Three-layer Neural Network Model Research Articles

A two-stage accelerated search strategy for large-scale multi-objective evolutionary algorithm

Since large-scale multi-objective problems (LSMOPs) have huge decision variables, the traditional evolutionary algorithms are facing difficulties of low exploitation efficiency and high exploration costs in solving LSMOPs. Therefore, this paper proposes an evolutionary strategy based on two-stage accelerated search optimizers (ATAES). Specifically, a convergence optimizer is devised in the first stage, while a three-layer lightweight convolutional neural network model is built, and the population is homogenized into two subsets, the diversity subset, and the convergence subset, which serve as input nodes and the expected output nodes of the neural network, respectively. Then, by constantly backpropagating the gradient, a satisfactory individual will be produced. Once exploitation stagnation is discovered in the first phase, the second phase will be run, where a diversity optimizer using a differential optimization algorithm with opposite learning is suggested to increase the exploration range of candidate solutions and thereby increase the population's diversity. Finally, to validate the algorithm's performance, on multi-objective LSMOP and DTLZ benchmark suits with decision variable quantities of 100, 300, 500, and 1000, the ATAES demonstrated its superiority with other advanced multi-objective evolutionary algorithms.

A novel two-layer fuzzy neural network for solving inequality-constrained [formula omitted]-minimization problem with applications

In this paper, we propose a novel two-layer fuzzy neural network model (TLFNN) for solving the inequality-constrained ℓ1-minimization problem. The stability and global convergence of the proposed TLFNN model are detailedly analyzed using the Lyapunov theory. Compared with the existing three-layer neural network model (TLNN) recently designed by Yang et al., the proposed TLFNN model possesses less storage, stronger robustness, faster convergence rate and higher convergence accuracy. These advantages are illustrated by some numerical experiments, where it is shown that the TLFNN model can achieve a convergence accuracy of 10−13 within 5s while the TLNN model can only acquire 10−6 in 105s when some random coefficient matrices are applied. Since the linear equality-constrained conditions can be equivalently transformed into double inequality-constrained ones, some simulation experiments for sparse signal reconstruction show that the proposed TLFNN model also has less convergence time and stronger robustness than the existing state-of-the-art neural network models for the equality-constrained ℓ1-minimization problem.

Comparative study of integer-order and fractional-order artificial neural networks: Application for mathematical function generation

This paper investigates the impact of fractional derivatives on the activation functions of an artificial neural network (ANN). Based on the results and analysis, a three-layer backpropagation neural network model with fractional and integer derivatives employed in the activation function and fractional gradient descent method backpropagation learning algorithm has been proposed. Specifically, three perceptrons have been proposed based on fractional and integer derivatives applied to activation functions and learning algorithms. They are fractional derivative activation function (FDAF) perceptron, integer derivative activation function (IDAF)-fractional derivative learning algorithm (FDLA) perceptron, and fractional derivative learning algorithm (FDLA) perceptron. The Riemann-Liouville (RL) fractional derivative, Grunwald-Letnikov (GL) derivative, Caputo-Fabrizio (CF) fractional derivative, Caputo (C) fractional derivative, and Atangana-Baleanu (AB) fractional derivative have been employed. The impact of these derivatives’ fractional-order (FO) is investigated in a wide range from 0.1−0.9 on testing mean square error (MSE) and time required to train the perceptron. FO-based and IO-based perceptrons are compared with the help of performance metrics such as testing MSE and the time required to train the models. The training and testing simulation results illustrate that Caputo-Fabrizio derivative-based perceptrons outperform other fractional derivatives. Also, the performance of the FO-based perceptron is better in terms of the least MSE.

