New techniques for intelligent networks with machine learning

Abstract

each node integrates identity and behavior reputation. The reputation is dynamically updated by setting the new node evaluation period and phased update mechanism. Identity reputation is calculated by information disclosure and network characteristics, behavior reputation is calculated by information release and forwarding, and rewards or punishments will be given to self-correction of information behavior or blocking of bad information. The simulation results show that compared with the traditional trust evaluation mechanism, setting rewards and punishments guidance can improve the accuracy of reputation evaluation. At the same time, reputation incentive can also inhibit the interaction of bad information while promoting the consciousness of reporting. Blockchain technology and its applications are also important problems. The paper entitled “Scientific research information management based on blockchain technology”, the authors combine the existing achievements and requirements in real applications. The paper explains characteristics of the blockchain technology and analyzes its disadvantages in scientific research information management compared with the traditional methods. The paper puts forward some suggestions for applications to provide a reference for the effective application of blockchain technology in scientific research information management. For combinational optimization problem, authors propose a simple yet effective discrete artificial bee colony (DABC) algorithm for solving quadratic assignment problems (QAPs) in the work entitled “A discrete artificial bee colony algorithm for quadratic assignment problem”. Typical QAP benchmark instances are selected from QAPLIB in order to conduct the simulation experiment where common performance metrics are used to evaluate the algorithm. The paper also investigates the influence factors of the algorithm’s performance. The results show that the proposed algorithm is a quite effective and a practical new approach for handling QAP problems. This special issue presents some of the important research work in Intelligent Networks. We believe this special issue will benefit the research community towards identifying challenges and disseminating the latest methodolo-gies and solutions to machine learning.