Multilayer Neural Network Model Research Articles

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model.

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine.

Reliability modeling and analysis of mixture of exponential distributions using artificial neural network

In recent years, statisticians have become more and more interested in the study of mixture models, especially in the last decade, without adequately considering the difficulty of modeling the reliability measures of mixture models using artificial neural networks. In this study, in which artificial neural networks and mixed model reliability criteria are analyzed, various reliability parameters are calculated considering different scenarios. In order to estimate the obtained numerical reliability parameters, a multilayer artificial neural network model has been developed. Seven different reliability parameter values have been obtained from the artificial neural network model designed with four input parameters. The prediction values obtained from the artificial neural network model developed with five neurons in the hidden layer have been compared with numerical data, and the performance of the model has been analyzed comprehensively. The mean squared error (MSE) value for the network model has been calculated as 1.98E‐08 and the R value as 0.99991. The results clearly revealed that the artificial neural network model developed using data from the appropriate statistical model is an excellent tool that can be used to estimate reliability measures.

Learning-based dynamic ticket pricing for passenger railway service providers

This article proposes a data-driven ticket dynamic pricing methodology for passenger railway service providers. There is a finite purchasing horizon, and the ticket prices should be set under varying conditions to affect the customer booking behaviour. A three-step process including machine learning and optimization tools is employed to maximize the revenue under a constrained train capacity. First, a multi-layer perceptron artificial neural network (MLP-ANN) model is proposed to predict the demand intensity due to seasonal situations using the ticket reservation data. Then, some regression models as price elasticity functions are used to quantify the effects of price, seasonal conditions and competition on the company’s sales. Finally, a nonlinear integer programming model is proposed to maximize the total revenue in the purchasing horizon. The results of the numerical studies on the Fadak Five-Star Trains’ reservation data indicate that the proposed methodology has high-grade potential to improve the service provider’s revenue.

Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence

This paper details the design process of a ternary NiTiHf shape memory alloy (SMA) with an austenite finish temperature (Af) beyond 400 °C. Specifically, available experimental data on the ternary NiTiHf SMA system was utilized to construct a database, which was employed to train and test a machine learning (ML) algorithm to predict the ideal NiTiHf SMA composition to exhibit an Af beyond 400 °C and a relatively smaller hysteresis. For this purpose, a multi-layer feedforward neural network (MLFFNN) model was proposed, trained, and tested. Consequently, the Ni49.7Ti26.6Hf23.7 and Ni50Ti27Hf23 alloys predicted by this ML algorithm were selected for validation experiments to assess the accuracy of the ML model’s predictions. As a result, the Ni49.7Ti26.6Hf23.7 alloy with an Af temperature of 403.5 °C and remarkable cyclic stability was established as a new NiTiHf SMA composition, which can be utilized in applications demanding reversible austenite-to-martensite phase transformation beyond 400 °C.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

On The Performance of Intrusion Detection Systems with Hidden Multilayer Neural Network using DSD Training

Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.

Data engineering for digital twining and optimization of naturally ventilated solar façade with phase changing material under global projection scenarios

The use of phase change materials (PCM) and its efficient integration can significantly lower the carbon emissions of building sector. However, optimal integration of thermal energy storages, including PCM, in the buildings is a challenging problem. Digital engineering offers a reliable mechanism to optimize such integration. In order to realize the usefulness of digital resources, especially on the global scale, a methodological approach is required based on validated and data-driven model(s) supported by optimal solution selection in the set of localized as well as external variables. This recommendation system, for the optimal solution, is exemplified here as a novel approach offering solution for an integrated solar chimney (naturally ventilated solar façade) for a building with passive energy storage based on PCM. The complete novel methodology is demonstrated for the sake of illustrating the procedure, followed by regional solutions and their impacts on a global scale. While the solution and results have significance in terms of their quantification; the real purpose and novelty of data engineering mechanism is the robustness and procedural illustration of the digital twin generation from the perspective of energy systems and building performances. The sequential procedure is summarized here with the case study of a solar chimney that has PCM storage in a building. The building is simulated for the four climatic zones covering a major chunk of the world map. The influencing variables are selected along with viable ranges for the prediction of optimal configurations. The multi-objective optimization leads to the region-specific characteristics of the geometry as well as phase-changing material (PCM) selections. While the response parameters have their own projections in their own specific climates. The results has indicated that multilayer perceptron artificial neural network model trained with Levenberg-Marquardt and Bayesian regularization algorithm having topology architecture 10-[10]-1 and 10-[40]-1 can predict the Air Changes per Hour (ACH) and energy efficiency by providing a coefficient of determination of 0.9997 and 0.9998, respectively. Sensitivity analysis has indicated that the solar radiation, width of the solar chimney, distance from the glazing to the wall, specific heat, and the temperature of fusion are the most significant to model the number of air changes per hour marking up to 16%, 11%, 11%, 17%, and 12%, respectively. Whereas wind velocity is the least sensitive parameter. The configuration of solar chimney with phase changing material can provide a ventilation rate of 4.00 1/h, 2.19 1/h, 4.9 1/h, and 2.85, while an energy efficiency of 37.6%, 40.33%, 39.35%, and 39.17% for tropical, dry, temperate, and continental climatic zone, respectively. The global predictions exemplify the impact of double-skin solar façade system with energy storage in the digital twin, thereby indicating the usefulness of digital data engineering.

Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing

A convolutional neural network has the characteristics of sharing information between layers, which can realize high-dimensional data processing. In general, the convolutional neural network uses a feedback mechanism to realize parameter self-regulation, which solves the disadvantages of manual parameter adjustment. However, it is unable to determine the iteration number with the best calculation accuracy. Calculation efficiency cannot be guaranteed while achieving the best accuracy. In this paper, a multilayer extreme learning convolutional neural network model is proposed for feature recognition and classification. Firstly, two-dimensional spatial characteristics of planetary bearing status data were enhanced. Then, extreme learning machine is embedded in a convolution layer to solve convex optimization problems. Finally, the parameters obtained from the training model were nested into a network to initialize the model parameters to separate each status feature. Planetary bearing experimental cases show the effectiveness and superiority of the proposed model in the recognition and classification of weak signals.

Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

The nonlinear dynamics of the determining factors of the morphometric characteristics of cracks in expansive soils make their typification a challenge, especially under field conditions. To overcome this difficulty, we used artificial neural networks to estimate crack characteristics in a Vertisol under field conditions. From July 2019 to June 2020, the morphometric characteristics of soil cracks (area, depth and volume), and environmental factors (soil moisture, rainfall, potential evapotranspiration and water balance) were monitored and evaluated in six experimental plots in a tropical semiarid region. Sixty-six events were measured in each plot to calibrate and validate two sets of inputs in the multilayer neural network model. One set was comprised of environmental factors with significant correlations with the morphometric characteristics of cracks in the soil. The other included only those with a significant high and very high correlation, reducing the number of variables by 35%. The set with the significant high and very high correlations showed greater accuracy in predicting crack characteristics, implying that it is preferable to have fewer variables with a higher correlation than to have more variables of lower correlation in the model. Both sets of data showed a good performance in predicting area and depth of cracks in the soils with a clay content above 30%. The highest dispersion of modeled over predicted values for all morphometric characteristics was in soils with a sand content above 40%. The model was successful in evaluating crack characteristics from environmental factors within its limitations and may support decisions on watershed management in view of climate-change scenarios.

Stability of biochar derived from banana peel through pyrolysis as alternative source of nutrient in soil: feedforward neural network modelling study.

Biochar derived from banana peels can be used as an alternative nutrient in the soil that can promote crop growth while reducing fertiliser usage. Biochar stability has proportional relationship to biochar residence time in the soil and potassium is one of the vital nutrients needed for plant growth. This research aims at providing optimum pyrolysis operating conditions like temperature, residence time, and heating rate using banana peels as feedstock. An electrical tubular furnace was used to conduct the pyrolysis process to convert banana peels into biochar. The elemental compositions of biochar are potassium, oxygen (O), and carbon (C) content. The O:C ratio was used as the biochar stability indicator. Analysis of results showed that operating temperature has the most remarkable effect on biochar yield, biochar stability, and biochar's potassium content. In addition, a multilayer feedforward artificial neural network model was developed for the pyrolysis process. Eleven training algorithms were selected to model the multi-input multi-output neural network (MIMO). The most suitable training algorithm was identified through four performance criterions which are root mean square error (RMSE), mean absolute error (MSE), mean absolute percentage error (MAPE), and regression (R2). The results show that the Levenberg-Marquardt backpropagation training algorithm has the lowest error. From the chosen training algorithm, neural network was trained, and optimum operating parameters for banana peel were predicted at 490°C, 110min, and 11°C/min with a high yield of 47.78%, O/C ratio of 0.2393, and 14.04 wt. % of potassium.

