Relevant bibliographies by topics / Graph-based neural network model

Academic literature on the topic 'Graph-based neural network model'

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Published: 6 September 2023

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Journal articles on the topic "Graph-based neural network model"

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Hao, Jia, Yonghong Tian, Qingqing Zhang, and Genmao Zhang. "Mongolian-Chinese Machine Translation Based on Graph Neural Network." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012050. http://dx.doi.org/10.1088/1742-6596/2400/1/012050.

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Abstract Most of the current Mongolian-Chinese neural machine translation relies on a sequence-to-sequence-based encoder-decoder, which does not effectively utilize the syntactic information of sentences as well as the hierarchical information of the language. To solve this problem, a graph-to-sequence-based encoder-decoder is constructed by introducing graph neural networks, making better use of syntactic information and semantic knowledge of sentences. We will build a Mongolian dependency tree library and design a densely connected graph convolutional neural network (D-GCN) based on GCN combined with a densely connected network (DenseNet). Experiments are conducted to compare with the sequence-to-sequence based Mongolian-Chinese neural machine translation model and the variant model of graph neural network, and the results verify the advantages of the D-GCN-based Mongolian-Chinese neural machine translation model in translation performance.
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Scarselli, F., M. Gori, Ah Chung Tsoi, M. Hagenbuchner, and G. Monfardini. "The Graph Neural Network Model." IEEE Transactions on Neural Networks 20, no. 1 (January 2009): 61–80. http://dx.doi.org/10.1109/tnn.2008.2005605.

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Elnaggar, Sarah G., Ibrahim E. Elsemman, and Taysir Hassan A. Soliman. "Embedding-Based Deep Neural Network and Convolutional Neural Network Graph Classifiers." Electronics 12, no. 12 (June 17, 2023): 2715. http://dx.doi.org/10.3390/electronics12122715.

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One of the most significant graph data analysis tasks is graph classification, as graphs are complex data structures used for illustrating relationships between entity pairs. Graphs are essential in many domains, such as the description of chemical molecules, biological networks, social relationships, etc. Real-world graphs are complicated and large. As a result, there is a need to find a way to represent or encode a graph’s structure so that it can be easily utilized by machine learning models. Therefore, graph embedding is considered one of the most powerful solutions for graph representation. Inspired by the Doc2Vec model in Natural Language Processing (NLP), this paper first investigates different ways of (sub)graph embedding to represent each graph or subgraph as a fixed-length feature vector, which is then used as input to any classifier. Thus, two supervised classifiers—a deep neural network (DNN) and a convolutional neural network (CNN)—are proposed to enhance graph classification. Experimental results on five benchmark datasets indicate that the proposed models obtain competitive results and are superior to some traditional classification methods and deep-learning-based approaches on three out of five benchmark datasets, with an impressive accuracy rate of 94% on the NCI1 dataset.
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Bi, Zhongqin, Lina Jing, Meijing Shan, Shuming Dou, and Shiyang Wang. "Hierarchical Social Recommendation Model Based on a Graph Neural Network." Wireless Communications and Mobile Computing 2021 (August 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/9107718.

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With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.
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AlBadani, Barakat, Ronghua Shi, Jian Dong, Raeed Al-Sabri, and Oloulade Babatounde Moctard. "Transformer-Based Graph Convolutional Network for Sentiment Analysis." Applied Sciences 12, no. 3 (January 26, 2022): 1316. http://dx.doi.org/10.3390/app12031316.

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Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models.
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Li, Boao. "Multi-modal sentiment analysis based on graph neural network." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 792–98. http://dx.doi.org/10.54254/2755-2721/6/20230918.

