Learning Discriminative Features by Covering Local Geometric Space for Point Cloud Analysis

Wang, Changshuo; Ning, Xin; Sun, Linjun; Zhang, Liping; Li, Weijun; Bai, Xiao

doi:10.1109/tgrs.2022.3170493

Cited by 62 publications

(26 citation statements)

References 63 publications

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“…The repeated application of this process can continuously reduce the error, and finally, produce an output value with relatively little error. A BP neural network also contains hidden layer nodes, and it needs to select one layer or two layers, or multiple layers according to its own data, and there is no coupling relationship between nodes at the same layer (Wang et al, 2022). The following figure shows the structure of a three-layer BP neural network.…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Huang

2023

Front. Ecol. Evol.

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Previous machine learning models usually faced the problem of poor performance, especially for aquatic product supply chains. In this study, we proposed a coupling machine learning model Shapely value-based to predict the CCL demand of aquatic products (CCLD-AP). We first select the key impact indicators through the gray correlation degree and finally determine the indicator system. Secondly, gray prediction, principal component regression analysis prediction, and BP neural network models are constructed from the perspective of time series, linear regression and nonlinear, combined with three single forecasts, a combined forecasting model is constructed, the error analysis of all prediction model results shows that the combined prediction results are more accurate. Finally, the trend extrapolation method and time series are combined to predict the independent variable influencing factor value and the CCLD-AP from 2023 to 2027. Our study can provide a reference for the progress of CCLD-AP in ports and their hinterland cities.

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Section: Bp Neural Network Modelmentioning

confidence: 99%

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Huang

2023

Front. Ecol. Evol.

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Section: Word Direction Representationmentioning

confidence: 99%

“…The word directions are gained by training the language model, which uses a single-layer NN to perform the solution of the binary language model while obtaining the word direction representation. Based on this, the NN language model NNLM is planned (Wang et al, 2022 ). The model takes the first k words of the present word w t , w t − k −1 , …, w t −1 , as input and uses a NN to predict the conditional likelihood of the occurrence of the present word w t to obtain a word direction representation while training the language model.…”

Section: Theory and Model Constructionmentioning

confidence: 99%

Efficient recognition of dynamic user emotions based on deep neural networks

Zheng

2022

Front. Neurorobot.

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The key issue at this stage is how to mine the large amount of valuable user sentiment information from the massive amount of web text and create a suitable dynamic user text sentiment analysis technique. Hence, this study offers a writing feature abstraction process based on ON-LSTM and attention mechanism to address the problem that syntactic information is ignored in emotional text feature extraction. The study found that the Att-ON-LSTM improved the micro-average F1 value by 2.27% and the macro-average F value by 1.7% compared to the Bi-LSTM model with the added attentivity mechanisms. It is demonstrated that it can perform better extraction of semantic information and hierarchical structure information in emotional text and obtain more comprehensive emotional text features. In addition, the ON-LSTM-LS, a sentiment analysis model based on ON-LSTM and tag semantics, is planned to address the problem that tag semantics is ignored in the process of text sentiment analysis. The experimental consequences exposed that the accuracy of the ON-LSTM and labeled semantic sentiment analysis model on the test set is improved by 0.78% with the addition of labeled word directions compared to the model Att-ON-LSTM without the addition of labeled semantic information. The macro-averaged F1 value improved by 1.04%, which indicates that the sentiment analysis process based on ON-LSTM and tag semantics can effectively perform the text sentiment analysis task and improve the sentiment classification effect to some extent. In conclusion, deep learning models for dynamic user sentiment analysis possess high application capabilities.

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“…It is efficient and robust enough to identify a set of illnesses quickly and accurately. Deep learning–based applications can be successfully applied in different areas including uncertainty estimations [ 37 ], point cloud analysis [ 38 ], indoor objects detection [ 1 ], and road sign detection [ 6 ]. Deep learning techniques are widely applied in the medical field.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction

Afif

Ayachi

Said

et al. 2023

Multimed Tools Appl

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Since 2019, COVID-19 disease caused significant damage and it has become a serious health issue in the worldwide. The number of infected and confirmed cases is increasing day by day. Different hospitals and countries around the world to this day are not equipped enough to treat these cases and stop this pandemic evolution. Lung and chest X-ray images (e.g., radiography images) and chest CT images are the most effective imaging techniques to analyze and diagnose the COVID-19 related problems. Deep learning-based techniques have recently shown good performance in computer vision and healthcare fields. We propose developing a new deep learning-based application for COVID-19 segmentation and analysis in this work. The proposed system is developed based on the context aggregation neural network. This network consists of three main modules: the context fuse model (CFM), attention mix module (AMM) and a residual convolutional module (RCM). The developed system can detect two main COVID-19-related regions: ground glass opacity and consolidation area in CT images. Generally, these lesions are often related to common pneumonia and COVID 19 cases. Training and testing experiments have been conducted using the COVID-x-CT dataset. Based on the obtained results, the developed system demonstrated better and more competitive results compared to state-of-the-art performances. The numerical findings demonstrate the effectiveness of the proposed work by outperforming other works in terms of accuracy by a factor of over 96.23%.

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Learning Discriminative Features by Covering Local Geometric Space for Point Cloud Analysis

Cited by 62 publications

References 63 publications

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Efficient recognition of dynamic user emotions based on deep neural networks

Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction

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