results are confirmed and compared to the existing literature. The artificial neural network (ANN) technique is then used to predict the fluctuations in the natural frequency of a clamped–simply supported axially FGPM beam with a non-uniform cross-section. The prediction of natural frequencies by ANN is based on a large dataset of over 1[Formula: see text]100 data points acquired from the analytical solution obtained in this study and data available in the literature. This research conducts a parametric analysis to evaluate the influence of numerous aspects, including as beam characteristics, material properties, geometric details, gradient parameters, and porosity distribution, on functionally graded (FG) beam vibration behavior. Finally, both ANN and computational analysis demonstrate that porosity distributions and non-uniform cross-sectional area have a substantial effect on the natural frequencies of axially FG beams.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B<sub>4</sub>C Composites: A Comparative Study with Nature-Inspired Algorithms

Precision polishing is difficult for advanced materials like silicon carbide and boron carbide. Magnetic abrasive flow machining (MAFM) has become an effective method for cleaning, deburring, and polishing metal and high-tech engineering parts. By finishing hybrid Al/SiC/B4C-metal matrix composites (MMCs), this research uses MAFM for experimental readings. The present work is innovative due to the aluminum workpiece fixture, hybrid composites, and response surface methodology (RSM) modeling. The neural simulation of the MAFM process and nature-inspired error reduction make it unique. Using six input and two output parameters, a generic framework is created. Box-Behnken design (BBD) of response surface methodology plans and executes 54 runs of experimentation. The hybrid artificial neural network (ANN) technique is used to compare the MAFM process systematically. ANN is used to model parameter input-output relations. To anticipate the created surface accurately, regression models must be precise. These hybrid particle swarm optimization (PSO)-genetic algorithm (GA)-simulated annealing (SA) algorithms optimize the MAFM process. Additionally, trained ANN models outperform the BBD model in prediction. For optimal error reduction, the neural network uses Bayesian regularization with 112 iterations. The ANN model regression graph shows a correlation between inputs and outputs. A scanning electron microscope (SEM) with 300-magnification examines the workpiece surface. According to SEM, MAFM provides fine surface textures, thus reducing abnormalities.

A hybrid decision support system in medical emergencies using artificial neural network and hyperbolic secant grey wolf optimization techniques

A hybrid decision support system in medical emergencies using artificial neural network and hyperbolic secant grey wolf optimization techniques

Intellectual property protection as catalyst for radical technological innovation in national research program teams through innovation milieu and group potentials

Based on the innovation-driven theory and the ability-motivation-opportunity (AMO) perspective, we explore the role of intellectual property protection (IPP) in enhancing the radical technological innovation (RTI) of national research project teams (NRPT). Survey data from 336 national research project team members from universities and enterprises were used to analyze the theoretical model of this study, bringing in the chain-mediated effects of innovation milieu (IM) and group potential (GP) for analysis, as well as two-stage hybrid partial least squares structural equation modeling (PLS-SEM) and artificial neural network techniques (ANN) to evaluate the hypotheses. The empirical findings of this paper show that the strength of IPP has a positive relationship with RTI of NRPT, and that IPP is the most important predictor of this. These new findings expand the scope of innovation-driven theory and AMO theory, and provide a constructive model for NRPT to provide suggestions for the improvement of IPP system as a way to improve the realization of RTI. The results of this study can guide policymakers in strengthening IPP systems to encourage research teams to explore innovation more proactively and to facilitate the reasonable sharing and transfer of innovative outcomes. By creating a supportive innovation environment and maximizing the potential of research teams, technological breakthroughs can be achieved more effectively.

Air-cooling process modelling for cord steel based on fusion approach of mechanism and artificial neural network

In this study, a modelling approach is introduced that fuses the mechanistic model with an artificial neural network (ANN) during the cord steel manufacturing. This involves replacing certain mechanistic calculations with ANN representations, optimizing mechanistic parameters through ANN techniques, and implanting mechanistic equations into the ANN framework to achieve more accurate and efficient calculations of the cooling process. The mechanistic parameters come from the models including Natural Convection Heat Transfer, Forced Convection Heat Transfer, Radiation Heat Transfer and Phase Transition during the air-cooling process. The model demonstrated an average error of 8.6 °C and 5.4 °C, compared to measurement values from two temperature sensors on the air-cooling line, and an average error of 3.3 °C relative to manually obtained temperature.

New approach for accurate discrimination and location of power transformers with different internal winding faults.

Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using conventional measuring devices attached to the transformer. Different types of internal winding faults are intensely considered: partial discharge, inter-disk faults, series and shunt short circuit faults and axial displacement. Depending on the transformer measured output voltage, input voltage and the input current, the construction of a locus diagram (ΔV-Iin) serves as an indicator for any physical modification to the winding. Using five suggested features extracted from the developed locus, an artificial neural network (ANN) technique is applied to accurately distinguish any deviation from the transformer healthy condition. The exact location of each fault inside the windings of power transformer is then determined. The obtained results validate the usefulness of the proposed scheme for different internal faults. The superiority of the proposed scheme is extensively examined by comparing its results with some published schemes.

