Artificial Neural Network Technique Research Articles

Investigation and predictive modeling of the optical behavior of chalcogenide thin film using different artificial neural network techniques

The Te72Ge24As4 samples were recently created in our laboratory in bulk form using the traditional melt-quench method. For its optical characterization. The studied thin film samples have been created using physical vapor deposition. By selecting the 400 nm to 2500 nm spectral range of wavelength, the spectral of the experimental transmission T(λ) and reflectance R(λ) for the studied film samples have been employed to examine optical characteristics. First, we have determined the extinction coefficient (k) and refraction index (n) indices and their spectral distribution of them. Using Tauc's theory, we then computed the optical band gap Eopt. Urbach energy Er is determined from the linear dependence of photon energy on the absorption coefficient which was taken as an indicator to identify the disorder degree in the films. The additional variables, like the dissipation and quality factors, the dielectric constant in complex form, optical, thermal, and electrical conductivity, and volume/surface energy were measured. A comprehensive analysis and predictive modeling using various artificial neural networks (ANNs) techniques were applied to examine the optical behavior of the film samples studied. Materials made of chalcogenide are well-known for having special optical properties, making them appropriate for applications in photonics and optoelectronics. We employed multiple architectures, including Feedforward Neural Networks (FNN) and Recurrent Neural Networks (RNN), to model the extinction coefficient (k) and the refractive index (n) of these films using experimental data. The performance of each model was evaluated using metrics such as mean squared error MSE and correlation coefficients R2. The optical parameters relevant to absorbance, refractive indices, and dielectric coefficients are computed rely on the modeling results and compared with those computed based on experimental measurements. Results demonstrate that FNN and RNN effectively capture the complex relationships between the optical parameters and exhibit small error rates. FFN shows superior accuracy in prediction. That highlights the potential of ANN techniques for advancing the understanding of chalcogenide materials and their applications in modern technology.

Optimisation of PCM passive cooling efficiency on lithium-ion batteries based on coupled CFD and ANN techniques

Ever since the lithium-ion batteries (LIBs) outbreak, there has been an exponential bloom of application over the last decade, especially for electric vehicles, automobiles and other transportation systems. Nonetheless, as the first-generation LIBs eventually aged and became increasingly thermally unstable, the utilisation of thermal management cooling systems is essential to maintain the safe operation of LIB packs in the long term. Compared to active cooling methods, passive cooling often offers a cost-effective, easy-to-install and energy-saving solution without significant changes to the design complexity. This article focuses on the thermal management of prismatic battery packs and proposes a coupling passive cooling method that combines phase change material (PCM) cooling and immersion cooling, which proves to be cost-effective and efficient. Furthermore, the study incorporates an artificial neural network (ANN) model into computational fluid dynamics (CFD) simulations to optimize a specific battery cooling system. This optimization takes into account the PCM package method and the properties of PCM and immersion liquid. The results demonstrate that the immersion liquid exhibits different behaviours under various PCM conditions than natural convection. Overall, this modelling framework presents an innovative approach by utilizing high-fidelity CFD numerical results as inputs for establishing a numerical dataset. Through ANN optimisation, eleven input parameters are considered, and the optimised scenario confirmed that PCM material with a density of 760 kg/m3, thermal conductivity 32 W/(m K), specific heat 1691 (J/kg K), latent heat 80,160 (J/kg), liquidus temperature 302.93 K, solidus temperature 315.15 K and direct liquid density 1.4 (g/ml), thermal conductivity 0.4 (W/m K), specific heat 1220 (J/kg K) with side thickness 5 (mm) and mid thickness 2.5 (mm). With this combination, the optimised performance demonstrated considerable decreases in the maximum temperature and the temperature difference by 4.26 % and 10.8 %, respectively. This approach has the potential to enhance the state-of-the-art thermal management of LIB systems, reducing the risks of thermal runaway and fire outbreaks.