An Artificial Neural Network Method for Forecasting the Stability of Soil Slopes

Artificial neural network (ANN) methods, based on sophisticated models, have been developed recently that can predict slope stability. In this study, we have developed a genetic algorithm (GA) based on ANN to assess the stability of soil slope. Firstly, an ANN-based genetic algorithm was trained for nonlinear input-output mapping of the slope. A total of 190 soil slopes with unique values of shear strength properties (friction angle, cohesion, and unit weight), geometric parameters (slope angle and slope height), and corresponding factor of safety (FS) have been collected to give a neural network training dataset. Then, a three-layer neural network model is established based on GA. The prediction and performance ability of the established model is assessed using the correlation coefficient (R2). By the outcomes, the trained ANN model with the R2 value of 0.98 is reliable, valid, and simple for evaluating the soil slope stability and estimating the FS. Additionally, the proposed neural network model is applied to a case of soil slope from prior studies. Findings show that the developed ANN model can be versatile in studying the stability of soil slopes.

Light-Stimulated IGZO Transistors with Tunable Synaptic Plasticity Based on Casein Electrolyte Electric Double Layer for Neuromorphic Systems.

In this study, optoelectronic synaptic transistors based on indium-gallium-zinc oxide (IGZO) with a casein electrolyte-based electric double layer (EDL) were examined. The casein electrolyte played a crucial role in modulating synaptic plasticity through an internal proton-induced EDL effect. Thus, important synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, and spike rate-dependent and spike number-dependent plasticity, were successfully implemented by utilizing the persistent photoconductivity effect of the IGZO channel stimulated by light. The synergy between the light stimulation and the EDL effect allowed the effective modulation of synaptic plasticity, enabling the control of memory levels, including the conversion of short-term memory to long-term memory. Furthermore, a Modified National Institute of Standards and Technology digit recognition simulation was performed using a three-layer artificial neural network model, achieving a high recognition rate of 90.5%. These results demonstrated a high application potential of the proposed optoelectronic synaptic transistors in neuromorphic visual systems.

Feasibility of measuring moisture content of green sand by a low frequency multiprobe detector based on dielectric characteristics

Green sand is a mixture of silica sand, bentonite, water and coal powder, and other additives. Moisture content is an important index to characterize the properties of green sand. Based on the dielectric characteristics of green sand and transmission line theory, a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed. A system was constructed, where six detectors with different arrangements and probes were designed. The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz. A voltage difference platform occurs in the range of 29–35 MHz, which is suitable for measuring the moisture content due to its insensitivity to frequency. The electric field intensity gradually decreases with the increase of the probe depth, and the intensity of central probe is always greater than that of the edge probe. When the distance of the probe away from the sand sample surface is 80 mm, the electric field intensity of the edge probe is found to be very weak. The optimal excitation frequency for measuring the moisture content of green sand is 29–33 MHz. The optimal detector is the one with one center probe and three edge probes, and their lengths are 80 mm and 60 mm, respectively. The distance between the center and edge probes is 25 mm, and the diameter of probes is 5 mm. Taking the voltage difference of transmission line, bentonite content, coal powder content and compactability as parameters of the input layer, and the moisture content as a parameter of the output layer, a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz. The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%–7wt.%.

Retracted: Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model.

[This retracts the article DOI: 10.1155/2022/6558512.].

Insights into brewed tea waste as a green and low-priced adsorbent for solid-phase extraction of Cd(II) ions: isotherm, kinetic, and artificial neural network approach

In the present research, brewed tea waste (BTW) was utilized as a green, low-priced, and abundant adsorbent for separation/preconcentration of Cd(II) ions through solid-phase extraction method from water and foods for the first time. BTW was applied as a natural adsorbent, without using any chelating agent to bind Cd(II) ions or any chemical reagent for its modification. A three-layer artificial neural network model using backpropagation algorithm was utilized to explicate a prediction model for the extraction performance of Cd(II) ions by selecting the input parameters as solution pH, quantity of BTW, sample volume, eluent concentration and volume, and equilibrium time for desorption. The preconcentration factor, relative standard deviation, and detection limit were attained as 100, 3.03%, and 0.56 µg L−1, respectively. It was decided that the Langmuir isotherm model is acceptable to characterize the retention of Cd(II) ions on BTW. This result pointed out that the active binding sites on the BTW surface are homogeneously distributed. Adsorption capacity of BTW was achieved as 41.5 mg g−1 which is higher than several expensive and difficult-to-prepare adsorbents. Adsorption kinetics was elucidated by pseudo-second order kinetic model. After confirmed the accuracy of the method with spike/recovery studies, it was employed for Cd(II) determination in water (stream and sea water) and food (eggplant, lettuce, parsley, apple, and apricot) samples with high accuracy. The inferences of the study proved that the BTW offers a magnificent application prospect in the extraction of Cd(II) ions.