Prediction of floodwater impacts on vehicle blockages at bridges using artificial neural network

During extreme flood events, various debris like floating vehicles can block the bridges in urban rivers and floodplains. Blockage of vehicles can influence the floodwater hydrodynamics and potentially on the flood risk implications. Such obstructions often raise upstream water levels with back water effects, causing more water to be redirected into nearby metropolitan areas. This study attempts at evaluating artificial neural network (ANN) model in predicting the variations in floodwater depths and velocities along the channel centreline based on the changes in flowrate and distances from the inlet. The floodwater depth and velocity variations were obtained for three different types of bridges at specified sites along the channel centreline with three incoming discharges. A multilayer feedforward neural network (FFNN) model was used to investigate the effects of discharge (Q) and distance, on depth variation rate (D) or velocity (V). Additionally, a comparison study was done between 2 input 1 output and 2 input 2 output i.e. single output (depth variation rate (D) or velocity (V) versus multi-output depth variation rate (D) and velocity (V) for all the three models of bridges that are blocked by vehicles. The study has applied 12 training algorithms (TA) in the ANN modelling to optimize the TA that is most suitable for the dataset of three different bridges. The optimization is based on the performance criterion namely regression (R), mean squared error (MSE), mean absolute error (MAE), mean absolute percentage (MAPE), accuracy and coefficient of determinant (R2). Bayesian regularization backpropagation (BR) training algorithm gives a highest accuracy when compared in all three bridges. The scenario 2 input 2 output gave greatest accuracy results compared to 2 input 1 output. The findings showed a reliable estimation of significant impacts on the flow propagations and the hydrodynamic processes along rivers and floodplains. This study can help the decision makers in effective river and floodplain management practices.

Facial Emotion Classifier using Convolutional Neural Networks for Reaction Review

Applications of facial emotion classification is gaining popularity in the world. There are many ways to train a model to classify human facial expressions by use of existing technologies. The strategy to order and recognize feelings of an individual conveyed by his facial expression is done by contrasting it to a gathered set of labelled experiences of feelings. In this paper, we propose the making of an intelligent system that will recognize and classify facial emotions. A multi-layer Convolutional Neural Network model is proposed. Another method of training using pretrained ResNet50 Model is explored. A basic live video streaming application is developed to showcase the use case of our model which will be capable of monitoring and recording facial emotions in real time from a live video stream and subsequently summarize the overall reactions at the end of the stream.

Optimized biodiesel production from C. innophyllum bio-oil using Kriging and Ann predicitive models

This work aimed at optimizing the two-stage transesterification efficiency of the production of C. inophyllum biodiesel using artificial neural network and Kriging predictive models. Response surface methodology was used to develop the central rotatable composite design of 27 trial experimental runs with variations in the input process parameters like methanol to oil molar ratio, potassium hydroxide catalyst loading, and reaction time. A multi-layered non-linear regressive artificial neural network model with feed-forward propagation and a numerical surrogate Kriging model was used to predict the C. inophyllum biodiesel yield. The efficacy of the developed model was verified using analysis of variance by com-paring its coefficient of determination and the mean relative percentage deviation values. The optimized C. inophyllum biodiesel as 98.1% is derived with 0.94 v/v of methanol to oil molar ratio, 0.98 wt.% of potassium hydroxide catalyst loading, and 80 minutes reaction time with 70?C constant reaction temperature as predicted by Kriging model. The optimized parameters were also verified experimentally.

Multilayer Network-Based CNN Model for Emotion Recognition

Human emotions are an important part in daily life. In this paper, a novel multilayer network-based convolutional neural network (CNN) model is proposed for emotion recognition, from multi-channel nonlinear EEG signals. Firstly, in response to the multi-rhythm properties of brain, a multilayer brain network with five rhythm-based layers are derived, where each layer can pertinently describe one specific frequency band. Subsequently, a novel CNN model is carefully designed, which uses the multilayer brain network as input and allows deep learning of the classifiable nonlinear features from the channel and frequency views. Moreover, one DenseNet model is developed as another branch to study time-domain nonlinear features from the EEG signals. All the learned features are eventually concatenated together for emotion recognition. Publicly available SEED dataset is used to test the proposed method, and it shows good results on all 15 subjects, with average accuracy of 91.31%. Our method builds a bridge between the multilayer network and deep learning, suggesting an effective approach for analyzing multivariate nonlinear time series, especially multi-channel EEG signals.

Driver Lane Change Intention Recognition Based on Attention Enhanced Residual-MBi-LSTM Network

Accurate identification of lane-changing intention can effectively assist intelligent driving vehicles in terms of decision-making and trajectory planning, which plays a significant role in enhancing driving safety by reducing traffic accidents caused by lane-changing. Based on the trajectory characteristics and vehicle interaction information, an attention-enhanced bidirectional multi-layer residual long-short term memory neural network (Attention Enhanced Residual-MBi-LSTM) model is proposed for lane change intention recognition in this paper. Firstly, an EWMA filter is employed to smooth the noisy data collected from the vehicle. Then a four-layer bidirectional residual LSTM memory (Residual-MBi-LSTM) structure is used to extract lane-changing features from the historical driving trajectories of ego-vehicle and vehicle interaction information. Besides, the attention mechanism is added to adjust the weight of data in different time frames. After that, the current lane-changing probability is calculated and output by the Softmax function. Finally, the vehicle lane-changing intention recognition model is firstly trained and then verified in the HighD dataset. According to the HIL experiment, the proposed model has the ability to identify driver intention on average of 2.07 seconds in advance.

