Design Of Neural Network Model Research Articles

Comparative study of typical neural solvers in solving math word problems

In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing challenges in fair and effective performance evaluation. This paper presents a comparative study of representative neural solvers, aiming to elucidate their technical features and performance variations in solving different types of MWPs. Firstly, an in-depth technical analysis is conducted from the initial deep neural solver DNS to the state-of-the-art GPT-4. To enhance the technical analysis, a unified framework is introduced, which comprises highly reusable modules decoupled from existing MWP solvers. Subsequently, a testbed is established to conveniently reproduce existing solvers and develop new solvers by combing these reusable modules, and finely regrouped datasets are provided to facilitate the comparative evaluation of the designed solvers. Then, comprehensive testing is conducted and detailed results for eight representative MWP solvers on five finely regrouped datasets are reported. The comparative analysis yields several key findings: (1) Pre-trained language model-based solvers demonstrate significant accuracy advantages across nearly all datasets, although they suffer from limitations in math equation calculation. (2) Models integrated with tree decoders exhibit strong performance in generating complex math equations. (3) Identifying and appropriately representing implicit knowledge hidden in problem texts is crucial for improving the accuracy of math equation generation. Finally, the paper also discusses the major technical challenges and potential research directions in this field. The insights gained from this analysis offer valuable guidance for future research, model development, and performance optimization in the field of math word problem solving.

Evolutionary Perspectives on Neural Network Generations: A Critical Examination of Models and Design Strategies

Abstract: In the last few years, Neural Networks have become more common in different areas due to their ability to learn intricate patterns and provide precise predictions. Nonetheless, creating an efficient neural network model is a difficult task that demands careful thought of multiple factors, such as architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different deep neural networks (DNN) in image recognition, natural language processing, and time series analysis. In addition, the text explores the advantages of selecting optimal values for various components of an Artificial Neural Network (ANN). These components include the number of input/output layers, the number of hidden layers, the type of activation function used, the number of epochs, and the model type selection. Setting these components to their ideal values can help enhance the model's overall performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.

Research and application of safety monitoring technology of distribution automation based on SOM neural network

Abstract For automatic situation monitoring in power distribution safety monitoring, data features are mainly extracted by single hidden layer neural network, which makes the standard error of monitoring results larger. Therefore, the research and application of power distribution automation safety monitoring technology based on SOM neural network are proposed. Preprocess the power grid, collect distribution operation data, set multi-dimensional monitoring nodes according to the collected data, build a distribution operation status monitoring model and analyze the data fusion technology of distribution automation safety monitoring according to the model. On this basis, the distribution automation safety monitoring system is defined, the output node correction weight and the reverse output node correction weight are calculated, the SOM neural network identification model is constructed and the research and application of the distribution automation safety monitoring technology are completed under the action of the gravitational function between individuals within the target time. The experimental results show that the change curve of the number of vulnerabilities and the actual number of false positives is consistent, the number of vulnerabilities is small and the monitoring results are more accurate; The state of the safety monitoring equipment of distribution automation is normal. After applying the method in this paper, the change curve is consistent with the actual value, and the delay time is less than 15 ms.

Abstract 16400: Development of Deep Neural Network Models for Identification of Pulmonary Vein Potentials From Body Surface Electrocardiograms of Patients With Atrial Fibrillation

[Background] Pulmonary vein (PV) potential is essential as the origin of atrial fibrillation (AF). However, no established method exists to evaluate PV potentials on a body surface ECG (BS-ECG). [Objective] To develop a method to identify PV potentials from BS-ECGs using state-of-the-art deep neural network (DNN) technology. [Methods and results] A cohort of 799 consecutive paroxysmal AF patients, who underwent Pulmonary Vein Isolation (PVI), were enrolled, and pre- and post-PVI 12-lead ECGs (12ECGs) were collected, amounting to a dataset of 1598 ECGs. Four DNN models were constructed, each employing distinct architectures: 1D-ResNet, 1D-DenseNet, 1D-EfficientNet-b3, and 1D-ViT (Vision Transformer). These models were rigorously trained and evaluated for the identification of PV potentials. The 1D-EfficientNet-b3-based model exhibited optimal performance, achieving an accuracy of 89.4% in the training set and 89.1% in the test set. The Gradient-weighted Class Activation Mapping (Grad-CAM) effectively demonstrated that the segments 200-300ms before the P-wave is crucial for identifying PV potentials. Furthermore, DNN models were developed for each single-lead ECG. While there were variations in the accuracy of PV potential identification among single-lead models, the V6-lead model demonstrated superior performance with an accuracy of 83.2%. [Conclusions] The findings of this research strongly suggest that our DNN-based model can accurately differentiate PV potentials from BS-ECGs. Additionally, Grad-CAM visualizations indicate that pertinent features for the identification of PV potentials are predominantly clustered in the P-wave frontal region. This signifies a promising advancement in the non-invasive detection and analysis of PV potentials, paving the way for improved management of paroxysmal AF.