Visual assessment of interactions among resuscitation activity factors in out-of-hospital cardiopulmonary arrest using a machine learning model.

AimThe evaluation of the effects of resuscitation activity factors on the outcome of out-of-hospital cardiopulmonary arrest (OHCA) requires consideration of the interactions among these factors. To improve OHCA success rates, this study assessed the prognostic interactions resulting from simultaneously modifying two prehospital factors using a trained machine learning model.MethodsWe enrolled 8274 OHCA patients resuscitated by emergency medical services (EMS) in Nara prefecture, Japan, with a unified activity protocol between January 2010 and December 2018; patients younger than 18 and those with noncardiogenic cardiopulmonary arrest were excluded. Next, a three-layer neural network model was constructed to predict the cerebral performance category score of 1 or 2 at one month based on 24 features of prehospital EMS activity. Using this model, we evaluated the prognostic impact of continuously and simultaneously varying the transport time and the defibrillation or drug-administration time in the test data based on heatmaps.ResultsThe average class sensitivity of the prognostic model was more than 0.86, with a full area under the receiver operating characteristics curve of 0.94 (95% confidence interval of 0.92–0.96). By adjusting the two time factors simultaneously, a nonlinear interaction was obtained between the two adjustments, instead of a linear prediction of the outcome.ConclusionModifications to the parameters using a machine-learning-based prognostic model indicated an interaction among the prognostic factors. These findings could be used to evaluate which factors should be prioritized to reduce time in the trained region of machine learning in order to improve EMS activities.

Probabilistic Evaluation of Tunnel Boring Machine Penetration Rate Based on Case Analysis

Although geological parameters are known to affect the penetration rate (PR) of a tunnel boring machine (TBM), their relation to the probability of TBM PR has been rarely considered. In this article, a probabilistic evaluation model of TBM PR was proposed. Firstly, the marginal distributions of five geological parameters were confirmed by mathematical statistics. Then Copula theory was used to construct a five-dimensional joint probability distribution of the geological parameters in line with the marginal distributions. Next, the collected geological parameters were utilized to train a three-layer backpropagation neural network (BPNN) model for predicting the TBM PR. Finally, A Copula—BPNN coupled model was built for estimating the probability of TBM PR, and a Weibull distribution function of the predicted TBM PR was obtained through Monte Carlo simulation. Considering the uncertainty, correlation, and multi-factor influence, this paper realized the probabilistic evaluation of TBM PR. Discussion on the parameter uncertainty and independence shows that the variability of the geological parameters is necessary in TBM PR prediction. Quantitative probability estimation of the TBM PR can help with optimizing the driving parameters under different geological conditions to improve construction efficiency.

GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.

DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.

Computer-assisted in vitro reconstitution of purine degradation pathway to lower the purine content in food.

With the increasing prevalence of gout and its etiological hyperuricemia, dietary control of gout based on low-purine food according to patients' eating habits is becoming a better choice compared to the existing drug treatment such as allopurinol with notorious side effects. Reconstructing the purine metabolic pathway in vitro to degrade purine substances in food into natural functional allantoin appears to be an innovative method for preparing nutritious and healthy food of low purine content. The present study reports a computer-assisted in vitro reconstruction of four purinolytic enzymes metabolizing adenosine into allantoin to reduce purine content in food for personalized dietary control of hyperuricemia and gout. Under the optimum reaction conditions of 40 °C and pH7, 0.1U of enzymes and 0.5 mmol L-1 adenosine determined by an orthogonal test design, 16 different enzyme complexes were experimentally tested. The tested enzyme composition and allantoin production values were used as input and output to build a three-layer back propagation artificial neural network (BP-ANN) model, which was further optimized by a genetic algorithm (GA). The optimum enzyme complex predicted by the GA-BP-ANN model produced 248.08±7.832 μmol L-1 allantoin, which was 19.9% higher than equimolar mixture of enzymes, and also more efficiently lowered purine contents in beer, as well as beef and yeast extracts. This is the first in vitro reconstitution of complete purine metabolic pathway by combining ANN and GA technologies, with successful application with respect to lowering the purine content in food, indicating a promising application of computer-assisted in vitro reconstitution of purinolytic pathway in low-purine food to prevent hyperuricemia and gout. © 2022 Society of Chemical Industry.

