Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.

Telecommunications networks have become an important social infrastructure, and their robustness is considered to be a matter of social significance. Conventional network planning methods are generally based on the maximum volume of ordinary traffic and only assume explicitly specified failure scenarios. Therefore, present networks have marginal survivability against multiple failures induced by an extraordinarily high volume of traffic generated during times of natural disasters or popular social events. This paper proposes a telecommunications network planning method based on probabilistic risk assessment. In this method, risk criterion reflecting the degree of risk due to extraordinarily large traffic loads is predefined and estimated using probabilistic risk assessment. The probabilistic risk assessment can efficiently calculate the small but non-negligible probability that a series of multiple failures will occur in the considered network. Detailed procedures for the proposed planning method are explained using a district mobile network in terms of the extraordinarily large traffic volume resulting from earthquakes. As an application example of the proposed method, capacity dimensioning for the local session servers within the district mobile network is executed to reduce the risk criterion most effectively. Moreover, the optimum traffic-rerouting scheme that minimizes the estimated risk criterion is ascertained simultaneously. From the application example, the proposed planning method is verified to realize a telecommunications network with sufficient robustness against the extraordinarily high volume of traffic caused by the earthquakes.

In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.