Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

Zhu, Daoye; Cheng, Chengqi; Zhai, Weixin; Li, Shizhong; Chen, Bo

doi:10.3390/ijgi10020075

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…The basic processing units for algorithm learning are the neurons, which are the building blocks of artificial neural networks. The function of BPNN's back propagation approach is depicted in Figure 3.1 [13,14]. Back propagation neural networks are more fundamental neural networks that use forward propagation for output results and back propagation for error propagation.…”

Section: Construction Of a Bpnn Evaluation Model Incorporating Adapti...mentioning

confidence: 99%

Enhancing IoT Security in Russian Language Teaching: A Improved BPNN and Blockchain-Based Approach for Privacy and Access Control

Jia

2024

SCPE

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Russian language instruction emerges as a pivotal course in tertiary education, necessitating novel approaches to maintain instructional quality and efficacy. This study introduces a novel approach to Russian language teaching that combines the robustness of Machine Learning with the security framework of Blockchain technology and is tailored to the unique needs of the Internet of Things (IoT) environment. At its core, the study creates an advanced back-propagation deep neural network enriched with a deep noise-reducing auto-encoder and a support vector machine to improve privacy and access control in IoT-based educational platforms. The proposed model employs a polynomial kernel function and a one-error penalty factor in a single hidden layer, resulting in a system that is not only efficient in handling small-scale data samples but also adept at processing larger data volumes, a common scenario in IoT settings. This design effectively overcomes the problems of overfitting and slow convergence that are common in traditional models. Furthermore, the incorporation of blockchain technology ensures a decentralized and secure data handling framework, reinforcing the privacy and access control aspects that are critical in the digital education domain. The combination of these technologies yields a more rational, scientifically based evaluation system, propelling the standardization and enhancement of Russian language instruction forward. This method not only improves language teaching quality, but it also paves the way for more secure, scalable, and efficient IoT applications in educational settings.

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Section: Construction Of a Bpnn Evaluation Model Incorporating Adapti...mentioning

confidence: 99%

Enhancing IoT Security in Russian Language Teaching: A Improved BPNN and Blockchain-Based Approach for Privacy and Access Control

Jia

2024

SCPE

View full text Add to dashboard Cite

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“…The neural network element state of each layer only affects the neuron state of the next layer; if the output signal cannot meet the expected output requirements, it is transferred to the error backward propagation process. According to the prediction error, from the output layer to the input layer, the weights and thresholds of the BP neural network are constantly modified so that the prediction output of the BP neural network is close to the expected output [56].…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…. , and Y n represent the output value of the BP neural network [56]. obtained parameters can more truly reflect the objective reality.…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…As shown in Figure 2, the BP neural network is composed of three parts: input layer, hidden layer, and output layer, in which the hidden layer can have multiple layers. X1, X2, ..., and Xn represent the input value of the BP neural network, and Y1, Y2, ..., and Yn represent the output value of the BP neural network [56]. At present, there is no more accurate method to determine the number of neurons in the hidden layer.…”

Section: Bp Neural Networkmentioning

confidence: 99%

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Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Qin

Tian

et al. 2021

Crystals

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Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.

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“…With non-uniform multi-layer input gridded data, the existing methods primarily design the branch structure of the CNN, in which each branch network processes a layer and the results of different branches are summarized at the neural network's end. Land-use classification based on multi-source remote sensing data [25,26] is a typical challenge. Chen et al [27] designed a CNN with two branches to process and integrate multispectral optical remote sensing image data and (light detection and ranging) LiDAR data.…”

Section: Of 23mentioning

confidence: 99%

Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model

et al. 2021

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The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of multi-type geospatial data. This model includes a seismic grid sample model (SGSM) and a spatiotemporal grid model based on a three-dimensional group convolution neural network (3DGCNN-SGM). The SGSM solves the problem concerning that the layers of different data types cannot form an ensemble with a consistent data structure and transforms the grid representation of data into grid samples for deep learning. The 3DGCNN-SGM is the first application of group convolution in the deep learning of multi-source geographic information data. It avoids direct superposition calculation of data between different layers, which may negatively affect the deep learning analysis model results. In this study, taking the atmospheric temperature anomaly and historical earthquake precursory data from Japan as an example, an earthquake early warning verification experiment was conducted based on the proposed SGMG-EEW. Five groups of control experiments were designed, namely with the use of atmospheric temperature anomaly data only, use of historical earthquake data only, a non-group convolution control group, a support vector machine control group, and a seismic statistical analysis control group. The results showed that the proposed SGSM is not only compatible with the expression of a single type of spatiotemporal data but can also support multiple types of spatiotemporal data, forming a deep-learning-oriented data structure. Compared with the traditional deep learning model, the proposed 3DGCNN-SGM is more suitable for the integration analysis of multiple types of spatiotemporal data.

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Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

Cited by 7 publications

References 35 publications

Enhancing IoT Security in Russian Language Teaching: A Improved BPNN and Blockchain-Based Approach for Privacy and Access Control

Enhancing IoT Security in Russian Language Teaching: A Improved BPNN and Blockchain-Based Approach for Privacy and Access Control

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model

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