Previsão de manutenção de pavimentos rígidos usando simulações numéricas e redes neurais artificiais

Abstract This work is concerned with the simulation of pavement behavior using the Finite Element Method (FEM) with the goal of predicting the appearance of cracks during its useful life. The developed method can help maintenance planning towards providing users with safety and comfort. The effects of both traffic loads and temperature variations are considered. Artificial Intelligence (AI) tools are adopted to reduce the time necessary for the interpretation of simulation results for the development of an efficient pavement management system. The developed rigid pavement management system uses the Artificial Neural Networks (ANN) technique for the prediction of both pavement response to fatigue accumulation and the behavior of the modeled pavement with a high degree of precision. The networks showed 4.5% and 3.6% square errors for positive and negative temperature gradients and 95% and 97% data correlation, respectively. The SR stress ratio obtained by the developed ABAQUS model is 0.614 and the value provided by the pavement management system is 0.648. The developed methodology was applied to a section of highway BR-101/NE, between the states of Paraiba PR and Pernambuco PE, in Brazil. Comparing the results with the limits provided by National Cooperative Highway Research Program (NCHRP) for traffic in the study, this pavement would require maintenance every six months. The maximum values of tensile stress due to bending were obtained by combining the positive temperature gradient with the traffic axis load at the edge during the summer months (December to March) due to the higher thermal gradient values.

Water pollution and water quality assessment and application of criterion impact loss (CILOS), geographical information system (GIS), artificial neural network (ANN) and decision-learning technique in river water quality management: An experiment on the Mahanadi catchment, Odisha, India

Water pollution and water quality assessment and application of criterion impact loss (CILOS), geographical information system (GIS), artificial neural network (ANN) and decision-learning technique in river water quality management: An experiment on the Mahanadi catchment, Odisha, India

Exploration of Arrhenius activation energy and thermal radiation on MHD double-diffusive convection of ternary hybrid nanofluid flow over a vertical annulus with discrete heating

The primary objective of this article is to examine the effect of discrete heating on MHD double-diffusive convection and thermal radiation of ternary hybrid nanofluid flow heat and mass transfer in a vertical cylindrical annulus along with Arrhenius activation energy and chemical reaction. In this study, the cavity inner wall has two distinct flush-mounted heat sources, while the outer wall is isothermally cooled at a lower temperature. The top and bottom walls are thermally insulated. The ensuing equations that govern the physical framework are solved using the implicit Crank-Nicholson finite difference technique. As the heater advances upward, the flow intensity decreases, leaving a part of the fluid static at the bottom of the cylinder. Because more heat induces high buoyant flow in the annulus, the absolute value of axial velocity and wall temperature rises as the length of the heat source rises. Enhancing the activation energy parameter values drops the Arrhenius energy function, elevating the pace of the generative chemical process and hence the concentration. Increasing the thermal radiation parameter lowers the surface heat flux while enhancing the nanofluid temperature. The Brownian motion parameter corresponds to the random motion of nanoparticles in a fluid, and this irregular movement augments the collision of nanoparticles with fluid particles, causing the particle's kinetic energy which leads to thermal energy and hence increases temperature, a 10% increment in Brownian motion parameter leads to 26% rise in the temperature. Also, the heat and mass transfer characteristics are forecasted and analyzed by considering the Levenberg–Marquardt backpropagating artificial neural network technique.

The Energy Potential of Residual Biomass Gasification Integrated with Internal Combustion Engine in Córdoba, Colombia using Artificial Neural Network Techniques

In this study, the energy potential of waste biomass gasification integrated to internal combustion engine in Cordoba, Colombia was investigated using artificial neural network techniques. A model was trained with proximate and elemental analysis data of different biomasses and this model was used to estimate the gasification potential of the four most abundant biomasses in Cordoba. The model developed achieved an adjusted determination coefficient (R2) of 0.9293 for validation and 0.9048 for training, demonstrating high predictive accuracy. The results indicate that temperature positively influences energy generation potential, while moisture content and air-to-fuel ratio have a negative impact. Among the biomass types analyzed, cassava stands out with the highest energy potential, exceeding 9 GWh/year, followed by plantain at approximately 3 GWh/year, maize cobs below 2 GWh/ year, and rice husk with <0.5 GWh/year. These findings provide critical insights for optimizing biomass gasification processes and harnessing regional biomass resources for energy generation.

