When Big Data Meets NFT: Challenges, Impacts, and Opportunities
by Qinuo Chen, Jingyao Guo, Bocheng Wei, Bangcheng Li, Jack Michael Kelly
Sustainable Big Data Analytics Process Pipeline Using Apache Ecosystem
by Jane Cheng and Peng Zhao
An Image-Based Ship Detector With Deep Learning Algorithms
by Peng Zhao, Yuan Ren, Hang Xiao
Decision-Making Approaches for Airport Surrounding Traffic Management
by Xiangfen Kong and Peng Zhao
Applied Big Data Analytics and Its Role in COVID-19 Research
by Peng Zhao and Xi Chen
A New Solution of The Social Distancing and Face Mask Monitor Using Deep Learning Algorithms
Authors: Bangcheng Li, Tristan Huang, and Xin Wang
Since the end of 2019, the COVID-19 pandemic has drastically affected the lives of people all around the world. The global economy has been deflating due to a loss of jobs, while face to face communication has been restricted to decrease the infection rate. Even though it has been six months, medical professionals are still unable to determine the end of the pandemic or when a vaccine will be developed. Based on this situation, scholars and researchers have pointed out that society will go through a long period of abnormality as governments continue to enforce social distancing and quarantines. This new way of life means many changes for us, such as online education, mandatory facial masks, and the vast majority of people working from home. In the current situation, people have begun to use artificial intelligence (AI) as a way to solve problems dealing with the pandemic. Johns Hopkins University Center for Systems Science and Engineering (CSSE) launched a global mapping website that tracks the spread of the virus, while in the medical field, image recognition software has been used to analyze viral gene sequences, which aids the effort of strain isolation. In almost all aspects of socially beneficial projects, AI technology has revealed its strong developmental potential. Even though the concept and technique of social distancing and face mask detections have been proposed, the level of industrial development has not been reached. The contribution of this research is to present the industrial solution. Besides, we summarize the current challenges and possible solutions in the future work.
Fake News Monitoring and Anti-rumor System Using Deep Learning and Blockchain
Authors: Tristan Huang, Bangcheng Li, and Xin Wang
Hundreds of posts spreading misinformation about COVID-19 are being left online, according to a report from the Center for Countering Digital Hate. Most recently, fake news and rumors were cited as a negative contributing factor to the COVID-19 pandemic and BLM movements. Irresponsible individuals and organizations published misleading information causing catastrophic consequences to society. In the age of social media, the ability to spread false information has increased exponentially. Though technology has fostered spread fake news and rumors, it can also help to terminate. Many technologies, such as AI-based fake new monitors and Blockchain-based platforms, have been developed to overcome the challenge. In 2017, Steve Huckle and Martin White examined how blockchains can be used to authenticate digital media by using an application capable of analyzing images and videos to verify the originality of media resources. In 2018, Thang Dinh and My Thai summarized the current uses of blockchain and AI technology and discussed potential future uses and integration into various industries. In this project, we will summarize and combine the existing algorithms in deep learning and blockchain to develop a new anti-fake news and rumors system. We introduce the basic idea of anti-fake news system based on blockchain technology in section 2. In section 3 we utilize LSTM model as predictor to detect fake news with python codes. The contribution of our project is presented in section 4. We come up with a new blockchain-based deep learning anti-fake news system, which can improve the ability to avoid fake news.