Earticle

Recently, Internet technology has been applied to home appliances as well as electronic devices such as PCs, laptops and mobile phones, requiring improved networks with high speed and bandwidth to handle a variety of data. In particular, network management techniques to maintain load balance using Software Defined networking (SDN) are cited as one of the most promising paradigms. In this paper, we propose a Deep Learning Mechanism (DLMBN) mechanism (Deep Learning Mechanism on Blockchain) that optimizes the load balance that can occur in the network by deep learning some important information related to the load balance after connecting the information of multiple distributed controllers into the blockchain. The proposed mechanism binds and manages the load of each controller distributed over the network with a blockchain, thus reducing load time while dynamically balancing the load balance. In particular, deep learning technology was used to ensure that each controller classified as a group would not be biased to one side and would maintain a balanced load balance across the entire network. As a result of the experiment, the proposed mechanism improved the load balance retention time by 14.6% on average compared to the mechanism previously studied, and the efficiency of SDNs processed in multiple groups by 17.3% on average. In addition, the overhead of SDNs for each group was lowered by 7.9%.

The integrated development of artificial intelligence and wearable technology provides technical conditions for users to participate in quantifying themselves. However, in the current, the research on wearable devices and other technical products is very lacking. Based on the existing user acceptance model, this paper proposes a quantitative self-acceptance model for wearable device users, and puts forward relevant assumptions and provide advice on the development of wearable technology

The vocational guidance and advising education enrollment are one of the most important tasks in the enrollment process and promote the quality and reputation of the University. Admissions counseling offices at Universities and Colleges play a major role in vocational guidance and advising education enrollment. However, the support of these units is limited by office hours, speed and advisory efficiency, and besides, handling and answering questions process may also encounter obstacles such as: overload, misinformation, problem with the transmission, language barriers, expressions, limited time, support resources,… Thus we decided to do research to understand this situation. Then creating a dataset supports vocational guidance and advising education enrollment activities. We also design and integrate chatbot into the school system to support the admissions counseling process.