scholarly journals A classification method for social information of sellers on social network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haoliang Cui ◽  
Shuai Shao ◽  
Shaozhang Niu ◽  
Chengjie Shi ◽  
Lingyu Zhou
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Mobile Phones ◽  
Classification Scheme ◽  
Social Information ◽  
Operating Environment ◽  
Network Applications ◽  
Text Information ◽  
Model Training ◽  
Deep Learning Model

AbstractSocial e-commerce has been a hot topic in recent years, with the number of users increasing year by year and the transaction money exploding. Unlike traditional e-commerce, the main activities of social e-commerce are on social network apps. To classify sellers by the merchandise, this article designs and implements a social network seller classification scheme. We develop an app, which runs on the mobile phones of the sellers and provides the operating environment and automated assistance capabilities of social network applications. The app can collect social information published by the sellers during the assistance process, uploads to the server to perform model training on the data. We collect 38,970 sellers’ information, extract the text information in the picture with the help of OCR, and establish a deep learning model based on BERT to classify the merchandise of sellers. In the final experiment, we achieve an accuracy of more than 90%, which shows that the model can accurately classify sellers on a social network.

A Festival-Wide Social Network Using 2D Barcodes, Mobile Phones and Situated Displays

2011 ◽  
Vol 3 (3) ◽  
pp. 14-30 ◽  
Author(s):  
Jakob Eg Larsen ◽  
Arkadiusz Stopczynski
Keyword(s):  
Social Network ◽  
Mobile Phones ◽  
Low Cost ◽  
The Novel ◽  
Client Application ◽  
Mobile Client ◽  
Music Festival ◽  
Network Applications ◽  
Network Interaction ◽  
Novel Approach

This paper reports on the authors’ experiences with an exploratory prototype festival-wide social network. Unique 2D barcodes were applied to wristbands and mobile phones to uniquely identify the festival participants at the CO2PENHAGEN music festival in Denmark. The authors describe experiences from initial use of a set of social network applications involving participant profiles, a microblog and images shared on situated displays, and competitions created for the festival. The pilot study included 73 participants, each creating a unique profile. The novel approach had potential to enable anyone at the festival to participate in the festival-wide social network, as participants did not need any special hardware or mobile client application to be involved. The 2D barcodes was found to be a feasible low-cost approach for unique participant identification and social network interaction. Implications for the design of future systems of this nature are discussed.

scholarly journals Efficient Use of GPU Memory for Large-Scale Deep Learning Model Training

2021 ◽  
Vol 11 (21) ◽  
pp. 10377
Author(s):  
Hyeonseong Choi ◽  
Jaehwan Lee
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Training Model ◽  
High Accuracy ◽  
Access Pattern ◽  
Learning Models ◽  
Training Models ◽  
Model Training ◽  
Access Patterns ◽  
Deep Learning Model

To achieve high accuracy when performing deep learning, it is necessary to use a large-scale training model. However, due to the limitations of GPU memory, it is difficult to train large-scale training models within a single GPU. NVIDIA introduced a technology called CUDA Unified Memory with CUDA 6 to overcome the limitations of GPU memory by virtually combining GPU memory and CPU memory. In addition, in CUDA 8, memory advise options are introduced to efficiently utilize CUDA Unified Memory. In this work, we propose a newly optimized scheme based on CUDA Unified Memory to efficiently use GPU memory by applying different memory advise to each data type according to access patterns in deep learning training. We apply CUDA Unified Memory technology to PyTorch to see the performance of large-scale learning models through the expanded GPU memory. We conduct comprehensive experiments on how to efficiently utilize Unified Memory by applying memory advises when performing deep learning. As a result, when the data used for deep learning are divided into three types and a memory advise is applied to the data according to the access pattern, the deep learning execution time is reduced by 9.4% compared to the default Unified Memory.

scholarly journals Detecting orientation of Brain MR scans using deep learning

2021 ◽  
Author(s):  
Chinmay Singhal ◽  
Nihit Gupta ◽  
Anouk Stein ◽  
Quan Zhou ◽  
Leon Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Free Text ◽  
Learning Models ◽  
Human Errors ◽  
Linguistic Barriers ◽  
Model Training ◽  
The Impact ◽  
Deep Learning Model ◽  
Made In