Pulse Shape Discrimination of n/γ in Liquid Scintillator at PMT Nonlinear Region Using Artificial Neural Network Technique.

Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for detecting neutrinos reacting with matter, detection is difficult because of the low interaction of neutrinos. In particular, it is important to distinguish between neutron (n) and gamma (γ) signals. The principle of the interaction of neutrons with matter differs from that of gamma rays with matter, and hence the detection signal's waveform is different. Conventionally, pulse shape discrimination (PSD) is used for n/γ separation. This study developed a machine learning method to see if it is more efficient than the traditional PSD method. The possibility of n/γ discrimination in the region beyond the linear response limits was also examined, by using 10- and 2-inch photomultiplier tubes (PMTs) simultaneously. To the best of our knowledge, no study has attempted PSD in a PMT nonlinear region using artificial neural networks. Our results indicate that the proposed method has the potential to distinguish between n and γ signals in a nonlinear region.

Utilizing Machine Learning for Analysing Digital Marketing's Influence on Gen-Z Travellers’ Destination Selection

Digital marketing significantly influences travellers’ decision-making processes. This research aims to analyze the specific digital marketing strategies affecting Gen-Z travellers’ decisions. Machine learning techniques were employed to uncover key insights. Data were collected through a survey using a convenience random sampling of 346 university students in Thailand. Correlation analysis and machine learning approaches, such as logistic regression and neural network analysis, were used to explore the relationships between various digital marketing strategies and their impact on Gen-Z travellers’ destination choices. The findings indicate that Online Travel Agency (OTA) websites exert the most significant influence on Gen-Z travellers’ decision-making processes, with an odds ratio of 1.6879. This is followed by the use of social media marketing strategies and businesses having their own websites. To validate these results, an Artificial Neural Network (ANN) technique was also employed, confirming the importance of rankings derived from the logistic regression analysis. This research provides valuable insights into which digital marketing strategies most influence Gen-Z travellers’ decision-making processes. These insights can help tourism businesses make informed investments in digital marketing strategies, prioritizing those that align with the preferences of Gen-Z travellers.

The clinicopathological significance of BRI3BP in women with invasive breast cancer.

Invasive breast cancer (BC) is a highly life-threatening disease affecting women world-wide. While its early identification may benefit the provision of more effective therapies, several BC-associated factors may influence BC patients' therapeutic outcomes. Therefore, identifying novel prognostic and therapeutic targets for invasive BC can help with accurate prognosis and therapy-related decisions. The BRI3 binding protein (BRI3BP) gene was found to be a principal gene in invasive BC cohorts using artificial neural network (ANN) techniques. Thus, this study aimed to evaluate the clinicopathological significance of BRI3BP at the transcriptomic and proteomic levels in invasive BC. Two transcriptomic BC cohorts, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n=1,980) and The Cancer Genome Atlas (TCGA; n=854), were used to evaluate BRI3BP expression at the mRNA level. Formalin-fixed paraffin-embedded (FFPE) tissues from an invasive BC cohort (n=100) were also used to evaluate BRI3BP expression at the protein level via immunohistochemistry. The association between BRI3BP expression, clinicopathological characteristics, and patient outcomes was evaluated. In both METABRIC and TCGA cohorts, high expression of BRI3BP was significantly associated with aggressive tumor features such as high histological grade, large tumor size, and lymph vascular invasion (LVI) positivity. At the protein level, high BRI3BP expression was associated with high histological grade, hormone receptor negativity, high expression of Ki67, and poor outcome. This study revealed the prognostic significance of BRI3BP in invasive BC patients. Further functional assessment is needed to confirm the biological role of BRI3BP in BC.