Exploring thermal contact conductance between two contact solids by artificial neural network

Realizing accurate measurement for thermal contact conductance (TCC) is difficult and needs many additional measurements. In this paper, we design a three-layer back propagation artificial neural network (ANN) model to retrieve TCC of two contact solid specimens. The model can retrieve TCC only according to the measured temperatures and loading pressures, and the effects of loading pressure, temperature and surface roughness on TCC are considered. Besides, thermal conductivity, heat flux and the parameter of the TCC empirical fitting formula can be obtained simultaneously. The retrieved results are validated by experimental values. The results show that for two contact pairs with different surface roughness, the retrieved parameters of TCC empirical fitting formula are clear reasonable and can be explained conceptually compared with the published studies.

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model.

The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout.

Predictive Study of Flow-Accelerated Corrosion Characteristic Parameters Based on the Neural Network

Corrosion of equipment by corrosive media is widespread in the processing of inferior crude oil. In hydroprocessing reactor effluent systems, corrosive media are very destructive to heat exchangers and air coolers during flow and cooling because of the high-temperature and -pressure environment. A fire and explosion in the air cooler or heat exchanger are highly likely when their tubes leak. Currently, there are no effective direct detection and prediction means to evaluate the corrosion risk in real time, creating significant hidden threats to the safe operation of the equipment. Therefore, this paper proposes a condition expansion method based on a Gaussian distribution. The distribution laws of characteristic corrosion parameters under various working conditions were studied, and the corrosion risk of the equipment was evaluated. A three-layer back-propagation neural network model is constructed to predict the characteristic corrosion parameters. After testing, the model is shown to have superior predictive accuracy and generalization performance. It can also meet the demand for real-time equipment corrosion prediction. The proposed method can serve an essential role in guiding engineers to take correct and timely prevention and control measures for different degrees of corrosion to reduce losses.

Programmable chalcogenide-based all-optical deep neural networks

Abstract We demonstrate a passive all-chalcogenide all-optical perceptron scheme. The network’s nonlinear activation function (NLAF) relies on the nonlinear response of Ge2Sb2Te5 to femtosecond laser pulses. We measured the sub-picosecond time-resolved optical constants of Ge2Sb2Te5 at a wavelength of 1500 nm and used them to design a high-speed Ge2Sb2Te5-tuned microring resonator all-optical NLAF. The NLAF had a sigmoidal response when subjected to different laser fluence excitation and had a dynamic range of −9.7 dB. The perceptron’s waveguide material was AlN because it allowed efficient heat dissipation during laser switching. A two-temperature analysis revealed that the operating speed of the NLAF is ≤ 1 $\le 1$ ns. The percepton’s nonvolatile weights were set using low-loss Sb2S3-tuned Mach Zehnder interferometers (MZIs). A three-layer deep neural network model was used to test the feasibility of the network scheme and a maximum training accuracy of 94.5% was obtained. We conclude that combining Sb2S3-programmed MZI weights with the nonlinear response of Ge2Sb2Te5 to femtosecond pulses is sufficient to perform energy-efficient all-optical neural classifications at rates greater than 1 GHz.

Machine Learning-Based Intelligent Scoring System for English Essays under the Background of Modern Information Technology.