AbstractThere has been a steady escalation in the impact of Artificial Intelligence (AI) on Healthcare along with an increasing amount of progress being made in this field. While many entities are working on the development of significant deep learning models for the diagnosis of brain-related diseases, identifying precise images needed for model training and inference tasks is limited due to variation in DICOM fields which use free text to define things like series description, sequence and orientation [1]. Detecting the orientation of brain MR scans (Axial/Sagittal/Coronal) remains a challenge due to these variations caused by linguistic barriers, human errors and de-identification - essentially rendering the tags unreliable [2, 3, 4]. In this work, we propose a deep learning model that identifies the orientation of brain MR scans with near perfect accuracy.

scholarly journals Paddy Rice Imagery Dataset for Panicle Segmentation

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1542
Author(s):  
Hao Wang ◽  
Suxing Lyu ◽  
Yaxin Ren
Keyword(s):  
Deep Learning ◽  
Rice Field ◽  
High Spatial Resolution ◽  
Learning Model ◽  
Accurate Solution ◽  
Training Process ◽  
Field Phenotyping ◽  
Model Training ◽  
Public Datasets ◽  
Deep Learning Model

Accurate panicle identification is a key step in rice-field phenotyping. Deep learning methods based on high-spatial-resolution images provide a high-throughput and accurate solution of panicle segmentation. Panicle segmentation tasks require costly annotations to train an accurate and robust deep learning model. However, few public datasets are available for rice-panicle phenotyping. We present a semi-supervised deep learning model training process, which greatly assists the annotation and refinement of training datasets. The model learns the panicle features with limited annotations and localizes more positive samples in the datasets, without further interaction. After the dataset refinement, the number of annotations increased by 40.6%. In addition, we trained and tested modern deep learning models to show how the dataset is beneficial to both detection and segmentation tasks. Results of our comparison experiments can inspire others in dataset preparation and model selection.

scholarly journals Improving the efficiency of using deep learning model to determine shoreline position in high-resolution satellite imagery

2021 ◽  
Vol 310 ◽  
pp. 04002
Author(s):  
Nguyen Thanh Doan
Keyword(s):  
Deep Learning ◽  
Satellite Image ◽  
Mekong Delta ◽  
Learning Model ◽  
Training Dataset ◽  
Learning Technology ◽  
Model Training ◽  
High Resolution Satellite Imagery ◽  
The Impact ◽  
Deep Learning Model

Nowaday, expanding the application of deep learning technology is attracting attention of many researchers in the field of remote sensing. This paper presents methodology of using deep convolutional neural network model to determine the position of shoreline on Sentinel 2 satellite image. The methodology also provides techniques to reduce model retraining while ensuring the accuracy of the results. Methodological evaluation and analysis were conducted in the Mekong Delta region. The results from the study showed that interpolating the input images and calibrating the result thresholds improve accuracy and allow the trained deep learning model to externally test different images. The paper also evaluates the impact of the training dataset on the quality of the results obtained. Suggestions are also given for the number of files in the training dataset, as well as the information used for model training to solve the shoreline detection problem.

scholarly journals Gradient Descent Optimization in Deep Learning Model Training Based on Multistage and Method Combination Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Chuanlei Zhang ◽  
Minda Yao ◽  
Wei Chen ◽  
Shanwen Zhang ◽  
Dufeng Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gradient Descent ◽  
Optimization Methods ◽  
Adaptive Methods ◽  
Learning Model ◽  
Optimization Strategy ◽  
Combination Strategy ◽  
Network Training ◽  
Model Training ◽  
Deep Learning Model

Gradient descent is the core and foundation of neural networks, and gradient descent optimization heuristics have greatly accelerated progress in deep learning. Although these methods are simple and effective, how they work remains unknown. Gradient descent optimization in deep learning has become a hot research topic. Some research efforts have tried to combine multiple methods to assist network training, but these methods seem to be more empirical, without theoretical guides. In this paper, a framework is proposed to illustrate the principle of combining different gradient descent optimization methods by analyzing several adaptive methods and other learning rate methods. Furthermore, inspired by the principle of warmup, CLR, and SGDR, the concept of multistage is introduced into the field of gradient descent optimization, and a gradient descent optimization strategy in deep learning model training based on multistage and method combination strategy is presented. The effectiveness of the proposed strategy is verified on the massive deep learning network training experiments.

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