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Breast cancer disease is considered to be the second leading reason for death among women. Unfortunately, even if the treatment of cancer started soon after diagnosis, the cancer cells may remain in the body, and cancer may recur. Various Machine Learning (ML) methods to predict breast cancer recurrence were applied recently, and the ML methods’ performance needs to be examined to determine the proper method for prediction. Usually, the datasets contain many features which may sometimes mislead the prediction process; as some features may lead to confusion or inaccurate prediction. Thus, in this study, two breast cancer recurrence datasets were statistically analyzed and further refined by Brain Storming Optimization algorithm (BSO). The proposed multi-stages technique consists of three main stages; first, the statistical feature selection methods (SFM) which statistically select the discriminative features based on importance ranking and features correlations and statistical hypothesis testing, to be passed to the second stage. The features are ranked based on their correlation with the class variable. The second stage namely the multi classifier (MC), which evaluates each method based on three classifiers and produces a combination of features performed by two SFM and three classifiers. In the third stage, the best combination of the selected features was recognized by the BSO algorithm to search for an optimal solution that produces the highest accuracy. In addition, the BSO algorithm has been modified to deal with the feature selection problem. The performance of the proposed technique was evaluated by stratified 10-fold cross-validation. As a result, the multi-stage learning technique showed to be effective in ranking the features and improved the classification accuracy for breast cancer recurrence.

We present a framework for road network management to assist road authorities in maintenance budget estimations and long-term maintenance strategies definition. Information about road conditions is obtained from monitoring. Available data are used to estimate and update prediction of degradation evolution. Our decision-making under uncertainty tool is based on a Markov decision process to minimize total expected costs not only from a financial perspective but also from social and environmental ones. This is achieved by combining both technical/condition indicators and performance indicators, i.e., end-user service levels, in the problem formulation. From a practical perspective, our tool can be adapted to different road authority's needs. In this paper we highlight three applications. First, the tool can be used to select and develop new, cost-effective monitoring techniques that sensor relevant condition indicators or end-user service levels. Second, the tool provides decision rules for efficient maintenance strategies as well as long-term policies for budget estimation. Finally, increasing budget restrictions, funding cuts are expected to increase future budgets, but also to have negative social and environmental impacts. This tool can be used to assess impacts of budget cuts. © 2014 Taylor & Francis Group, London..

International audience; A condition-based maintenance optimization approach is developed for the road-cracking problem in order to derive optimal maintenance policies that minimize a total discounted maintenance cost. The approach is based on a Markov decision process that takes into ac- count multiple actions with varying effects on future road performance. Maintaining the road consists of adding a new asphalt layer; however, as resurfacing actions are constrained by a maximum total road thickness, the maintenance decision is not only how thick a layer to apply, but also how much old road to remove. Each combination of these actions leads to different maintenance costs and different future degradation behaviours. The road state is modelled by a dependent bivariate deterioration variable (the longitudinal cracking percentage and the deterioration growth rate), for taking these different changes in the cracking patterns into account. Moreover, the sensitivity to cracking for existing roads can be reduced with the addition of new layers, and thus actions that can lead to states better than good-as-new have to be considered. A numerical analysis is provided to illustrate the benefits of the introduction of the deterioration speed in the decision framework, as well as the belief that initially building a road to its maximum thickness is not optimal. The trade-offs in the design decisions and the exploitation/maintenance costs are also explored.

International audience; We develop an optimal maintenance policy for a road section to minimize the total maintenance cost over the infinite horizon when some deterioration and decision parameters are not observable. Both perfect and imperfect maintenance actions are possible through the application of various thicknesses of resurfacing layers. We use a two-phase deterioration process based on two parameters: the longitudinal cracking percentage and the deterioration growth rate. Our deterioration model is a state-based model based on the state-dependent Gamma process for the longitudinal cracking percentage and the Bilateral Gamma process for the deterioration growth rate. Moreover the maintenance decision is constrained by a maximum road thickness that makes the maintenance decisions more complex as it becomes how much surface layer to add as well as to remove. Because only one of the two deterioration parameters is observable, we formulate the problem as a partially observed Markov decision process and solve it using a grid-based value iteration algorithm. Numerical examples have shown that our model provides a preventive maintenance policy that slows down the initiation as well as the propagation of longitudinal cracks and that may ameliorate the road state to a better than as-good-as-new one by altering its composition through additive resurfacing layers.

This work is a part of a research project for the development of new condition-based approaches for road maintenance optimization. The degradation pattern on interest is here the longitudinal cracking process due to cumulative fatigue (the traffic repetition leads to the occurrence of cracks in the road basement which grows up to the road surface). In this context, we have proposed a new theoretical deterioration model based on two dependent indicators -the observable deterioration measurement and the potential deterioration growth. Even if the construction of the model is based on practical considerations, its application in an operational context remains difficult. The objective of this communication is to study the applicability of such a model and propose some improvements. After analyzing the original model to highlight its strengths and limitations, we propose to revisit the definitions of the decision parameters while specifying the construction of the associated functions. A statistical inference procedure is discussed. A numerical example based on the original maintenance model is presented to illustrate the benefits of this approach that we will present as some “best practices” for future road pavement management.