This work is to reduce the workload of teachers in English teaching and improve the writing level of students, so as to provide a way for students to practice English composition scoring independently and satisfy the needs of college teachers and students for intelligent English composition scoring and intelligently generated comments. In this work, it firstly clarifies the teaching requirements of college English classrooms and expounds the principles and advantages of machine learning technology. Secondly, a three-layer neural network model (NNM) is constructed by using the multilayer perceptron (MLP), combined with the latent Dirichlet allocation (LDA) algorithm. Furthermore, three semantic representation vector technologies, including word vector, paragraph vector, and full-text vector feature, are used to represent the full-text vocabulary of English composition. Then, a model based on the K-nearest neighbors (kNN) algorithm is proposed to generate English composition evaluation, and a final score based on the extreme gradient boosting (XGBoost) model is proposed. Finally, a model dataset is constructed using 800 college students' English essays for the CET-4 mock test, and the model is tested. The research results show that the semantic representation vector technology proposed can more effectively extract the lexical semantic features of English compositions. The XGBoost model and the kNN algorithm model are used to score and evaluate English compositions, which improves the accuracy of the scores. This makes the management of the entire scoring model more efficient and more accurate. It means that the model proposed is better than the traditional model in terms of evaluation accuracy. This work provides a new direction for the application of artificial intelligence technology in English teaching under the background of modern information technology.

Mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning.

A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.

Evaluating Effectiveness of Low-Carbon Transition Policy Mix Based on Urban Private Car Trajectory Data

Under the vision of achieving carbon neutrality by 2060, it is urgent to introduce appropriate carbon reduction policy for city road traffic. This paper establishes a three-layer neural network model to predict the carbon emission from private cars based on urban private car trajectory data, simulates and analyzes the carbon emission from private cars, travel cost, personal income, and government revenue under the four policy perspectives, and evaluates and compares the emission reduction effects under four policy perspectives. Next, this paper evaluates the government revenue from the perspective of carbon tax and policy mix and compares the individual consumer utility of two-commodity and three-commodity mix, as well as the total social benefits under the four policy perspectives. The results show that the policy mix has better implementation effect on carbon emission reduction, personal income, and travel cost. The implementation effect of the single carbon tax policy is better in terms of government revenue. The implementation effect of the single carbon trading policy is better in terms of social benefit. In addition, as the carbon tax rate increases, the consumer utility tends to decline. Finally, this paper puts forward specific policy implementation proposals based on the above simulation analysis.

Development of NLP-Integrated Intelligent Web System for E-Mental Health.

As the COVID-19 pandemic continues, the need for a better health care facility is highlighted more than ever. Besides physical health, mental health conditions have become a significant concern. Unfortunately, there are few opportunities for people to receive mental health care. There are inadequate facilities for seeking mental health support even in big cities, let alone remote areas. This paper presents the structure and implementation procedures for a mental health support system combining technology and professionals. The system is a web platform where mental health seekers can register and use functionalities like NLP-based chatbot for personality assessment, chatting with like-minded people, and one-to-one video conferencing with a mental health professional. The video calling feature of the system has emotion detection capabilities using computer vision. The system also includes downloadable prescription facilities and a payment gateway for secure transactions. From a technological aspect, the conversational NLP-based chatbot and computer vision-powered video calling are the system's most important features. The system has a documentation facility to analyze the mental health condition over time. The web platform is built using React.js for the frontend and Express.js for the backend. MongoDB is used as the database of the platform. The NLP chatbot is built on a three-layered deep neural network model that is programmed in the Python language and uses the NLTK, TensorFlow, and Keras sequential API. Video conference is one of the most important features of the platform. To create the video calling feature, Express.js, Socket.io, and Socket.io-client have been used. The emotion detection feature is implemented on video conferences using computer vision, Haar Cascade, and TensorFlow. All the implemented features are tested and work fine. The targeted users for the platform are teenagers, youth, and the middle-aged population. Mental health-seeking is still considered taboo in some societies today. Apart from basic established facilities, this social dilemma of undergoing treatment for mental health is causing severe damage to individuals. A solution to this problem can be a remote platform for mental health support. With this goal in mind, this system is designed to provide mental health support to people remotely from anywhere worldwide.