A numerical control machining tool path step error prediction method based on BP neural network

Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks can be calculated on GPUs and cloud platforms, which is conducive to reducing computation time and improving accuracy through continuous learning. This article innovatively introduces a BP neural network model to predict step error values. Firstly, the core parameters required for step error calculation are taken as the data samples to construct the neural network model, and map to the same scale through Z-score normalization to eliminate the adverse effects of singular parameters on the calculation results. Then, considering only a small number of parameters determine theoretical values of step error, the Dropout technique can drop hidden layer neurons with a certain probability, which is helpful to avoid overfitting and used in the neural network model design. In the neural network model training, this paper adds the Stochastic Gradient Descent with Momentum (SGDM) optimizer to the back propagation of network training in order to improves the network’ stability and accuracy. The proposed neural network predicts step error of samples from three surface models, the results show that the prediction error decreases as sample training increases. After trained by 15% of the surface samples, the neural network predicts the step errors of the remaining samples. Compared with theoretical values, more than 99% of the predicted values have an absolute error less than 1 μm. Moreover, the cost time is only one-third of the geometric method, which verifies the effectiveness and efficiency of our method.

Retracted: Hybrid Graph Neural Network Model Design and Modeling Reasoning for Text Feature Extraction and Recognition

Retracted: Hybrid Graph Neural Network Model Design and Modeling Reasoning for Text Feature Extraction and Recognition

Intelligent feedforward gust alleviation based on neural network

This paper proposes a neural network-based intelligent feedforward gust alleviation framework, which includes a neural network identification model and a neural network controller. A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight. A neural network identification model is first trained to accurately predict the aircraft’s output. Then, based on the output of the identification model and the collected flight data, the parameters of the time-delay neural network controller are obtained through a learning process. The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation. For example, when the aircraft is 40000 kg and the flight speed is 0.81Ma, the controller achieves a 67.82% reduction in wingtip acceleration and a 35.90% reduction in center of mass acceleration under continuous turbulence excitation. When considering the measurement uncertainties, such as noise existing in the collected data, the trained controller can still achieve an acceptable gust alleviation effect. Finally, considering a flight in which the aircraft mass is constantly changing, the intelligent controller, which continuously learns from new flight data, maintains a good gust alleviation effect throughout the flight.

Retracted: Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets

Retracted: Development of Integrated Neural Network Model for Identification of Fake Reviews in E-Commerce Using Multidomain Datasets

Rapid detection and identification of rocks and minerals by laser‐induced breakdown spectroscopy

AbstractIn geological exploration, it is necessary to analyze the geological history according to the rock types. But in places for people to reach, sample collection, and transportation is a costly task. At present, the remote intelligent detection of rock types needs to be further developed. In this study, laser‐induced breakdown spectroscopy (LIBS), a sensitive optical technique that can rapidly analyze various elements, is applied to real‐time detection and analysis of rock types. Representative rock samples and minerals are selected for spectral analysis and machine learning. The characteristic spectral lines of Ca, Al, Mg, Ti, Si, Na, Fe, K, and Li were observed in the spectra. By comparing the spectra of different samples, the differences among them were discussed. First, principal component analysis (PCA) is used for dimensionality reduction. With the help of PCA, the data are distributed in two‐dimensional and three‐dimensional space and different kinds of rocks and minerals are classified successfully. Then, combined with error back propagation training artificial neural network, the rock and mineral identification model was established, and the recognition rate can reach 100%. The results show that LIBS is a powerful tool for remote intelligent real‐time rock detection and classification, and has great application prospects in the exploration of extraterrestrial objects including planets, satellites and asteroids in the future.

