Abstract With the increasing of frequency and destructiveness of product‐harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product‐harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product‐harm event's evolution; ultimately, nine risk‐forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product‐harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self‐organising data mining (SODM). Further, an SODM‐based multiple classifier fusion (SB‐MCF) model was presented for the risk prediction related to a product‐harm event. The experimental results based on 165 Chinese listed companies indicated that the SB‐MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB‐MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case‐based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm).

Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and traditional blob tracking methods is unable to separate the vehicles in the merged blobs. We use vehicle shape models, in addition to camera calibration and ground plane knowledge, to detect, track and classify moving vehicles in presence of occlusion. We use a 2-stage approach. In the first stage, hypothesis for vehicle types, positions and orientations are formed by a coarse search, which is then refined by a data driven Markov chain Monte Carlo (DDMCMC) process. We show results and evaluations on some real urban traffic video sequence using three types of vehicle models