ABSTRACT
Recently, blockchain systems are being applied in various application fields by combining blockchain with existing legacy systems. In particular, the cryptocurrency payment transaction system to support digital financial transactions is emerging as an important issue. Nevertheless, the development and valuation of blockchain-based cryptocurrency transactions and application services are fluctuating. With the advent of the Untact era due to Covid-19 recently, the commercialization of cryptocurrency is becoming more focused. In addition, as technical constraints for the spread of commercialization, there are problems of reaching a fast consensus in a large-scale blockchain network, consuming excessive energy for calculation, and storing the entire blockchain for verification. We propose a lightweight blockchain transaction process modeling to overcome these problems and to enhance blockchain applicability in an application environment where computing resources are weak. In addition, we propose a lightweight transaction-based blockchain application model optimized for areas with weak computing and network resources such as vending machines and ATMs.
ABSTRACT
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage. © 2022 Global IT Research Institute-GiRI.
ABSTRACT
COVID-19 contact tracing, maintaining social distance from an infected person, and spotting the hotspots is a difficult and time taking process. Many traditional methods can be used for this purpose but these methods have limited capabilities. Using these methods the process becomes more complex, slower, and difficult. This process involves the storage and sharing of personal data that requires security and privacy. To address these issues, we have proposed a blockchain, AI, and IoT-based framework for COVID-19 contact tracing and distancing. In this framework, Every user registers on the blockchain and gets public and private key pairs, the access level of every user is defined in the smart contract. The activity data of the user is collected using the smartwatch, which ensures real-time and accurate data collection. The data is encrypted with the keys of the data owner and stored on the cloud. So in case of data leakage, no one can decrypt the data without keys, so in this way, the secure data storage issue is resolved. Every transaction is recorded on the blockchain, it makes auditing easy in case of any malicious data transaction. Every user can access data according to its access level defined in the smart contract, it ensures the security of data. By the analysis of data, the ML model predicts the infected person and spot the COVID-19 hotspot areas. © 2021 IEEE.
ABSTRACT
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.