Hybrid Artificial Neural Network Research Articles

Modelling and optimization of biodiesel production from waste fish oil using nano immobilized rPichiapastoris whole cell biocatalyst with response surface methodology and hybrid artificial neural network based approach

In this study, zinc oxide (ZnO) nano particle immobilized recombinant whole cell biocatalyst (rWCB) was used for bioconversion of waste fish oil in to biodiesel in a lab scale packed bed reactor (PBR). Central composite design and hybrid artificial neural network (ANN) models were explored to optimize the production of biodiesel. Developed rWCB exhibited maximum lipase activity at 15 % (v/v) of glutaraldehyde concentration and 6 % (w/v) of ZnO nanoparticles at pH of 7. Maximum biodiesel yield reached about 91.54 ± 1.86 % after 43 h in PBR using hybrid ANN model predicted process conditions of 13.2 % (w/v) of nano immobilized rWCB concentration and 4.7:1 of methanol to oil ratio at 33 °C. Importantly, developed nano immobilized rWCB was adequately stable for commercialization. Thus, production of biodiesel from waste fish oil using ZnO nano immobilized rWCB could become potential candidate for commercialization.

Photonic advantage of optical encoders

Abstract Light’s ability to perform massive linear operations in parallel has recently inspired numerous demonstrations of optics-assisted artificial neural networks (ANN). However, a clear system-level advantage of optics over purely digital ANN has not yet been established. While linear operations can indeed be optically performed very efficiently, the lack of nonlinearity and signal regeneration require high-power, low-latency signal transduction between optics and electronics. Additionally, a large power is needed for lasers and photodetectors, which are often neglected in the calculation of the total energy consumption. Here, instead of mapping traditional digital operations to optics, we co-designed a hybrid optical-digital ANN, that operates on incoherent light, and is thus amenable to operations under ambient light. Keeping the latency and power constant between a purely digital ANN and a hybrid optical-digital ANN, we identified a low-power/latency regime, where an optical encoder provides higher classification accuracy than a purely digital ANN. We estimate our optical encoder enables ∼10 kHz rate operation of a hybrid ANN with a power of only 23 mW. However, in that regime, the overall classification accuracy is lower than what is achievable with higher power and latency. Our results indicate that optics can be advantageous over digital ANN in applications, where the overall performance of the ANN can be relaxed to prioritize lower power and latency.

Modeling and simulation of the enzymatic kinetics for the production of Galactooligosaccharides (GOS) using an Artificial Neural Network hybrid model

Due to the complexity of biochemical systems, the development of traditional phenomenological models is limited if the underlying mechanics are not entirely known. As an alternative, hybrid model frameworks, consisting of data-driven models complemented with first principles models like conservation law, are starting to be used for complex systems. In this work, a comparison of the modeling capabilities between a data-driven model and a hybrid model was developed. The enzymatic production of Galactooligosaccharides (GOS) with the effect of metallic ions was considered as case study. Compared with the experimental results, predictions from data-driven model achieve an R2 of 0.9188 in the best training fold, and the hybrid model an R2 of 0.9696 in the best training fold. Illogical predictions were avoided by including non-phenomenological first-principles constraints into the hybrid model. Finally, an optimization analysis was carried out to find the highest GOS productivity using the hybrid model, optimization results present a deviation of 5.99 % compared to the highest productivity found from experimental data.

Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms

Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms

Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network

AbstractThis study seeks to present a sophisticated artificial intelligence (AI) framework to model the compressive strength () of concrete containing waste foundry sand (WFS), with the aim of minimizing the need for time‐consuming laboratory tests and skilled technicians. For this purpose, artificial neural network (ANN) is hybridized with two metaheuristic algorithms—particle swarm optimization (PSO) and ant colony optimization (ACO) to predict the of 340 samples containing WFS collected from the literature. Results indicated that the ACO + ANN model showed the best performance with the Pearson coefficient of 0.9971, mean absolute error of 0.0221 MPa, and root mean squared error of 0.7473 MPa. The values of prediction errors exhibited that more than 90% of them in the ACO + ANN model fall within the range of (−1.5 MPa, 1.5 MPa), while this range for the PSO + ANN and traditional ANN models was obtained as (−3 MPa, 3 MPa) and (−4 MPa, 4 MPa), respectively. Furthermore, the proposed ACO + ANN model predicted the in the range of 5.24–54.48 MPa. Besides, the results indicated that the water‐to‐cement ratio, cement strength class, and cement content had the most significant impact on the of WFS‐containing concrete. Finally, a comparison was made between the proposed ACO + ANN model and four other AI models recently proposed in the literature, in which the performance criteria demonstrated that the proposed ACO + ANN model outperformed the models in the literature.

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network.