Optimal architecture artificial neural network model design with exploitative alpha gray wolf optimization for soft calibration of CO concentration measurements in electronic nose applications

The low-cost and small size solid-state sensor arrays are suitable to implement a wide-area electronic nose (e-nose) for real-time air quality monitoring. However, accuracy of these low-cost sensors is not adequate for precise measurements of pollutant concentrations. Artificial neural network (ANN) estimation models are used for the soft calibration of low-cost sensor array measurements and significantly improve the accuracy of low-cost multi-sensor measurements. However, optimality of neural architecture affects the performance of ANN estimation models, and optimization of the ANN architecture for a training data set is essential to improve data-driven modeling performance of ANNs to reach optimal neural complexity and improved generalization. In this study, an optimal architecture ANN estimator design scheme is suggested to improve the estimation performance of ANN models for e-nose applications. To this end, a gray wolf optimization (GWO) algorithm is modified, and an exploitative alpha gray wolf optimization (EA-GWO) algorithm is suggested. This modification enhances local exploitation skill of the best alpha gray wolf search agent, and thus allows the fine-tuning of ANN architectures by minimizing a multi-objective cost function that implements mean error search policy. Experimental study demonstrates the effectiveness of optimal architecture ANN models to estimate CO concentration from the low-cost multi-sensor data.

Neural Network Model Design for Landscape Ecological Planning Assessment Based on Hierarchical Analysis.

In this paper, an in-depth study and analysis of landscape ecological planning and evaluation are carried out using the analytic hierarchy process (AHP) algorithm that integrates neural networks. The application of AHP in the field of tree species planning and the introduction of quantitative analysis methods can effectively change the subjectivity of previous qualitative analysis in tree species selection and make it objective, scientific, and reasonable. The research can provide a reference for other urban tree species planning. From the connotation of landscape ecological service process and ecological space structure, the analysis of landscape ecological service process involves service supply area and service association area, which correspond to different key components of ecological space structure. With the help of the platform, based on the identification and identification methods and theories of ecological spatial structure, the key components of ecological spatial structure in different environments are identified and extracted by using the representation model, binary suitability model, weighted suitability model, and process model. The type of service is based on the different service processes supported by the key components of the ecological spatial structure, forming the ecological spatial structure under different service types. Spatial structure; on this basis, the basic characteristics of the key components of the ecological spatial structure are analyzed, and the correlation characteristics of the ecological spatial structure are analyzed based on the correlation classification system of ecological spatial structure. A backpropagation (BP) neural network-based state assessment method of the grid structure is established. The method takes the parameters of the autoregressive model constructed by the acceleration signals of different working conditions as the feature quantity and the results of the fuzzy hierarchical analysis method as the labels, divides the data set into a training set and a test set, and uses the BP neural network learning method and the training set to supervised train the BP neural network learning assessment model. The test set is used to test the effectiveness and accuracy of the BP neural network-based learning method. The study shows that the evaluation system established by the BP neural network structure is fast and accurate and can substantially reduce the cost of manual testing.

Dynamic Neural Network Model Design for Solar Radiation Forecast

Sunlight is an energy source that is a gift from God and is a source of life for living things, including humans as caliphs on earth. Judging from its impact, solar radiation is an environmental parameter that has positive and negative effects on human life. The pattern of distribution of solar radiation is important information for human life to be the attention of many people, both policymakers and researchers in the field of environment. This study objects to modeling the radiation of solar using a dynamic neural network (DNN) model. The data used in this research is the meteorological data of Mataram City for the period January 2018 to May 2019, which was obtained from the Department of Environment and Forestry of West Nusa Tenggara Province. In the development of this model, solar radiation was seen as a function of a combination of several variables related to meteorological (wind speed, wind direction, humidity, air pressure, and air temperature) and solar radiation data at some previous time. Considering the advantages and effectiveness of the activation function in the proposed DNN model learning process, this study's network learning in the hidden layer employed two activation functions: hyperbolic tangent (Type I) and hyperbolic tangent sigmoid functions (Type II). The output aggregation used two aggregates for each type: the weighted aggregation function (Type a) and the maximum function (Type b). The results of computer simulations based on the root of mean square error (RMSE) measure indicate that the model for modeling solar radiation in these two cases is quite accurate. Furthermore, it could be seen that the model's performance using the hyperbolic tangent activation function (Type b) is relatively better than the hyperbolic tangent sigmoid type of the activation function (Type a), with the RMSE values are 18.3924 and 18.4005, respectively.

Research on the application of artificial intelligence in computer network technology in the era of big data

SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.

Hybrid Graph Neural Network Model Design and Modeling Reasoning for Text Feature Extraction and Recognition

Natural language processing is an important branch of deep learning. In particular, the classification of short texts is one of the main tasks of computer linguistics, because it ensures the security of information. Therefore, this paper reviews the text classification methods for the first time, aiming at comparing the modern methods to solve the text classification problems, determine the trend in this direction, and select the best algorithm to be applied to research and business tasks. In this paper, with the help of the emerging algorithm in the field of deep learning, a web domain knowledge text classification method based on feature words is developed with the support vector machine (SVM) as the basic classification algorithm. Two datasets representing basic research disciplines and emerging research fields are selected to verify the proposed research framework. Experiments show that the performance of this method is improved by 3% compared with the benchmark method.

