No / Objectives The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. Methods and materials This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. Results and conclusions The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.

The shipboard power system supplies energy to sophisticated systems for weapons, communications, navigation, and operation. After a fault is encountered, reconfiguration of a shipboard power system becomes a critical activity that is required to either restore service to a lost load or to meet some operational requirements of the ship. Reconfiguration refers to changing the topology of the power system in order to isolate system damage and/or optimize certain characteristics of the system related to power efficiency. When finding the optimal state, it is important to have a method that finds the desired state within a short amount of time, in order to allow fast response for the system. Since the reconfiguration problem is highly nonlinear over a domain of discrete variables, the genetic algorithm method is a suitable candidate. In this thesis, a reconfiguration methodology, using a genetic algorithm, is presented that will reconfigure a network, satisfying the operational requirements and priorities of loads. Graph theory is utilized to represent the shipboard power system topology in matrices. The reconfiguration process and the genetic algorithm are implemented in MATLAB and tested on an 8-bus power system model and on larger power system with distributed generators by considering different fault scenarios. Each test system was reconfigured in three different ways: by considering load priority, without considering load priority, and by combining priority factor and magnitude factor. The test results accuracy was verified through hand checking.

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