To address the difficulty and complexity of detecting piston errors for segmented telescopes, this paper proposes a new piston error measurement method based on a hybrid artificial neural network. First, we use the Resnet network to learn the mapping relationship between the focal plane degradation image and signs of the piston error. Then, based on the established theoretical relationship between the modulation transfer function and the piston error, a BP neural network is used to learn the mapping relationship between the MTF and the absolute value of the piston error. After the training of the hybrid network is completed, a wide-range and high-precision detection of the piston error of the sub-mirrors can be achieved using the combined output of the two networks, where only a focal plane image of the point source with broadband illumination is used as the input. The detection range can reach the entire coherent length of the input broadband light, and the detection accuracy can reach 10 nm. The method proposed in this paper has the advantages of high detection accuracy, a wide detection range, low hardware cost, a small network scale, and low training difficulty.

An audio-based anger detection algorithm using a hybrid artificial neural network and fuzzy logic model

An audio-based anger detection algorithm using a hybrid artificial neural network and fuzzy logic model

Development of a hybrid artificial neural network method for evaluation of the sustainable construction projects

Planned methods may be developed to improve the efficiency of building construction. The construction business is profoundly impacted by the prevalence of inaccurate cost and schedule prediction. The main strategy to improve the project performance is to evaluate the hybrid sustainable materials using the artificial neural network (ANN) method based on the effective factors in construction projects in Iraq. This strategy needs an effective method to classify the project input representation and specify the accurate activity of each factor. This paper uses a hybrid artificial neural network to correlate and classify the sustainable hybrid of construction projects to evaluate their performance. The contribution of this method is the selection of the Multi-Criteria Decision-Maker method (MCDM) based on time and cost-effective factors correlated with the artificial neural network method. A dynamic selection procedure for project materials may be created using the existing technique as an evolutionary model for successful project completion. The MCDM observed that the appropriate sustainable material was considered as the main factor with a rank of 0.823 for cost effect and 0.735 for time effect and the main influence factor in Iraqi projects was the building height. The results present superior functional cost evaluation results correlated with the selection of hybrid sustainable materials.

Optimization of the Effect of Laser Power Bed Fusion 3D Printing during the Milling Process Using Hybrid Artificial Neural Networks with Particle Swarm Optimization and Genetic Algorithms

Optimization of the Effect of Laser Power Bed Fusion 3D Printing during the Milling Process Using Hybrid Artificial Neural Networks with Particle Swarm Optimization and Genetic Algorithms

An intelligent adverse delivery outcomes prediction model based on the fusion of multiple obstetric clinical data

Adverse delivery outcomes is a major re-productive health problem that affects the physical and mental health of pregnant women. Obviously, obstetric clinical data has periodically time series characteristics. This paper proposed a three stage adverse delivery outcomes prediction model via the fusion of multiple time series clinical data. The first stage is data aggregation, in which the data set is collected from the obstetric clinical data and divided based on time series features. In the second stage, a multi-channel gated cycle unit is used to solve the calculation error caused by irregular sampling of time series data. The hidden layer feature vector is connected with the fully connected layer, reshaped into a new one-dimensional feature, and fused with the non-time series data into a new data set. The third stage is the prediction stage of adverse delivery outcomes. By connecting the multi-channel gated cycle unit with the extreme gradient lift, the data transmitted in the corresponding channel is used in the feature extraction stage, in which the weighted entropy-based feature extraction is adopted. With the help of the extracted features, a hybrid artificial neural network architecture (MGRU-XGB) was developed to predict adverse delivery outcomes. The experimental results showed that the hybrid model had the best prediction performance for adverse delivery outcomes compared with other single models in terms of sensitivity, specificity, AUC and other evaluation indexes.

Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory.

In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model's performance was evaluated using simulated data from visual change detection experiments conducted on mice. Comparisons were made between the proposed model, a recurrent neural network simulating short-term memory based on sustained neural activity, and a feedforward neural network with short-term synaptic depression (STPNet) trained to achieve the same performance level as the mice. The results revealed that incorporating astrocytic modulation of synaptic transmission enhanced the model's performance.

Probabilistic Slope Stability Analysis of Mount St. Helens Using Scoops3D and a Hybrid Intelligence Paradigm

In the past, numerous stratovolcanoes worldwide witnessed catastrophic flank collapses. One of the greatest risks associated with stratovolcanoes is a massive rock failure. On 18 May 1980, we witnessed a rock slope failure due to a volcano eruption, and a 2185.60 m high rock slope of Mount St. Helens was collapsed. Thus, from the serviceability perspective, this work presents an effective computational technique to perform probabilistic analyses of Mount St. Helens situated in Washington, USA. Using the first-order second-moment method, probability theory and statistics were employed to map the uncertainties in rock parameters. Initially, Scoops3D was used to perform slope stability analysis followed by probabilistic evaluation using a hybrid computational model of artificial neural network (ANN) and firefly algorithm (FF), i.e., ANN-FF. The performance of the ANN-FF model was examined and compared with that of conventional ANN and other hybrid ANNs built using seven additional meta-heuristic algorithms. In the validation stage, the proposed ANN-FF model was the best-fitted hybrid model with R2 = 0.9996 and RMSE = 0.0042. Under seismic and non-seismic situations, the reliability index and the probability of failure were estimated. The suggested method allows for an effective assessment of the failure probability of Mount St. Helens under various earthquake circumstances. The developed MATLAB model is also attached as a supplementary material for future studies.