College English Online Teaching Model Based on Deep Learning

In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.

Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection

Epilepsy makes the patients suffer great pain and has a very bad impact on daily life. In this paper, a novel method is proposed to implement electroencephalogram (EEG)-based epilepsy detection, in which multi-frequency multilayer brain network and deep learning are jointly utilized. Firstly, based on the multi-frequency characteristics of brain, we construct a multilayer brain network from the multi-channel EEG signals. The time, frequency and channel-related information from EEG signals are all mapped into the multilayer network topology, making it an effective feature for epilepsy detection. Subsequently, with multilayer brain network as input, a multilayer deep convolutional neural network (MDCNN) model is designed. MDCNN model has two blocks and uses a parallel multi-branch design in the first block, which exactly matches the multilayer structure of the proposed brain network. The experimental results on publicly available CHB-MIT datasets show that the proposed method can accurately detect epilepsy, with a high average accuracy of 99.56%, sensitivity of 99.29%, and specificity of 99.84%. All these provide an efficient solution for characterizing the complex brain states using multi-channel EEG signals.

Artificial Neural Network Models for Predicting and Optimizing the Effect of Air-frying Time and Temperature on Physical, Textural, Sensory, and Nutritional Quality Parameters of Fish Ball

ABSTRACT The study was conducted to see the effect of air-frying time and temperature on physical, textural, sensory and nutritional quality parameters of air-fried fish balls and compared with the quality parameters of deep-fried fish balls. Multilayer feed-forwarded artificial neural network (ANN) models were fitted to the experimental data to predict the response variables of air-fried fish balls as a function of frying temperature and time. The model was validated using the holdback method. Based on the R2 and RMSE values, ANN model with three hidden layers was found to be best fitted model to explain the variability in the proximate composition, texture and sensory data. The desired air-frying condition obtained was temperature at 200 oC and time at 8 minutes by multi-response desirability score. The air-fried fish balls at desired condition had 81 % reduction in fat content and 17 % increase in protein content compared to deep fried sample.

Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model

Recently, artificial neural networks have been successfully applied in many areas such as forecasting financial time series, predicting financial failure, and classification of ratings. However, it has hardly been applied in forecasting sukuk prices, which is considered the most common Islamic capital market instrument. Since sukuk is a new financial asset, there are not enough studies in this area. Therefore, this study aims to forecast the Turkish sovereign sukuk prices using with artificial neural network model and to reveal the determinants in the forecasting of sukuk prices. For this purpose, a multi-layer feed forward artificial neural network model is designed using dollar-based international sovereign sukuk price data issued by the Turkish Ministry of Treasury and Finance. The dollar index, volatility index, geopolitical risk index, Standard and Poor’s Middle East and North Africa sukuk index, and Eurobond prices constituted as input variables of the designed model and the sovereign sukuk prices formed the output. As a result, the sovereign sukuk prices were forecasted accurately at the success rate of 99.98%. The accurate forecasting of sukuk prices will play a critical role in reducing the risk perception of sukuk investors and increasing their profitability. The findings of the study are important in terms of proving that the artificial neural network model is an effective model for forecasting the sukuk prices and revealing that the dollar index, volatility index, geopolitical risk index, Standard and Poor’s MENA sukuk index, and Eurobond prices are determinants in forecasting sukuk prices.

AI in predicting COPD in the Canadian population

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Canadians aged 35 years or older are living with COPD. Primary care is often the first contact an individual will have with the healthcare system providing acute care, chronic disease management, and services aimed at health maintenance. This study used Electronic Medical Record (EMR) data from primary care clinics in seven provinces across Canada to develop predictive models to identify COPD in the Canadian population. The comprehensive nature of this primary care EMR data containing structured numeric, categorical, hybrid, and unstructured text data, enables the predictive models to capture symptoms of COPD and discriminate it from diseases with similar symptoms. We applied two supervised machine learning models, a Multilayer Neural Networks (MLNN) model and an Extreme Gradient Boosting (XGB) to identify COPD patients. The XGB model achieved an accuracy of 86% in the test dataset compared to 83% achieved by the MLNN. Utilizing feature importance, we identified a set of key symptoms from the EMR for diagnosing COPD, which included medications, health conditions, risk factors, and patient age. Application of this XGB model to primary care structured EMR data can identify patients with COPD from others having similar chronic conditions for disease surveillance, and improve evidence-based care delivery.