Secure Data Access Through Electronic Devices Using Artificial Intelligence

Author(s):  
T.P. Anithaashri ◽  
G. Ravichandran ◽  
Saiteja Kavuru ◽  
Sandesh Haribabu
2016 ◽  
Vol 96 (4) ◽  
pp. 5295-5314 ◽  
Author(s):  
Xiong Li ◽  
Saru Kumari ◽  
Jian Shen ◽  
Fan Wu ◽  
Caisen Chen ◽  
...  

2021 ◽  
Author(s):  
Mark Howison ◽  
Mintaka Angell ◽  
Michael Hicklen ◽  
Justine S. Hastings

A Secure Data Enclave is a system that allows data owners to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party’s access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave and provide a reference architecture for its implementation on the Amazon Web Services platform using managed cloud services.


Author(s):  
Abdul MATEEN ◽  
Abdul RAUF ◽  
Abdul HANAN ABDULLAH ◽  
Mahmood ASHRAF

Author(s):  
Marzook Khatri

Abstract: The deployment of 5G mobile communication networks is just getting started right now. There are numerous technologies available today, each capable of fulfilling activities such as enabling voice traffic via voice over IP (VoIP), providing broadband data access in mobile environments, and so on. However, there is a pressing need to implement technology that can bring all of these systems together into a single unified system. Because it is all about smoothly integrating terminals, networks, and applications, 8G presents a solution to this dilemma. In this work, an attempt is made to provide a study of various cellular technologies, such as 4G, 5G, 6G, 7G, and FG, as well as a detailed comparison between them. With the introduction of network virtualization and the implementation of 5G/IoT, mobile networks will become more complicated and offer more diverse services. This raises concerns about a considerable increase in the workload of network operations. Meanwhile, artificial intelligence (AI) is advancing rapidly and is projected to alleviate human resource shortages in a variety of industries. Similarly, the mobile industry is gaining traction in the application of artificial intelligence (AI) to network operations in order to improve the efficiency of mobile network operations. This paper will address the idea of using AI technology to network operations and will give various use examples to demonstrate that AI-driven network operations have a bright future. Keywords: 5G & 6G networks, Artificial Intelligence, Next generation network, Future Advancement.


Author(s):  
Денис Валерьевич Сикулер

В статье выполнен обзор 10 ресурсов сети Интернет, позволяющих подобрать данные для разнообразных задач, связанных с машинным обучением и искусственным интеллектом. Рассмотрены как широко известные сайты (например, Kaggle, Registry of Open Data on AWS), так и менее популярные или узкоспециализированные ресурсы (к примеру, The Big Bad NLP Database, Common Crawl). Все ресурсы предоставляют бесплатный доступ к данным, в большинстве случаев для этого даже не требуется регистрация. Для каждого ресурса указаны характеристики и особенности, касающиеся поиска и получения наборов данных. В работе представлены следующие сайты: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Портал открытых данных Российской Федерации, World Bank, The Big Bad NLP Database, Common Crawl. The work presents review of 10 Internet resources that can be used to find data for different tasks related to machine learning and artificial intelligence. There were examined some popular sites (like Kaggle, Registry of Open Data on AWS) and some less known and specific ones (like The Big Bad NLP Database, Common Crawl). All included resources provide free access to data. Moreover in most cases registration is not needed for data access. Main features are specified for every examined resource, including regarding data search and access. The following sites are included in the review: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Open Data portal of the Russian Federation, World Bank, The Big Bad NLP Database, Common Crawl.


2019 ◽  
Vol 6 (1) ◽  
pp. 317-335 ◽  
Author(s):  
Mélanie Bourassa Forcier ◽  
Hortense Gallois ◽  
Siobhan Mullan ◽  
Yann Joly

Inventions ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 4
Author(s):  
Ping-Hei Chen ◽  
Hyung Cho

Innovative and high-end techniques have been recently developed in academic institutes and are gradually being employed in our daily lives for improving living quality, namely, artificial intelligence (AI) technology, autonomous cars, hyper-loop for high-speed transportation, miniaturization of electronic devices, heat dissipation from cooling films to outer space, and so on [...]


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