scholarly journals Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chenyuan Wang
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Value Chain ◽  
Evaluation Method ◽  
Learning Model ◽  
Coordination Mechanism ◽  
Good Effect ◽  
Wavelet Domain ◽  
Residual Learning ◽  
Management Concepts

This paper mainly analyzes the current situation of e-commerce in domestic SMEs and points out that there are limited initial investment and difficulty in financing in China’s SMEs; e-commerce control is not scientific; e-commerce personnel of SMEs are not of high quality, in the case of improper setting of the e-commerce sector and shortage of talents, rigid management model, and outdated management concepts. By using the loss function and the value chain management theory of the deep learning in the stationary wavelet domain residual learning model, the e-commerce model of SMEs is newly constructed, and the e-commerce department as the core department of the enterprise is proposed. By training the optimal parameters of the deep residual network and comparing the results with other models, the method of this paper has a good effect against the sample. The original loss function based on the residual learning model deep learning is modified to solve the original model fuzzy problem, which improves the effect and has good robustness. Finally, based on the wavelet residual depth residual evaluation method, this paper evaluates the application effect of this model and proposes relevant suggestions for improving this model, including rationalizing and perfecting the external value chain coordination mechanism, establishing the e-commerce value chain sharing center, and promoting integration of e-commerce business, strengthening measures and recommendations in various aspects of e-commerce information construction. At last, taking the business activities of a company as an example, applying the theory described in this paper to specific practice proves the feasibility and practical value of the theory.

Dissimilarity-Based Regularized Learning of Charts

2021 ◽  
Vol 17 (4) ◽  
pp. 1-23
Author(s):  
Prerna Mishra ◽  
Santosh Kumar ◽  
Mithilesh Kumar Chaube
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Structural Variation ◽  
Learning Model ◽  
Discriminative Power ◽  
Learning Models ◽  
Dissimilarity Index ◽  
Regularization Parameters ◽  
Learned Features

Chart images exhibit significant variabilities that make each image different from others even though they belong to the same class or categories. Classification of charts is a major challenge because each chart class has variations in features, structure, and noises. However, due to the lack of affiliation between the dissimilar features and the structure of the chart, it is a challenging task to model these variations for automatic chart recognition. In this article, we present a novel dissimilarity-based learning model for similar structured but diverse chart classification. Our approach jointly learns the features of both dissimilar and similar regions. The model is trained by an improved loss function, which is fused by a structural variation-aware dissimilarity index and incorporated with regularization parameters, making the model more prone toward dissimilar regions. The dissimilarity index enhances the discriminative power of the learned features not only from dissimilar regions but also from similar regions. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.

Abstract WP269: Automatic Segmentation of Cerebral Arteries in MRA TOF Using Deep Learning

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Leonard L Yeo ◽  
Melih Engin ◽  
Robin Lange ◽  
Sethu R Boopathy ◽  
Yang Cunli ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Loss Function ◽  
Automatic Segmentation ◽  
Cerebral Arteries ◽  
Ground Truth ◽  
Learning Model ◽  
Cerebrovascular Diseases ◽  
Intracranial Arteries ◽  
Deep Learning Model

Purpose: Time-of-Flight (TOF) MRA is commonly used for grading cerebrovascular diseases. Analysis of cerebral arteries in MRA TOF is a challenging and time consuming task that would benefit from automation. Established image processing methods for automatic segmentation of cerebral arteries suffer from common artefacts such as kissing vessels (when two nearby vessels touch) and signal intensity drop (especially in the presence of pathology). Artificial intelligence models are promising candidates for resolving such artefacts. Here, we propose and assess the performance of a deep learning model for automatic segmentation of cerebral arteries in MRA TOF which is robust to common MRI artefacts. Methods: A 3D convolutional neural network (CNN) is proposed for automatic segmentation of intracranial arteries in MRA TOF. The neural network is trained with a custom loss function and residual blocks to penalize the occurrence of common artefacts such as kissing vessels. The model is trained and tested on a dataset consisting of 82 subjects (50 healthy volunteers and 32 patients with intracranial stenosis) following a 3-fold cross-validation method, i.e. 3 models are trained where each model is blind to one-third of the data in the training process to avoid bias. Manual segmentation of the arteries done by an expert reader are used as ground-truth for training and testing the model. Results: The proposed deep learning model achieved excellent accuracy compared against the ground truth (Dice score 0.89). Our proposed deep learning model outperformed a state-of-the-art neural network for image segmentation (3DU-Net, Dice score 0.85) and resulted in considerably less occurences of artefacts such as kissing vessels (9% of cases had segmentation artefacts for our model vs 16% for 3D U-Net). The proposed deep learning model was fast, taking only 2 seconds to produce a 3D model of the arteries on a laptop with a dedicated GPU. Conclusion: The proposed deep learning model accurately segments intracranial arteries in MRA TOF and is robust to common artefacts of MR imaging thanks to implementation of a custom loss function. The model can potentially increase the accuracy and speed of grading cerebrovascular diseases.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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