Transportation infrastructure could be vulnerable to local manifestations of global climate change, such as storm frequencies and durations of seasons. To adapt, transportation agencies need methodologies for reprioritizing their assets subject to the new sources of vulnerability. Prioritizing assets is nontrivial when criteria assessments and owner/operator preferences are considered in conjunction with the possible climate scenarios. Few efforts to date have addressed these scenarios in a priority setting for infrastructure asset management in the literature. This paper extends a scenario-based multicriteria decision framework that can assist decision makers in effectively allocating limited resources to adapt transportation assets to a changing climate. The framework is demonstrated with one of the most susceptible metropolitan transportation systems in the United States, the Hampton Roads region in coastal southeastern Virginia. First, the high-level goals of a long-range transportation plans are used in a traditional multicriteria analysis to generate a baseline prioritization of assets. Next, several scenarios that incorporate and combine a variety of climate conditions are identified. Finally, the scenarios are used to adjust the initial criteria weighting, which results in several reprioritizations of the assets. The results help to identify the most influential scenarios and characterize the sensitivity of the baseline prioritization across multiple scenarios. With these results, additional scientific and investigative efforts can be focused effectively to study and understand the influential scenarios.

Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and otherArtificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem.

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