scholarly journals Piano Automatic Computer Composition by Deep Learning and Blockchain Technology

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 188951-188958
Author(s):  
Huizi Li
Keyword(s):  
Deep Learning ◽  
Automatic Computer ◽  
Blockchain Technology ◽  
Computer Composition

Deep learning based Blockchain solution for preserving privacy in future vehicles

2020 ◽  
pp. 1-14
Author(s):  
Varsha R ◽  
Meghna Manoj Nair ◽  
Siddharth M. Nair ◽  
Amit Kumar Tyagi
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Smart Devices ◽  
The Road ◽  
Blockchain Technology ◽  
Technological Advances ◽  
Previous Decade ◽  
Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

scholarly journals Perspective Cryptocurrency Projects Supported by China

2019 ◽  
Vol 7 (2) ◽  
pp. 37-44
Author(s):  
N. Asadova
Keyword(s):  
Deep Learning ◽  
Research And Development ◽  
Positive Attitude ◽  
Chinese Government ◽  
Bank Of China ◽  
Digital Currency ◽  
Blockchain Technology ◽  
International Consulting ◽  
Strict Regulation ◽  
Research Institute

In this article are discussed the most perspective cryptocurrency and blockchain projects that China is investing in. After the regulations regarding cryptocurrencies that is put forth by China, the Chinese government decided to create several financial bodies to regulate and develop the cryptocurrency. Despite the strict regulation of cryptocurrencies, China has been significantly investing in blockchain projects. China has developed the Digital Currency Research Institute (DCRI) of the People’s Bank of China — a research body under the aegis of PBOC that focuses on the research and development of digital currencies and blockchain-related technologies. China actively supports more than 40 platforms, mostly in such fields as AI, Deep Learning and Software. The Chinese government has shown a positive attitude towards blockchain technology. Blockchain and cryptocurrency come hand-in-hand (except a private chain where a token is unnecessary). In the nearest future, China plans to introduce a blockchain to the most different spheres. For this purpose, there will even double the volume of investment to 3 billion dollars, since the second quarter of 2018. “This technology can transform many spheres of our life. As soon as in the country pursue powerful technological policy, it is sure that even more companies will begin to work in the field of the blockchain” —the partner of the international consulting company PwC in Shanghai Chongg Chong Yin commented to journalists.

scholarly journals Blockchain for Deep Learning: Review and Open Challenges

2021 ◽  
Author(s):  
Muhammad Shafay ◽  
Raja Wasim Ahmad ◽  
Khaled Salah ◽  
Ibrar Yaqoob ◽  
Raja Jayaraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Single Point ◽  
Data Provenance ◽  
Learning Models ◽  
Data Types ◽  
Consensus Protocol ◽  
Blockchain Technology ◽  
Trusted Data ◽  
Learning Frameworks

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly efficient, robust, and secure deep learning frameworks.

scholarly journals Blockchain for Deep Learning: Review and Open Challenges

2021 ◽  
Author(s):  
Muhammad Shafay ◽  
Raja Wasim Ahmad ◽  
Khaled Salah ◽  
Ibrar Yaqoob ◽  
Raja Jayaraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Single Point ◽  
Data Provenance ◽  
Learning Models ◽  
Data Types ◽  
Consensus Protocol ◽  
Blockchain Technology ◽  
Trusted Data ◽  
Learning Frameworks

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly efficient, robust, and secure deep learning frameworks.

scholarly journals Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 32
Author(s):  
Tong Liu ◽  
Fariza Sabrina ◽  
Julian Jang-Jaccard ◽  
Wen Xu ◽  
Yuanyuan Wei
Keyword(s):  
Deep Learning ◽  
Transport System ◽  
Public Transport ◽  
Denial Of Service ◽  
Transport Systems ◽  
Public Transport System ◽  
Blockchain Technology ◽  
Novel Approach ◽  
Wide Range ◽  
Security Properties

A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.

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