Browsing by Author "Alemu, Getachew(PhD)"

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    Brain Tumor Detection and Segmentation Using Hybrid Intelligent Algorithms: Design and Implementation
    (Addis Ababa University, 2012-11) Megersa, Yehualashet; Alemu, Getachew(PhD)
    In brain tumor diagnosis, clinicians integrate their medical knowledge and brain magnetic resonance imaging (MRI) scans to obtain the nature and pathological characteristics of brain tumors and to decide on treatment options. However, manually detecting and segmenting brain tumors in today’s brain MRI, where a large number of MRI scans taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. Therefore, there is a need for computer aided brain tumor detection and segmentation from brain MR images to overcome the tedium and observer variability involved in the manual segmentation. As result a number of methods have been proposed in recent years to fill this gap, but still there is no commonly accepted automated technique by clinicians to be used in clinical floor due to accuracy and robustness issues. In this thesis, an automatic brain tumor detection and segmentation framework that consists of techniques from skull stripping to detection and segmentation of brain tumors is proposed with fuzzy Hopfield neural network as its final tumor segmentation technique. Through preprocessing, image fusion and initial tumorous slice classification, the final hybrid intelligent fuzzy Hopfield neural network algorithm based tumor segmentation, and tumor region detection and extraction is achieved. The performance of the proposed framework is evaluated on various MR images including simulated and real, normal and tumorous. Quantitatively the method is validated against available ground truth (manual detection and segmentation) using commonly used validation metrics. The final segmentation mean and standard deviation result in Jaccard similarity index, Dice similarity score, sensitivity and specificity are 0.8569 0.0896, 0.9186 0.0638, 0.9480 0.0402 and 0.9917 0.0387 respectively. Quantitative and qualitative segmentation result indicates the potential of the proposed framework.
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    Decentralized Motion Coordination Method Design using CO-FIELD Approach of SWARM AI Metaheuristics for Improving the Reliability of Bus Transit System
    (Addis Ababa University, 2014-11) Mitiku, Sisay; Alemu, Getachew(PhD)
    Bus transit system plays a major role in combating both air pollution and road congestion and is one of the most important modes of transportation. In spite all this however, it is not considered to be reliable mode of transportation by its customers. The complex nature of the transportation system in general and bus transit operation in particular makes it difficult for the application of traditional mathematical model. In this thesis, the problem of regulating and monitoring the reliability of a bus transit system using a SWARM artificial intelligence solution is addressed. The increasing availability of near-real time data from intelligent bus transit system makes the applicability of such solution more attractive. As the bus transit system is distributed and stochastically dynamic because of uncertain inter-stop trip time and uneven passenger distribution, the application of interaction based and emergent self-organized solution such as swarm ai solution is highly recommended. The problem is formulated as a distributed motion coordination problem. A gradient field (co-field) coordination model of swarm artificial intelligence which is inspired by the nature of naturally found fields such as electro statistic and electromagnetic fields is used to solve the proposed model. Multi-agent simulation model is used both to model the bus transit system and to iteratively design the SWARM artificial intelligence metaheuristics. The simulation is implemented with NetLogo integrated development environment so that the desired emergent phenomena is designed and evaluated. Line 31 of Ambessa Awtobis organization, Addis Ababa, Ethiopia, is taken as a case study to improve the reliability of the developed multi-agent simulation. Different simulation experiment is carried out and different measure of effectiveness of the system is collected. The result from the multi-agent simulation experiment shows that the proposed method is adaptive to wider passenger density scenarios. From the result, we can conclude that decentralized metaheuristics of control methods without any sort of formal mathematical model can be a viable solution for improving the bus transit system reliability problem. More over this method also helps to solve the problem of how effectively to utilize the increasingly available huge near-time data from intelligent transit system. Our recommendation is that a research on design support system of swarm Artificial intelligence solution, such as reducing a programming overhead for rapid prototyping of emergent phenomena is worth doing in the future.