Efficient Secure Outsourcing of Large-Scale Convex Separable Programming for Big Data

2019 ◽  
Vol 5 (3) ◽  
pp. 368-378 ◽  
Author(s):  
Weixian Liao ◽  
Changqing Luo ◽  
Sergio Salinas ◽  
Pan Li
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Separable Programming ◽  
Secure Outsourcing

scholarly journals A tutorial on secure outsourcing of large-scale computations for big data

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 1406-1416 ◽  
Author(s):  
Sergio Salinas ◽  
Xuhui Chen ◽  
Jinlong Ji ◽  
Pan Li
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Secure Outsourcing

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.

Prospects for Cooperation Between Russia and the Republic of Korea in the Fields of Big Data, Network and Artificial Intelligence for the Development of the Digital Economy

2021 ◽  
Vol 65 (8) ◽  
pp. 51-60
Author(s):  
Yujeong Kim
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Large Scale ◽  
Technology Development ◽  
Technological Development ◽  
Digital Economy ◽  
Republic Of Korea ◽  
Data Network ◽  
Leading Position ◽  
The Government

Today, each country has interest in digital economy and has established and implemented policies aimed at digital technology development and digital transformation for the transition to the digital economy. In particular, interest in digital technologies such as big data, 5G, and artificial intelligence, which are recognized as important factors in the digital economy, has been increasing recently, and it is a time when the role of the government for technological development and international cooperation becomes important. In addition to the overall digital economic policy, the Russian and Korean governments are also trying to improve their international competitiveness and take a leading position in the new economic order by establishing related technical and industrial policies. Moreover, Republic of Korea often refers to data, network and artificial intelligence as D∙N∙A, and has established policies in each of these areas in 2019. Russia is also establishing and implementing policies in the same field in 2019. Therefore, it is timely to find ways to expand cooperation between Russia and Republic of Korea. In particular, the years of 2020and 2021marks the 30th anniversary of diplomatic relations between the two countries, and not only large-scale events and exchange programs have prepared, but the relationship is deepening as part of the continued foreign policy of both countries – Russia’s Eastern Policy and New Northern Policy of Republic of Korea. Therefore, this paper compares and analyzes the policies of the two countries in big data, 5G, and artificial intelligence to seek long-term sustainable cooperation in the digital economy.

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