Hybrid Artificial Neural Network Model Research Articles

Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion

This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (qu) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (DR) significantly affects the variation in the bearing capacity factor (Nγ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on Nγ at higher Q values, and the unit weight (γ) changes minimally impact Nγ within the range of 16–22 kN/m3. Additionally, an increase in the critical state friction angle (ϕcv) consistently increases Nγ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (Nγ).

Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.

Development and application of a hybrid artificial neural network model for simulating future stream flows in catchments with limited in situ observed data

Development and application of a hybrid artificial neural network model for simulating future stream flows in catchments with limited <i>in situ</i> observed data

Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume

The construction of durable and sustainable infrastructure requires the use of industrial byproducts such as fly ash (FA) and silica fume (SF) to enhance strength and durability. This study introduces novel machine learning models to forecast the results of rapid chloride penetration test (RCPT) for self-compacting concrete (SCC) containing high volumes of FA and SF. This study assessed the effects of supplementary cementitious materials (SCMs) and elevated temperature curing on the RCPT outcomes for SCC. Various metaheuristic algorithms including teaching–learning-based optimization (TLBO), ant colony optimization (ACO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE)—optimize the learning rate, weights, and biases of artificial neural network (ANN) models. A dataset of 360 experimental RCPT data points with seven input parameters was used to train and test the hybrid models. The accuracy of these models was assessed using eight performance indices, and the results were further analyzed through rank analysis, scatter plots, and error matrices. The training and testing sets for the AI models, specifically ANN-TLBO, exhibit a strong correlation between the experimental and predicted RCPT values, with R2 values of 0.9962 for training and 0.9676 for testing, compared to the R2 values of the other proposed models. Consequently, the results of this study suggest that the ANN-TLBO model is the optimal hybrid ANN model for predicting RCPT values in SCC. Verified experimental results and external validation indicate that the ANN-TLBO model is an effective alternative for accurately predicting real-time RCPT in SCC.

Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models.

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.

Experimental analysis of forming forces in incremental forming and its prediction by AI-based methods

Single point incremental forming (SPIF) is an emerging approach to manufacture the user-specific parts of greater strength economically without involving dedicated tools and dies. In this study, peak deforming loads have been measured and estimated in order to determine the size and capacity of forming machinery for manufacturing components, using the SPIF process, for designed process conditions on AA7075-O sheets. Estimation of maximum deforming loads has been accomplished with the help of hybrid artificial neural network (HANN) approach by taking experimental load values as the input dataset. The load values, estimated by HANN approach, have been compared with the load values estimated by two state-of-art regression approaches, that is, Support Vector Machine (SVM) and Gaussian Process Regression (GPR). Results show that the regression methods (SVM and GPR) are outperformed by the HANN model with an accuracy of 99.80%. HANN model estimated force values very close to experimental results. The forming forces were found to be greater when the flat-end tools were used as a forming agent in place of hemispherical-end tool. The proposed study would help the researchers and scientists to assess the capacity the machines used in SPIF process to prepare a particular sheet-part and to prevent the failure of hardware during the SPIF process.

A novel COVID-19 infection-forecasting model based on artificial neural networks

The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.

Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading

Determining the bearing capacity of a strip footing under inclined loading is crucial in designing foundations. Due to the complex correlations, the subject area remains predominantly unexplored, or it has been simulated using only limited datasets. This paper presents the development of a prediction model based on machine learning (ML), leveraging advanced hybrid artificial neural network (ANN) models for estimating the bearing capacity of strip footings under inclined loading. The ANN models are hybridized with four different optimization algorithms, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE), which enhance the accuracy and efficiency of the predictive capabilities of ANN. The models are trained on a dataset of 920 records, and their performance is evaluated using a range of significant performance metrics. The ANN-ICA model achieved the highest rank in the score analysis (R2 =0.912, RMSE=0.165 in testing), followed by ANN-ALO and ANN-ACO. To reinforce the trustworthiness of the predictions, external validation is employed, and visual analysis is conducted using the Taylor diagram. The findings suggest that the proposed models are robust, and the incorporation of optimization techniques has improved the performance of traditional ANNs. The research findings have significant implications for the field of geotechnical engineering, providing engineers and researchers with valuable insights into the applicability of hybrid artificial neural network (ANN) models and alternative machine learning (ML)-based prediction models in assessing the bearing capacity of strip footings.

