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As one of the emerging algorithms in the field of artificial immune systems (AIS), the dendritic cell algorithm (DCA) has been successfully applied to a number of challenging real-world problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understanding the algorithm. Moreover, previous investigations have(More)
As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the eld of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows , are reported. The(More)
Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data(More)
Wildfire propagation is a complex process influenced by many factors. Simulation models of wildfire spread, such as DEVS-FIRE, are important tools for studying fire behavior. This paper presents how the sequential Monte Carlo methods, i.e., particle filters, can work together with DEVS-FIRE for better simulation and prediction of wildfire. We define an(More)
As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) has been applied to a range of problems, particularly in the area of intrusion detection. Ideally, the intrusion detection should be performed in real-time, in order to continuously detect misuses, as soon as they occur. Consequently, the analysis process performed by an intrusion detection(More)