Load Identification for the More Electric Aircraft Distribution System Based on Intelligent Algorithm

Accurate identification of electrical load working status can provide information support to the remote electrical distribution system (EDS) of more electric aircraft (MEA), which could use it to realize redundant switching and protection. This paper presents a method to automatically identify the load status on the remote power distribution unit (RPDU) of MEA by using an intelligent algorithm. The experimental platform is built in an aircraft Electrical Power System (EPS) distribution large-scale test cabin. Four pieces of typical aviation equipment are installed in the test cabin and powered from RPDU. Voltage and current values under 15 working combinations on the RPDU are measured to extract the steady-state V-I trajectory. In total, 750 group samples were collected in the feature parameter database. A generalized regression neural network (GRNN) identification model was established, and the smoothing factor was calculated by using a conventional cross-validation method to train and reach an optimal value. However, the identification results are not ideal. In order to improve the accuracy, the parameter of GRNN was optimized by genetic algorithms. The proposed model shows great performance as accuracy of all 15 classifications reached 100%. The proposed model has advantages of flexible network structure, high fault tolerance, and robustness. It can realize global approximation optimization, avoid local optimization, effectively improve GRNN fitting accuracy, improve model generalization ability, and reduce model training calculation.

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

Design of Neural Network Model for Cross-Media Audio and Video Score Recognition Based on Convolutional Neural Network Model.

In this paper, the residual convolutional neural network is used to extract the note features in the music score image to solve the problem of model degradation; then, multiscale feature fusion is used to fuse the feature information of different levels in the same feature map to enhance the feature representation ability of the model. A network composed of a bidirectional simple loop unit and a chained time series classification function is used to identify notes, parallelizing a large number of calculations, thereby speeding up the convergence speed of training, which also makes the data in the dataset no longer need to be strict with labels. Alignment also reduces the requirements on the dataset. Aiming at the problem that the existing cross-modal retrieval methods based on common subspace are insufficient for mining local consistency within modalities, a cross-modal retrieval method fused with graph convolution is proposed. The K-nearest neighbor algorithm is used to construct modal graphs for samples of different modalities, and the original features of samples from different modalities are encoded through a symmetric graph convolutional coding network and a symmetric multilayer fully connected coding network, and the encoded features are fused and input. We jointly optimize the intramodal semantic constraints and intermodal modality-invariant constraints in the common subspace to learn highly locally consistent and semantically consistent common representations for samples from different modalities. The error value of the experimental results is used to illustrate the effect of parameters such as the number of iterations and the number of neurons on the network. In order to more accurately illustrate that the generated music sequence is very similar to the original music sequence, the generated music sequence is also framed, and finally the music sequence spectrogram and spectrogram are generated. The accuracy of the experiment is illustrated by comparing the spectrogram and the spectrogram, and genre classification predictions are also performed on the generated music to show that the network can generate music of different genres.

Double convolutional neural network for fault identification of power distribution network

The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for the distribution network based on a double convolutional neural network is proposed in this study. The 1-dimension convolutional auto-encoder (1-D CAE) is used to learn features from the power grid transient data. The obtained low-dimensional fault features are imported into the 1-dimension convolutional neural network (1-D CNN) identification model. The identification accuracy of the proposed model is higher than that of the traditional methods by the verification of the measured transient fault data of the power distribution network. The robustness study implies that the DCNN model can be applied in practical situations prone to using contaminated samples.

Digital Computer Modulation Signal Classification Based on Neural Network

Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.

NemaNet: A convolutional neural network model for identification of soybean nematodes

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of the world production. Besides, the identification of these species through microscopic analysis by an expert with taxonomic knowledge is often laborious, time-consuming, and susceptible to failure. From this perspective, robust and automatic approaches are necessary for identifying phytonematodes that are capable of providing correct diagnoses for the classification of species and subsidizing of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and present a comparative assessment with thirteen popular models of CNNs, all of them representing state-of-the art classification and recognition. The general average was calculated for each model, on a from-scratch training; the NemaNet model reached 96.76% accuracy, while the best evaluation fold reached 98.04%. When training with transfer learning was performed, the average accuracy reached 98.82%. The best evaluation fold reached 99.35%, and overall accuracy improvements of over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, compared to other popular models were achieved. • Detect phytonematodes in soybean crops in Brazil using convolutional neural network. • A new dataset with five main species of phytonematodes from Brazil's soybean culture. • Evaluation of thirteen most popular CNN's architectures and new model (NemaNet). • Results describe NemaNet reaches best results in identifying nematodes overall.