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Thanks to popularity of social media, people are witnessing the rapid proliferation of posts with various modalities. It is worth noting that these multi-modal expressions share certain characteristics, including the interdependence of objects in the posted images, which is sometimes overlooked in previous researches as they focused on single image-text posts and pay little attention on obtaining the global features. In this paper, a neural network with multiple channels for image-text sentiment detection is proposed. The first step is to encode text and images to capture implicit tendencies. Then the introduction of this model obtains multi-modal expressions by collecting the shared characteristics of the dataset. Finally, the attention mechanism provides reliable predictions of the sentiment tendencies of the given pairs of image-text data. The results of experiments conducted on two publicly available datasets crawled from Twitter prove the reliability of the model on multi-modal sentiment detection, since the model precedes previously proposed models in the main evaluating criteria.
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Xu, Jinling, Yanping Chen, Yongbin Qin, Ruizhang Huang, and Qinghua Zheng. "A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction." Symmetry 13, no. 8 (August 9, 2021): 1458. http://dx.doi.org/10.3390/sym13081458.

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The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing structural information of a sentence is important to support this task. Currently, graph neural networks are widely implemented to support relation extraction, in which dependency trees are employed to generate adjacent matrices for encoding structural information of a sentence. Because parsing a sentence is error-prone, it influences the performance of a graph neural network. On the other hand, a sentence is structuralized by several named entities, which precisely segment a sentence into several parts. Different features can be combined by prior knowledge and experience, which are effective to initialize a symmetric adjacent matrix for a graph neural network. Based on this phenomenon, we proposed a feature combination-based graph convolutional neural network model (FC-GCN). It has the advantages of encoding structural information of a sentence, considering prior knowledge, and avoiding errors caused by parsing. In the experiments, the results show significant improvement, which outperform existing state-of-the-art performances.
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Gao, Huiguo, Mengyuan Lee, Guanding Yu, and Zhaolin Zhou. "A Graph Neural Network Based Decentralized Learning Scheme." Sensors 22, no. 3 (January 28, 2022): 1030. http://dx.doi.org/10.3390/s22031030.

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As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices. It is a challenging problem since link loss, partial device participation, and non-independent and identically distributed (non-iid) data distribution would all deteriorate the performance of decentralized learning algorithms. Existing work may restrict to linear models or show poor performance over non-iid data. Therefore, in this paper, we propose a decentralized learning scheme based on distributed parallel stochastic gradient descent (DPSGD) and graph neural network (GNN) to deal with the above challenges. Specifically, each user device participating in the learning task utilizes local training data to compute local stochastic gradients and updates its own local model. Then, each device utilizes the GNN model and exchanges the model parameters with its neighbors to reach the average of resultant global models. The iteration repeats until the algorithm converges. Extensive simulation results over both iid and non-iid data validate the algorithm’s convergence to near optimal results and robustness to both link loss and partial device participation.
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Zhong, Hongwei, Mingyang Wang, and Xinyue Zhang. "HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network." Electronics 12, no. 9 (May 6, 2023): 2124. http://dx.doi.org/10.3390/electronics12092124.

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Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.
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Zhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.

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Graph Convolutional Neural Networks are more and more widely used in traffic flow parameter prediction tasks by virtue of their excellent non-Euclidean spatial feature extraction capabilities. However, most graph convolutional neural networks are only used to predict one type of traffic flow parameter. This means that the proposed graph convolutional neural network may only be effective for specific parameters of specific travel modes. In order to improve the universality of graph convolutional neural networks. By embedding time feature and spatio-temporal attention layer, we propose a spatio-temporal attention graph convolutional neural network based on the attention mechanism of the neural network. Through experiments on passenger flow data and vehicle speed data of two different travel modes (Hangzhou Metro Data and California Highway Data), it is verified that the proposed spatio-temporal attention graph convolutional neural network can be used to predict passenger flow and vehicle speed simultaneously. Meanwhile, the error distribution range of the proposed model is minimum, and the overall level of prediction results is more accurate.
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Dissertations / Theses on the topic "Graph-based neural network model"

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McMichael, Lonny D. (Lonny Dean). "A Neural Network Configuration Compiler Based on the Adaptrode Neuronal Model." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc501018/.