A novel reverse and forward directional relaying scheme in six phase overhead transmission lines using adaptive neuro-fuzzy inference system

Recent power system is structurally difficult and is vulnerable to undesirable conditions like transmission faults. In this event of transmission line faults, exact fault zone detection enhances the restoration process, thus improving reliability of the complete power system. In order to solve the above problem, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based fault zone detector, which combines artificial neural network (ANN) and fuzzy logic technique (FLT) in six phase overhead transmission lines (SPOTL). To overcome the limitation of ANN and fuzzy expert system (FES) architectures and, the selection work has been formulated as an optimization method and solved using ANFIS. The inputs are the zero sequence component currents at the middle bus of the transmission line. The training data are extracted using discrete Fourier transform and collected, and then ANFIS is trained to identify the fault zone. The ANFIS based scheme reach setting has been checked for various types of faults, with a wide range of faults and transmission line parameters. Simulation study ensure that this method has a high reach setting, does not require the design of communication channel. Further, the ANFIS study shows that ANFIS is suitable for all type of faults. The ANFIS significantly outperforms other techniques proposed in the literature in terms of various evaluation metrics.

Prognostic Significance of DSCC1, a Biomarker Associated with Aggressive Features of Breast Cancer.

Background and Objectives: Invasive breast cancer (BC) was traditionally investigated visually, and no technique could identify the key molecular drivers of patient survival. However, essential molecular drivers of invasive BC have now been discovered using innovative genomic, transcriptomic, and proteomic methodologies. Nevertheless, few evaluations of the prognostic factors of BC in Saudi Arabia have been performed. Evaluating the biomarkers associated with the development of early-stage BC could help determine the risk of metastasis and guide treatment decisions. In a previous study, using large BC cohorts and artificial neural network techniques, DNA replication and sister chromatid cohesion 1 (DSCC1) was found to be one of the principal genes in invasive BC samples. To date, no studies have addressed the prognostic significance of DSCC1 in invasive BC and its association with aggressive tumor behavior. This research aimed to address this gap. Materials and Methods: The association of clinicopathological features and patient outcomes with DSCC1 expression at the mRNA level was assessed using the Molecular Taxonomy Breast Cancer International Consortium (METABRIC; n = 1980) and The Cancer Genome Atlas (TCGA; n = 854) cohorts. DSCC1 was also evaluated at the protein level using immunohistochemistry on samples from invasive BC patients (n = 100) presenting to King Abdul Aziz Specialist Hospital in Saudi Arabia. The association of clinicopathological parameters (including patient age, tumor grade, tumor size, and patient outcome) with protein level was also evaluated. Results: In both METABRIC and TCGA cohorts, high expression of DSCC1 was significantly associated with high histological grade, large tumor size, lymphovascular invasion positivity, and hormone receptor negativity (all p < 0.001). A high DSCC1 mRNA level was associated with poor outcomes (p < 0.001 for METABRIC, p = 0.23 for TCGA). At the protein level, high DSCC1 expression was associated with high histological grade (p = 0.001), lymph node presence (p = 0.008), hormone receptor negativity (p = 0.005), high Ki67 expression (p = 0.036), and shorter survival (p = 0.008). Conclusions: This study confirmed the prognostic significance of DSCC1 in invasive BC patients. DSCC1 could be a therapeutic target in BC cases with poor outcomes.

Implementing enterprise metaverse as a means of enhancing growth hacking performance: Will adopting the metaverse be a success in organizations?

This study examines the factors influencing the implementation of the enterprise metaverse within organisations and aims to define how this implementation can assist organisations in achieving growth hacking. The study employed a mixed-methods research design, utilising structural equation modelling (SEM) and artificial neural network (ANN) techniques to analyse the framework. The results show that real-time analytics capability, operational excellence, industry pressure, and stakeholder pressure are significantly associated with enterprise metaverse implementation. The results also reveal that the success or failure of an enterprise metaverse largely depends on how it is implemented. In addition, potential and realised absorptive capacity are identified as significant moderators. This study provides a deeper understanding of organisational behavioural intentions towards the enterprise metaverse using the underpinnings of institutional theory.

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.

An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization

The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics.

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&lt;sub&gt;4&lt;/sub&gt;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