Modeling properties of recycled aggregate concrete using gene expression programming and artificial neural network techniques

Modeling properties of recycled aggregate concrete using gene expression programming and artificial neural network techniques

A novel approach to predicting liquefaction-induced settlements using Kolmogorov–Arnold Networks (KANs)

Soil liquefaction refers to the loss of strength and bearing capacity of soil under dynamic loads. Structures built on liquefied soils experience settlement. In this study, the newly developed Kolmogorov–Arnold Networks (KAN) method, an innovative artificial neural network technique, was utilized to predict liquefaction-induced settlement. The KAN method, which extends beyond the traditional Multi-Layer Perceptron (MLP) approach, was compared with the Random Forest method for benchmarking purposes. Using data derived from laboratory and field studies, models were constructed using both methods, and their performances were analyzed. According to the results, the KAN model outperformed the RF model in terms of R2, MAE, MSE, and RMSE metrics. Additionally, the feature importance analysis on the KAN model identified cyclic stress ratio (csr) and corrected SPT blow count (n1_60) as the most significant variables. These results underscore the potential of the KAN method in enhancing predictive accuracy and reliability in geotechnical applications, paving the way for its broader acceptance and implementation in real-world scenarios.

Image processing and neural network technique for size characterization of gravel particles

Particle size is considered one of the significant characteristics used in geotechnical practices. Traditionally, sieve analysis is utilized for coarse-grained soil. However, this method could be time consuming and take much effort, especially for large scale infrastructure projects. This paper presents an efficient method for estimating gravel particle characterization utilizing image processing and artificial neural network technique (IPNN). The proposed algorithm is performed by utilizing particle boundary delineation and shape feature extraction to train a neural network model for estimating gravel size distribution curve. It is found that excellent agreement exists between the results obtained from conventional sieve analysis and neural analysis for gravel soil particles with maximum difference in passing percentages up to only 3.70%. The proposed technique shows satisfactory results for crushed stone samples with maximum difference in passing percentages about 10.90% mainly in large diameter particles. The presented technique (IPNN) could offer a promising alternative technique for material quality control process especially in large scale projects.

Etodolac utility in osteoarthritis: drug delivery challenges, topical nanotherapeutic strategies and potential synergies.

Osteoarthritis (OSA) is a prevalent joint disorder characterized by losing articular cartilage, primarily affecting the hip, knee and spine joints. The impact of OSA offers a major challenge to health systems globally. Therapeutic approaches encompass surgical interventions, non-pharmacological therapies (exercise, rehabilitation, behavioral interventions) and pharmacological treatments. Inflammatory processes within OSA joints are regulated by pro-inflammatory and anti-inflammatory cytokines. Etodolac, a COX-2-selective inhibitor, is the gold standard for OSA management and uniquely does not inhibit gastric prostaglandins. This comprehensive review offers insights into OSA's pathophysiology, genetic factors and biological determinants influencing disease progression. Emphasis is placed on the pivotal role of etodolac in OSA management, supported by both preclinical and clinical evidences in topical drug delivery. Notably, in-silico docking studies suggested potential synergies between etodolac and baicalein, considering ADAMTS-4, COX-2, MMP-3 and MMP-13 as essential therapeutic targets. Integration of artificial neural network (ANN) techniques with nanotechnology approaches emerges as a promising strategy for optimizing and personalizing topical etodolac delivery. Furthermore, the synergistic potential of etodolac and baicalein warrants in-depth exploration. Hence, by embracing cutting-edge technologies like ANN and nanomedicine, the optimization of topical etodolac delivery could guide a new era of OSA treatment.

Ultrasound-assisted extraction, optimization, and purification of total flavonoids from [formula omitted] and analysis of their antioxidant, anti-inflammatory, and analgesic activities

Daphne genkwa (D. genkwa) is the dried flower buds of a Chinese medicinal plant with multiple biological activities. Response surface methodology (RSM) combined with artificial neural network (ANN) techniques were utilized to optimize ultrasound-assisted extraction conditions for D. genkwa. Antioxidant activity and anti-inflammatory and analgesic properties of total flavonoids from D. genkwa (TFDG) were assessed. Optimal conditions involving ultrasonic power of 225 W, 30 min extraction time, 30 mL/g liquid–solid ratio, 60 °C extraction temperature, and 70% ethanol concentration yielded a maximum total flavonoids content (TFC) of 5.41 mg/g. After microporous resin purification, four specific flavonoids in D. genkwa were identified and quantified using high-performance liquid chromatography (HPLC). The TFDG demonstrated potent antioxidant activity, with a 94% rate of scavenging the 2, 2-diphenyl-1-picrylhydrazyl (DPPH). Furthermore, TFDG exhibited pain-alleviating properties in hot plate and acetic acid-induced writhing tests and noteworthy inhibitory effects on xylene-induced ear swelling in mice. The total flavonoids extracted by ultrasound had excellent biological activity. This establishes a foundation for further investigation into the potential medical value of D. genkwa.