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A useful compiler has been designed that takes a high level neural network specification and constructs a low level configuration file explicitly specifying all network parameters and connections. The neural network model for which this compiler was designed is the adaptrode neuronal model, and the configuration file created can be used by the Adnet simulation engine to perform network experiments. The specification language is very flexible and provides a general framework from which almost any network wiring configuration may be created. While the compiler was created for the specialized adaptrode model, the wiring specification algorithms could also be used to specify the connections in other types of networks.
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Koessler, Denise Renee. "A Predictive Model for Secondary RNA Structure Using Graph Theory and a Neural Network." Digital Commons @ East Tennessee State University, 2010. https://dc.etsu.edu/etd/1684.

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In this work we use a graph-theoretic representation of secondary RNA structure found in the database RAG: RNA-As-Graphs. We model the bonding of two RNA secondary structures to form a larger structure with a graph operation called merge. The resulting data from each tree merge operation is summarized and represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not based on the merge data vector. The network correctly assigned a high probability of RNA-likeness to trees identified as RNA-like in the RAG database, and a low probability of RNA-likeness to those classified as not RNA-like in the RAG database. We then used the neural network to predict the RNA-likeness of all the trees of order 9. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel.
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Calvert, David. "A distance-based neural network model for sequence processing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0010/NQ30591.pdf.

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Ozkok, Yusuf Ibrahim. "Web Based Ionospheric Forecasting Using Neural Network And Neurofuzzy Models." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606031/index.pdf.

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This study presents the implementation of Middle East Technical University Neural Network (METU-NN) models for the ionospheric forecasting together with worldwide usage capability of the Internet. Furthermore, an attempt is made to include expert information in the Neural Network (NN) model in the form of neurofuzzy network (NFN). Middle East Technical University Neurofuzzy Network (METU-NFN) modeling approach is developed which is the first attempt of using a neurofuzzy model in the ionospheric forecasting studies. The Web based applications developed in this study have the ability to be customized such that other NN and NFN models including METU-NFN can also be adapted. The NFN models developed in this study are compared with the previously developed and matured METU-NN models. At this very early stage of employing neurofuzzy models in this field, ambitious objectives are not aimed. Applicability of the neurofuzzy systems on the ionospheric forecasting studies is only demonstrated. Training and operating METU-NN and METU-NFN models under equal conditions and with the same data sets, the cross correlation of obtained and measured values are 0.9870 and 0.9086 and the root mean square error (RMSE) values of 1.7425 TECU and 4.7987 TECU are found by operating METU-NN and METU-NFN models respectively. The results obtained by METU-NFN model is close to those found by METU-NN model. These results are reasonable enough to encourage further studies on neurofuzzy models to benefit from expert information. Availability of these models which already attracted intense international attention will greatly help the related scientific circles to use the models. The models can be architecturally constructed, trained and operated on-line. To the best of our knowledge this is the first application that gives the ability of on-line model usage with these features. Applicability of NFN models to the ionospheric forecasting is demonstrated. Having ample flexibility the constructed model enables further developments and improvements. Other neurofuzzy systems in the literature might also lead to better achievements.
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FUTIA, GIUSEPPE. "Neural Networks forBuilding Semantic Models and Knowledge Graphs." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850594.

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Thiruvengadachari, Sathish. "Experimental and neural network-based model for human-machine systems reliability." Diss., Online access via UMI:, 2006.

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Zorzetto, Luiz Flavio Martins. "Bioprocess monitoring with hybrid neural network/mechanistic model based state estimators." Thesis, University of Nottingham, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283350.

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Wang, Feng. "Neural network model of memory reinforcement for text-based intelligent tutoring system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0021/NQ30122.pdf.

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Wredh, Simon. "Neural Network Based Model Predictive Control of Turbulent Gas-Solid Corner Flow." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420056.