Prediction model of wind speed using hybrid artificial neural network based on Levenberg-Marquardt algorithm

<span lang="EN-US">In this paper, a new method is developed to model the wind speed data that is considered as a function of seasonal wind variations. A hybrid artificial neural network (HANN) is investigated based on the Weibull distribution model. The presented HANN model predicts wind speed data with seasonal and chronological characteristics similar to real wind data. The design of the wind farm was implemented using MATLAB software. The suggested model has been applied and validated with wind data collected from the site of Tangier-MED in Morocco over two years, 2015 and 2016. The errors in terms of mean absolute percentage error MAPE and root mean square error RMSE are respectively 0.011 and 0.067 in 2015.</span>

Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India.

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

Use and acceptance of crypto currencies in India: an evaluation of block chain application in financial sector using PLS SEM and ANN approach

PurposeThis paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.Design/methodology/approachThis study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.FindingsThe findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.Research limitations/implicationsPrior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.Practical implicationsThe research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.Social implicationsCryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.Originality/valueGiven the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.

Modelling of clear water scour depth around bridge piers using M5 tree and ANN-PSO

Abstract Scouring refers to the process by which bed sediment in a river is eroded around the periphery of a bridge abutment or pier. Many empirical models are available to estimate the scour depth for different flow, geometry, and bed roughness condition. However, none of them provide a better estimation of scour depth for a wide range of input parameters. Thus, in this paper, the scour depth around bridge piers has been modelled using M5 tree and hybrid artificial neural network (ANN)-particle swarm optimisation (PSO) techniques by considering the wide range of datasets. The clear-water scouring (CWS) datasets are collected from the literature and five different non-dimensional influencing parameters are selected as input parameters to model the scour depth. A Gamma test (GT) was performed to choose the best input parameter combinations. Based on the lowest gamma value and V-ratio, 4 out of 26 distinct input combinations for CWS depth modelling were chosen in the GT. According to statistical measures, the proposed M5 tree model predicts scour depth better than empirical approaches. Additionally, the developed ANN-PSO model is suitable for determining scour depth in both rectangular and circular shapes of piers. The results of both developed models are compared with other existing models and found to be satisfactory.

Development of automatic orbital pipe MIG welding system and process parameters’ optimization of AISI 1020 mild steel pipe using hybrid artificial neural network and genetic algorithm

Development of automatic orbital pipe MIG welding system and process parameters’ optimization of AISI 1020 mild steel pipe using hybrid artificial neural network and genetic algorithm

Compact modeling of metal–oxide TFTs based on the Bayesian search-based artificial neural network and genetic algorithm

Thin-film transistors play an important role in ultra-high definition displays. The conventional physical model requires a significant amount of time and resources, while its generalizability is limited. This paper introduces a method for quickly incorporating the characteristics of emerging devices into circuit simulations using an artificial neural network (ANN) model. The multi-layer perceptron (MLP) model, with a simple structure and high modeling efficiency, is employed as a typical ANN model. The pivotal step in using the MLP model is to determine its topology. This hyperparameter problem can be easily solved using Bayesian search. To improve the accuracy of the model, a genetic algorithm is proposed to optimize the initial values of the weights and biases. Furthermore, by introducing the first derivative in the loss function, small signal parameters can be taken into consideration. This modeling approach significantly reduces the time required for compact device modeling. Our experimental results exhibit a strong correlation between the model’s output and the corresponding experimental data, thereby providing comprehensive validation for the effectiveness of our proposed model. Once the trained model parameters were extracted, we implemented the hybrid ANN model using Verilog-A. This circuit-level experiment demonstrates that this hybrid ANN model can accurately estimate various physical devices and circuits.

Machine-learning-based performance prediction of the energy pile heat pump system

The coefficient of performance (COP) is one of the important indicators for evaluating ground source heat pump systems, and a certain amount of machine learning algorithms have been applied for COP prediction. However, most of the current models use a large number of input variables, and it may be difficult to obtain all the required parameters for diagnosis of the sensors. Most of the influencing factors contain fluid temperature, which is not easy to measure, and there is a risk of leakage when sensors are deployed in the system. In this work, two-year field tests were conducted based on an energy pile heat pump system, where in situ results were used as sample points, and the more easily measured ambient temperature and humidity, room temperature and humidity, and hourly power consumption were used as input parameters. Spearman correlation analysis is used to rank the influencing factors, and the prediction of COP was developed by a hybrid artificial neural network (ANN), optimized via the particle swarm optimization algorithm (PSO), and finally compared with empirical models and their updates. Results showed that among the input variables, the most and least important factors affecting the COP are the ambient temperature and ambient humidity, respectively. The prediction results of PSO-ANN were in the best agreement with the measured values, and the accuracy was higher than that of the empirical regression models. The proposed method noted in this study can be used as a supplement to the current COP prediction.