Short-term Electric Energy Load Forecasting of Ankara Region Using Artificial İntelligence Methods

While energy, which is an indispensable part of everyday life, maintains its importance and place in socio-economic structures of countries, importance of electricity energy, as an important component of energy, is increasing its gravity exponentially. Importance of electricity energy demand maintains its increasing trend in line with growing population, urbanization, industrialization, becoming widespread of technology and welfare. While meeting electricity energy demand, correct and effective load forecasting is needed in order to ensure operational safety without losing the stability of voltage, frequency and power flows within the limits determined under the real time conditions of electric power systems, operate and plan secure and low-cost electricity transmission systems and supply sustainable, reliable, high quality and affordable electricity to consumers. In recent years, use of the artificial intelligence models are quite common in many areas of the power system analysis and control, static and dynamic security analysis, dynamic load modelling and alarm processing and error diagnostics. Therefore, in this study, hybrid artificial neural network model optimized with genetic algorithm has been proposed to perform short-term load forecast since fairly good forecasting results are being obtained especially for non-linear complex problems. The proposed short-term load forecasting model has been tested by making 24 hours load forecasting using actual load data of Ankara Region in Turkey. The results obtained from the proposed model have been compared with classical ANN. In this study, for short-term electric load forecasting based on artificial neural networks and genetic algorithm an adaptive hybrid system has been proposed. Using artificial neural network based on Genetic Algorithm (GA) a new approach for short-term electric load forecasting has been developed. This proposed hybrid algorithm has been implemented for short-term load forecasting. In the proposed model, the actual forecast was made by ANN, Genetic Algorithm is used to select the most appropriate activation function for each node of ANN. Thus, it has been observed that network error decreased even more than classical ANN. The results show that hybrid ANN gives better result than classical ANN. So, the accuracy of the proposed model for short-term load forecasting has been increased. These results showed that the proposed model has a higher accuracy rate than the classical ANN in the short-term load forecasting.

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.

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

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.

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

&lt;span lang="EN-US"&gt;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.&lt;/span&gt;

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.

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.

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.

Socio-economic correlation analysis and hybrid artificial neural network model development for provincial waste electrical and electronic equipment generation forecasting in China

Ability to forecast the waste electrical and electronic equipment (WEEE) generation can help formulate a robust future WEEE management system. Previous studies applied forecasting models, such as the grey model and artificial neural networks, to predict WEEE generation from a country perspective, leading to less accurate forecasts due to huge socio-economic differences in rural and urban areas. Additionally, there has been the incompatibility of a single forecasting model for all WEEE types, and this remained a research gap. Taking advantage of respective forecasting models, this study presents a hybrid model, Grey Artificial Neural Network, to forecast the WEEE generation of 31 province-level regions in China while evaluating the socio-economic analysis of seven WEEE types via Pearson correlation analysis. More than 70% of WEEE from province-level regions strongly correlates (R > ±0.8) with the gross domestic product and the population, whereas some top WEEE-generating province-level regions (i.e., Tianjin and Shanghai) correlate weakly to moderately. The root mean square error and mean absolute error of the developed Grey Artificial Neural Network hybrid model are the lowest at 8.29 and 6.48, compared to the grey model (13.53 and 11.13) and back-propagation neural network (9.21 and 7.22). Though the Grey Artificial Neural Network hybrid model has the lowest error, posterior mean square deviation ratio analysis indicates that this hybrid model is only suitable for washing machines, refrigerators, color televisions, and personal computers (urban area), while the back-propagation neural network is suitable for monochrome televisions, air conditioners, and personal computers (rural area). Compared to 2019, it is projected that an additional 32.92 M units of WEEE will be generated by 2025, suggesting that China should build at least 15 extra recycling centers (14% more based on 2016) to handle the increased WEEE generation. This study provides policy implications for effective WEEE monitoring and collection systems to build resilient WEEE management.

Accurate Estimation of PEMFC State of Health using Modified Hybrid Artificial Neural Network Models

Accurate Estimation of PEMFC State of Health using Modified Hybrid Artificial Neural Network Models

Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.

The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete.

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

Joining of aluminum (Al) and copper (Cu) materials used in aerospace, automotive, chemistry, nuclear, electrical-electronic and energy sector with traditional fusion welding methods has some negative consequences. One of the major negativities is the serious decrease in the mechanical properties of the materials as a result of the high temperature that occurs during welding. Therefore, Friction Stir Welding (FSW) is of great importance in joining materials which are dissimilar and have low melting temperatures. In this study, a hybrid artificial neural network (ANN) model has been developed to predict the relationship between FSW parameters and ultimate tensile strength and to increase the prediction performance of ANN using heuristic algorithms by welding Al and Cu plates with FSW. The used hybrid approach consists of ANN and Particle Swarm Optimization/Teaching Learning Based Optimization techniques. The welding applications conducted with three different welding parameters. These parameters, rotation speed (525, 1025, 1525 rpm), welding speed (50, 75, 100 mm/min) and tool offset (0–0.75–1.5 mm) were determined as input parameters of the developed ANN model. Tensile and microhardness tests were applied to determine the mechanical properties in the experimental studies after welding process. In addition, Scanning Electron Microscope analysis and X-ray diffraction were used for microstructure and characterization of the fracture surface and the results obtained were interpreted and examined.