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Over the past decades, attention has been brought to the importance of indoor air quality and the serious threat of bio-aerosol contamination towards human health. A novel idea to transport hazardous particles away from sensitive areas is to automatically control bio-aerosol concentrations, by utilising airflows from ventilation systems. Regarding this, computational fluid dynamics (CFD) may be employed to investigate the dynamical behaviour of airborne particles, and data-driven methods may be used to estimate and control the complex flow simulations. This thesis presents a methodology for machine-learning based control of particle concentrations in turbulent gas-solid flow. The aim is to reduce concentration levels at a 90 degree corner, through systematic manipulation of underlying two-phase flow dynamics, where an energy constrained inlet airflow rate is used as control variable. A CFD experiment of turbulent gas-solid flow in a two-dimensional corner geometry is simulated using the SST k-omega turbulence model for the gas phase, and drag force based discrete random walk for the solid phase. Validation of the two-phase methodology is performed against a backwards facing step experiment, with a 12.2% error correspondence in maximum negative particle velocity downstream the step. Based on simulation data from the CFD experiment, a linear auto-regressive with exogenous inputs (ARX) model and a non-linear ARX based neural network (NN) is used to identify the temporal relationship between inlet flow rate and corner particle concentration. The results suggest that NN is the preferred approach for output predictions of the two-phase system, with roughly four times higher simulation accuracy compared to ARX. The identified NN model is used in a model predictive control (MPC) framework with linearisation in each time step. It is found that the output concentration can be minimised together with the input energy consumption, by means of tracking specified target trajectories. Control signals from NN-MPC also show good performance in controlling the full CFD model, with improved particle removal capabilities, compared to randomly generated signals. In terms of maximal reduction of particle concentration, the NN-MPC scheme is however outperformed by a manually constructed sine signal. In conclusion, CFD based NN-MPC is a feasible methodology for efficient reduction of particle concentrations in a corner area; particularly, a novel application for removal of indoor bio-aerosols is presented. More generally, the results show that NN-MPC may be a promising approach to turbulent multi-phase flow control.
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Dai, Jing. "Reservoir-computing-based, biologically inspired artificial neural networks and their applications in power systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47646.

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Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.
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Books on the topic "Graph-based neural network model"

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T, Bialasiewicz Jan, and Langley Research Center, eds. Neural network modeling of nonlinear systems based on Volterra series extension of a linear model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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Neural networks and intellect: Using model-based concepts. Oxford: Oxford University Press, 2001.

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Grzeszczuk, Radek. Neuroanimator: Fast neural network emulation and control of physics-based models. Toronto: University of Toronto, Dept. of Computer Science, 1998.

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El-Gindy, Moustafa. Development of a tire/pavement contact-stress model based on artificial neural networks. McLean, Va. (6300 Georgetown Pike, McLean, 22101-2296): U.S, Dept. of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2001.

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El-Gindy, Moustafa. Development of a tire/pavement contact-stress model based on artificial neural networks. McLean, VA: U.S. Department of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2001.

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Hiroaki, Wagatsuma, ed. Neuromorphic and brain-based robots. Cambridge, UK: Cambridge University Press, 2011.

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Milford, Michael John. Robot navigation from nature: Simultaneous localisation, mapping, and path planning based on hippocampal models. Berlin: Springer, 2008.

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Shishkin, Aleksey. Methods of digital processing and speech recognition. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1904325.

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The monograph discusses the theory, algorithms and practical methods of implementing digital processing and recognition of speech signals. The basics of mathematical analysis of digital signals necessary for speech processing are presented. The acoustic theory of speech formation with the construction of a general discrete model is briefly described. The main characteristic features of speech signals, as well as methods of their isolation are considered. Hidden Markov models and the architecture of traditional recognition systems based on them are described in detail. Weighted finite converters used to increase the efficiency and speed up the process of decoding acoustic signals are considered. The main architectures of artificial neural networks and examples of integrated (end-to-end) speech recognition systems based on them are presented. It is intended for students, postgraduates, researchers and specialists dealing with speech signal processing, pattern recognition and artificial intelligence.
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Perlovsky, Leonid I. Neural Networks and Intellect: Using Model-Based Concepts. Oxford University Press, USA, 2000.

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Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.

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What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learning, defeated the world’s best Go player. The climax of this chapter is a fictional example starring Sherlock Holmes demonstrating that complex associative computation in practice has less in common with accurate pattern recognition and more with abstract high-level conceptual inference.
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Book chapters on the topic "Graph-based neural network model"

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Liu, Wei-ming. "Knowledge Dissemination Model Based on Graph Neural Network." In Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022), 3157–63. Paris: Atlantis Press SARL, 2022. http://dx.doi.org/10.2991/978-2-494069-31-2_370.

