scholarly journals Measurement Method and Application of a Deep Learning Digital Economy Scale Based on a Big Data Cloud Platform

2022 ◽  
Vol 34 (3) ◽  
pp. 1-17
Author(s):  
Yanmei Zhao ◽  
Yixin Zhou
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Digital Media ◽  
Measurement Method ◽  
Financial Liberalization ◽  
Economic Globalization ◽  
Digital Economy ◽  
Cloud Platform ◽  
Short Term ◽  
Digital Transactions

In recent years, with the acceleration of the process of economic globalization and the deepening of my country's financial liberalization, the scale of international short-term capital flows has been extremely rapid. This article mainly studies the deep learning digital economy scale measurement method and its application based on the big data cloud platform. This article uses the indirect method to estimate the stock of renminbi circulating abroad. The results show that the application of big data cloud platforms can increase the development share of digital media and digital transactions in the digital economy, and optimize the structure of China's digital economy.

scholarly journals Big Data and Deep Learning-Based Video Classification Model for Sports

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Wang ◽  
Haiyan Zhang ◽  
Guoliang Yuan
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Short Term Memory ◽  
Classification Model ◽  
Video Classification ◽  
Short Term ◽  
Term Memory ◽  
Sports Video ◽  
Human Movements

Information technologies such as deep learning, big data, cloud computing, and the Internet of Things provide key technical tools to drive the rapid development of integrated manufacturing. In recent years, breakthroughs have been made in big data analysis using deep learning. The research on the sports video high-precision classification model in this paper, more specifically, is the automatic understanding of human movements in free gymnastics videos. This paper will combine knowledge related to big data-based computer vision and deep learning to achieve intelligent labeling and representation of specific human movements present in video sequences. This paper mainly implements an automatic narrative based on long- and short-term memory networks to achieve the classification of sports videos. In the classical video description model S2VT, long- and short-term memory networks are used to learn the mapping relationship between word sequences and video frame sequences. In this paper, we introduce an attention mechanism to highlight the importance of keyframes that determine freestyle gymnastic movements. In this paper, a dataset of freestyle gymnastics breakdown movements for professional events is built. Experiments are conducted on the data and the self-constructed dataset, and the planned sampling method is applied to eliminate the differences between the training decoder and the prediction decoder. The experimental results show that the improved method in this paper can improve the accuracy of sports video classification. The video classification model based on big data and deep learning is to provide users with a better user experience and improve the accuracy of video classification. Also, in the experiments of this paper, the effect of extracting features for the classification of different lifting sports models is compared, and the effect of feature extraction network on the automatic description of free gymnastic movements is analyzed.

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

AUTOMATIC EXCHANGE OF INFORMATION SEBAGAI BIG DATA DI BIDANG PERPAJAKAN

2018 ◽  
Vol 1 (1) ◽  
pp. 128-133
Author(s):  
Selvi Selvi
Keyword(s):  
Big Data ◽  
Developing Country ◽  
Research Method ◽  
Large Data ◽  
Economic Globalization ◽  
Literature Study ◽  
Related Literature ◽  
Exchange Of Information

Economic globalization between countries becomes commonplace. Differences in financial rules are used for many parties to practice the Basic Erosion and Shifting Profit (BEPS) which leads to state losses. In tackling it has been agreed to implement Automatic Exchange of Information (AEoI), which automatically converts data into large data in the field of taxation.The research method of this paper is a literature study which combines several related literature and global and national implications using secondary data.Drawing up the conclusion that AEoI challenges have been theoretically overcome by Indonesia as a developing country. However, practically mash has not been able to find out whether it can be overcome or not because Indonesia still has not implemented AEoI

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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