A Comparative Study of Machine Learning and Conventional Techniques in Predicting Compressive Strength of Concrete with Eggshell and Glass Powder Additives

Recent research has focused on assessing the effectiveness of response surface methodology (RSM), a non-machine learning technique, and artificial neural networks (ANN), a machine learning approach, for predicting concrete performance. This research aims to predict and simulate the compressive strength of concrete that replaces cement and fine aggregate with waste materials such as eggshell powder (ESP) and waste glass powder (WGP) for sustainable construction materials. In order to ensure concrete’s durability and structural integrity, a compressive strength evaluation is essential. Precise predictions maximize efficiency and advance sustainability, particularly when dealing with waste materials like ESP and WGP. The response surface methodology (RSM) and artificial neural network (ANN) techniques are used to accomplish this for practical applications in the built environment. A dataset comprising previously published research was used to assess ANN and RSM’s predictive and generalization abilities. To model and improve the model, ANN used seven independent variables, while three variables, cement, waste glass powder, and eggshell powder, improved the RSM. Both the ANN and RSM techniques are effective instruments for predicting compressive strength, according to the statistical results, which include mean squared error (MSE), determination coefficient (R2), and adjusted coefficient (R2 adj). RSM was able to achieve the R2 by 0.8729 and 0.7532 for compressive strength, while the accuracy of the results for ANN was 0.907 and 0.956 for compressive strength. Moreover, the correlation between ANN and RSM models and experimental data is high. The artificial neural network model, however, exhibits superior accuracy.

Predictive modeling of hepatitis B viral dynamics: a caputo derivative-based approach using artificial neural networks

A fractional model for the kinetics of hepatitis B transmission was developed. The hepatitis B virus significantly affects the world’s economic and health systems. Acute and chronic carrier phases play a crucial part in the spread of the HBV infection. The Hepatitis B infection can be spread by chronic carriers even though they show no symptoms. In this article, we looked into the Hepatitis B virus’s various stages of infection-related transmission and built a nonlinear epidemic. Then, a fractional hepatitis B virus model using a Caputo derivative and vaccine effects is created. First, we determined the proposed model’s essential reproductive value and equilibria. With the aid of Fixed Point Theory, a qualitative analysis of the problem’s approximative root has been produced. The Adams-Bashforth predictor-corrector scheme is used to aid in the iterative approximate technique’s evaluation of the fractional system under consideration that has the Caputo derivative. In the final section, a graphical representation compares various noninteger orders and displays the discovered scheme findings. In this study, we’ve utilized Artificial Neural Network (ANN) techniques to partition the dataset into three categories: training, testing, and validation. Our analysis delves deep into each category, comprehensively examining the dataset’s characteristics and behaviors within these divisions. The study comprehensively analyzes the fractional HBV transmission model, incorporating both mathematical and computational approaches. The findings contribute to a better understanding of the dynamics of HBV infection and can inform the development of effective public health interventions.

Optimization of nano-filler and silane treatment on mechanical performance of nanographene hybrid composites using RSM and ANN technique

This study utilized response surface methodology (RSM) and artificial neural networks (ANN) to optimize the formulation of polymer composites with three key variables: silane concentration, silane dipping time, and the number of nanoparticles. RSM and analysis of variance were employed to explore the effects of these variables on the composite’s mechanical properties, while ANN was used for analyzing complex interactions between parameters. The results reveal a significant correlation between the predicted and actual response values derived from the models. Notably, silane treatment of fiber was identified as the primary factor influencing the composite’s flexural strength. The study found that all fiber-related properties substantially affected both flexural strength and hardness, with silane dipping time being the most critical factor. Additionally, incorporating nanoparticles improved the fiber matrix’s strength by enhancing agglomeration. ANN achieved a 95% accuracy in predicting flexural strength and hardness. Comparisons among experimental data, the regression model, and ANN confirmed the robustness of the ANN predictions. The optimal composition identified for flexural strength was 5 wt % nanoparticles, 10 wt % silane treatment, and a 20-min dipping time. For hardness, the ideal formulation was 5 wt % nanoparticles, 15% silane treatment, and a 20-min dipping time in silane. This comprehensive approach offers a refined method for developing high-performance polymer composites with tailored properties.

Time-variations of wave energy and forecasting power availability at a site in Fiji using time-series, regression and ANN techniques

ABSTRACT Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit ( R 2 ) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved.

Application of artificial neural network and least squares regression technique in developing novel models for predicting rock parameters

Application of artificial neural network and least squares regression technique in developing novel models for predicting rock parameters