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An, Zhiwei, Yirui Tan, Jinli Zhang, Zongli Jiang, and Chen Li. "A Session Recommendation Model Based on Heterogeneous Graph Neural Network." In Knowledge Science, Engineering and Management, 160–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40289-0_13.

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Li, Zhijun, Chenguang Yang, and Liping Fan. "Neural Network Based Model Reference Control." In Advanced Control of Wheeled Inverted Pendulum Systems, 193–210. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2963-9_9.

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Wasserman, Theodore, and Lori Drucker Wasserman. "Mindfulness-Based Approaches and Attention Regulation." In Therapy and the Neural Network Model, 115–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26921-0_7.

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Grancharova, Alexandra, and Tor Arne Johansen. "Explicit NMPC Based on Neural Network Models." In Explicit Nonlinear Model Predictive Control, 187–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_8.

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Ming-zhong, Mo. "Network Security Analysis Based on Graph Theory Model with Neutral Network." In Lecture Notes in Electrical Engineering, 551–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27311-7_73.

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Wang, Zhiyu, Xulun Hu, Fang Zuo, Hong Li, Yiran Zhang, and Weifeng Wang. "A Graph Neural Network Based Model for IoT Binary Components Similarity Detection." In Communications in Computer and Information Science, 120–31. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8350-4_10.

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Sambamoorthy, Ramakrishnan, Jaswanthi Mandalapu, Subrahmanya Swamy Peruru, Bhavesh Jain, and Eitan Altman. "Graph Neural Network Based Scheduling: Improved Throughput Under a Generalized Interference Model." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 144–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92511-6_9.

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Zhao, Cong, Yan Wen, Ming Chen, and Geng Chen. "Recommendation Based Heterogeneous Information Network and Neural Network Model." In Wireless and Satellite Systems, 588–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69072-4_48.

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Septiawan, Reza, Arief Rufiyanto, Sardjono Trihatmo, Budi Sulistya, Erik Madyo Putro, and Subana Shanmuganathan. "Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies." In Artificial Neural Network Modelling, 423–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_20.

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Conference papers on the topic "Graph-based neural network model"

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Liu, Weidong, Haochen Zhang, Xu Guo, and Yong Han. "Graph Convolutional Network Based Patent Issue Discovery Model." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533370.

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Cao, Wanwan, Yu Lu, Quanxiao Wang, Jianchao Guan, and Jun Guo. "Predictive model based on graph convolutional neural network." In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2022. http://dx.doi.org/10.1109/itoec53115.2022.9734716.

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Sun, Shuyuan, Xuelian Chen, Fan Yang, Bei Yu, Shang Li, and Xuan Zeng. "Efficient Model-Based OPC via Graph Neural Network." In 2023 International Symposium of Electronics Design Automation (ISEDA). IEEE, 2023. http://dx.doi.org/10.1109/iseda59274.2023.10218720.

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Liu, Weidong, Xin Liu, and Wenbo Qiao. "Bayesian Graph Convolutional Neural Network based Patent Valuation Model." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207247.

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Feng, Qi, Yuan Tan, Ming Zhou, Guangjun Zeng, Zhe Chen, Yiming Liu, and Yifeng Li. "Recommendation Model Based on Deep Separated Graph Neural Network." In 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2021. http://dx.doi.org/10.1109/icccbda51879.2021.9442597.

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Tang, Hanchao, Licai Wang, and Qibin Luo. "Trajectory-text retrieval model based on graph neural network." In Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), edited by Tao Zhang and Ting Yang. SPIE, 2023. http://dx.doi.org/10.1117/12.2660180.

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Wang, Xinyu. "Graph Neural Network Model Based on Layered Converter Aggregation." In 2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2023. http://dx.doi.org/10.1109/icecai58670.2023.10176410.

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Pan, Zeting, Yue Yu, and Junsheng Chang. "A Distributed Graph Inference Computation Framework Based on Graph Neural Network Model." In The 34th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc., 2022. http://dx.doi.org/10.18293/seke2022-042.

