Medical image segmentation is a technique for detecting boundaries in a 2D or 3D image automatically or semiautomatically. The enormous range of the medical image is a considerable challenge for image segmentation. Magnetic resonance imaging (MRI) scans to aid in the detection and existence of brain tumors. This approach, however, requires exact delineation of the tumor location inside the brain scan. To solve this, an optimization algorithm will be one of the most successful techniques for distinguishing pixels of interest from the background, but its performance is reliant on the starting values of the centroids. The primary goal of this work is to segment tumor areas within brain MRI images. After converting the gray MRI image to a color image, a multiobjective modified ABC algorithm is utilized to separate the tumor from the brain. The intensity determines the RGB color generated in the image. The simulation results are assessed in terms of performance metrics such as accuracy, precision, specificity, recall, F-measure, and the time in seconds required by the system to segment the tumor from the brain. The performance of the proposed algorithm is computed with other algorithms like the single-objective ABC algorithm and multiobjective ABC algorithm. The results prove that the proposed multiobjective modified ABC algorithm is efficient in analyzing and segmenting the tumor from brain images.
Measuring knowledge management (KM) effectiveness is a very important issue in organizations today. This study aims to develop a method to evaluate the effectiveness of KM under uncertainty in research centers (RCs) in Iran. To develop an evaluation, the relevant literature was reviewed to identify KM effectiveness criteria. Next, the judgments of the experts, specialists, scholars, and professionals in IT and KM systems and senior managers of Iran’s RCs were determined using a pairwise comparison questionnaire. Because linguistic terms are an integral part of human judgments that will influence the results of the research, a fuzzy Analytic Hierarchy Process (AHP) called the Extent Analysis (EA) method was used for data analysis and weight determination. Accordingly, 34 subcriteria extracted from the literature that are important in the evaluation of KM effectiveness were categorized into six main criteria as follows: human resources, leadership and center structure, knowledge creation and acquisition, knowledge storage and security, knowledge sharing, and knowledge utilization and updating. The findings indicate that human resources is the most important criterion based on both the AHP and fuzzy AHP methods. The other five criteria in descending order of importance are knowledge sharing, leadership and center structure, knowledge utilization and updating, knowledge creation and acquisition and knowledge storage and security. Finally, to test the validity and reliability of the proposed framework, we evaluated the effectiveness of KM system in nine of Iran’s RCs.
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