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Zhang, Lianming, Benle Yin, Qian Wang, and Pingping Dong. "Graph Neural Network-based Delay Prediction Model Enhanced by Network Calculus." In 2023 IFIP Networking Conference (IFIP Networking). IEEE, 2023. http://dx.doi.org/10.23919/ifipnetworking57963.2023.10186434.

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Li, Xiaoling, Zixuan Guan, Qian Xu, Shenze Huang, and Junxian Wang. "Graph Classification Model Based on Graph Neural Networks and Graph Distance." In 2021 8th International Conference on Computational Science/Intelligence and Applied Informatics (CSII). IEEE, 2021. http://dx.doi.org/10.1109/csii54342.2021.00022.

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Reports on the topic "Graph-based neural network model"

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Mu, Ruihui. A Novel Recommendation Model Based on Deep Neural Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2020. http://dx.doi.org/10.7546/crabs.2020.05.11.

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Chen, Lynda, and Cameron Brennan. Preliminary to a Neural Network Model of Sonar-Based Target Discrimination in the Echolocating Bat. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada205681.

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Olsen and Willson. L51916 Pressure Based Parametric Emission Monitoring Systems (PEMS). Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2002. http://dx.doi.org/10.55274/r0010181.

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The natural gas industry operates over 8000 stationary large bore (bore greater than 14 in) natural gas engines for natural gas compression on pipelines and power generation. As emissions regulations become increasingly more stringent, the need for low cost methods for compliance demonstration arises. A PEMS model is one such approach. Research in this area has increased significantly during the last decade. PEMS models for this application utilize parameters commonly measured on industrial engines in the field to predict engine-out emissions. Monitoring emissions in this manner represents a significant cost savings over the periodic use of chemiluminescence NOX analyzers, which are not standard equipment in natural gas compressor stations. PEMS model accuracy is dependent on the quality of the input data, both the training NOX measurements and the selection of input parameters. Hence, it is important to have both reliable data measurement methods and an understanding of engine operating parameters relation to NOX. This work is part of the body of work referred to as the Integrated Test Plan (ITP), performed at the Engines and Energy Conversion Laboratory (EECL). This report details an investigation into Parametric Emissions Monitoring System (PEMS) models. It is the final document to be delivered under the ITP program. Much of the work performed under the ITP program focused on Hazardous Air Pollutants (HAPs) research. However, the emphasis of the PEMS work is on the prediction of oxides of nitrogen (NOX) emissions from large bore natural gas engines. In this work two different PEMS models are developed, a semi-empirical model and a neural network model. The semi-empirical model is based on general relationships between NOX emissions and engine parameters, but contains empirical constants that are determined based on the best fit to engine experimental data. The neural network model utilizes a similar set of input parameters, but relies on the neural network code to determine the relationships between input parameters and measured NOX emissions. The neural network model also contains empirical constants. The mathematics involved in both models is described. A single term semi-empirical model, which has been utilized in the literature as a PEMS model, is applied for comparative purposes.
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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Ferguson, Spencer, William McNally, and John McPhee. Predicting the Flight of a Golf Ball: Comparing a Physics-Based Aerodynamic Model to a Neural Network. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317493.

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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Semerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, April 2021. http://dx.doi.org/10.31812//123456789/4364.

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The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the business climate index were identified, in particular, the annual GDP growth rate and the volume of retail sales. The similarity of the trends in the calculated and actual business climate index was obtained, the forecast values were calculated with an accuracy of 89.38%. And also, the obtained modeling results were developed by means of building and using neural networks with learning capabilities, which makes it possible to improve the quality and accuracy of the business climate index forecast up to 96.22%. It has been established that the consequences of the impact of COVID-19 are forecasting a decrease in the level of the country's business climate index in the 3rd quarter of 2020. The proposed approach to modeling the country's business climate is unified, easily applied to the macroeconomic data of various countries, demonstrates a high level of accuracy and quality of forecasting. The prospects for further research are modeling the business climate of the countries of the world in order to compare trends and levels, as well as their changes under the influence of quarantine restrictions.
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Gindi, Gene. Neural Networks for Model-Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada277375.

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