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Article
Redesign AlNajaf City Traffic Using Graph Theory
إعادة تصميم المخطط المروري لمدينة النجف الاشرف باستخدام نظرية البيان

Authors: Kadhim AlJanabi --- Mansoor Habeeb --- Anwar Nsaif
Journal: journal of kerbala university مجلة جامعة كربلاء ISSN: 18130410 Year: 2014 Volume: 12 Issue: 3 Pages: 141-150
Publisher: Kerbala University جامعة كربلاء

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Abstract

Traffic flow and tours represent one of the most important issues in what is known as city planning since their results show how the main street, hi ways, and intersections look like and how they are connected to each other to give the maximum performance and traffic flow during the different time intervals including the rush hours. In this paper we present a traffic model for AlNajaf City based on graph theory, Minimum Spanning Tree, and Shortest Path Algorithms. The model shows the best network paths and alternative tours for the traffic flow in the main streets and intersections in different rush hours. Different tools and software were used in the implementation of the proposed model, including MatLab( Matrix-Laboratory ) , AutoCad, and others.

يمثل تدفق حركة المرور ومساراتها واحدة من أهم القضايا فيما يعرف باسم تخطيط المدن لان نتائجها تظهر كيف تبدو شوارع المدن الرئيسية وتقاطعاتها وكيف أنها ترتبط مع بعضها البعض لإعطاء أفضل قدر من الأداء وتدفق حركة المرور خلال فترات زمنية مختلفة بما في ذلك ساعات الذروة . تقدم هذه الورقة نموذجا مقترحا لحركة المرور لمدينة النجف الاشرف على أساس نظرية البيان، خوارزميات شجرة الامتداد ذات الحد الأدنى وخوارزميات أقصر مسار .يعطي ألنموذج المقترح أفضل مسارات شبكة الطرق والمسارات ألبديله لتدفق حركة المرور في الشوارع الرئيسية والتقاطعات في ساعات الذروة المختلفة . وقد استخدمت أدوات وبرامج مختلفة في تنفيذ النموذج المقترح ، بما في ذلك ماتلاب، أو اتوكاد وغيرها


Article
Minimum Spanning Tree Algorithm and Connected Components for Skin Cancer Image Object Detection

Author: Hind Rostom Mohamed
Journal: Journal of Education for Pure Science مجلة التربية للعلوم الصرفة ISSN: 20736592 Year: 2014 Volume: 4 Issue: 1 Pages: 242-253
Publisher: Thi-Qar University جامعة ذي قار

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Abstract

In the present paper, a Skin Cancer Image Object Detection method based on Minimum Spanning Tree Algorithm and Connected Components is proposed. we propose minimum spanning tree algorithm which that capable of Segment with Extraction of connected boundaries for Skin Cancer Image Segmentation . The algorithm is proposed to create a graph using the local features minutiae points in skin cancer as objects image ,one can draw a map connect these point so the work will be able to segment any part of the skin cancer image by finding the map of the part by minimum spanning tree algorithm. The performance of the proposed detector compares favorably both computationally and qualitatively, in comparison Object Detection with Connected Components which are also based on surround influence .The last stage contains Extraction of connected components skin image edge detection . The proposed scheme can serve as a low cost preprocessing step for high level tasks such shape based recognition and image retrieval. The experimental results confirm the effectiveness of the proposed algorithm.


Article
Minimum Spanning Tree Algorithm for Skin Cancer Image Object Detection
كشف الكائن في صورة سرطان الجلد باستخدام الحد الادنى من خوارزمية الشجرة الممتدة

Author: Hind Rostom Mohamed هند رستم محمد شعبان
Journal: Journal of College of Education for Women مجلة كلية التربية للبنات ISSN: Print ISSN 16808738 /E ISSN: 2663547X Year: 2014 Volume: 25 Issue: 2 Pages: 502-510
Publisher: Baghdad University جامعة بغداد

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Abstract

This paper proposes a new method Object Detection in Skin Cancer Image, the minimum spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer Images only. The Skin Cancer Image Detection of test objects relies on their distances to the closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer Image.The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on different Skin Cancer Images. We obtained very good results . The experiment showed that the proposedmethod obtained very good results but it requires more testing on different types of Skin Cancer Images.

تقترح هذه الورقة طريقة جديدة لكشف حدود الكائن في صورة سرطان الجلد، الحد الادنى من خوارزمية الشجرة الممتدة (MST ) حيث يبنى واصف كشف الكائن على هيكل من الحد الأدنى للشجرة الممتدةمن خلال مجموعة التدريب المستهدفة من صورسرطان الجلد فقط. تعتمد عملية الكشف على الكائن في صورسرطانعلى الاختبار من خلال الحصول على مسافات الاقرب الى حافة تلك الشجرة . بينتالتجارب أن شجرة الامتداد الحد الأدنى ( MST) تؤدي بشكل جيد لا سيما في حالة ضبابية الصورة وفي صور سرطان الجلد التي تحتوي على ضوضاء عالي.تم تنفيذ الطريقة المقترحة لكشف كائن صورة سرطان الجلد واختبارها على مختلف الصور سرطان الجلد . حصلنا على نتائج جيدة جدا. وأظهرت التجربة أن الطريقة المقترحة يمكننا من خلالها الحصول على نتائج جيدة للغاية ولكنه يتطلب المزيد من التجارب على أنواع مختلفة من صور سرطان الجلد.


Article
An efficient approach for medical text categorization based on clustering and similarity measures

Author: Amal Hameed Khaleel
Journal: Misan Journal of Acodemic Studies مجلة ميسان للدراسات الاكاديمية ISSN: 1994697X Year: 2016 Volume: 15 Issue: 29 Pages: 113-131
Publisher: Misan University جامعة ميسان

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Abstract

AbstractThe huge amount of medical information available in the medical document, makes the use of automated text categorization methods essential in clinical diagnosis and treatment. Automatic categorization of a text can provide information about classes which a text belongs to. This paper can serve as a medical diagnosis tool for categorization patient records by propose text categorization algorithm based on the similarity cluster centers for the categorization of patients with eye diseases records. We propose VEMST algorithm as update to EMST algorithm by using variance to find cluster centers. A text categorization algorithm is developed using two similarity measures (cosine , common words) to classify the categorical data. The results showed that when the number and size of medical documents used great for training the classification accuracy increases, as we noticed when we use comparing medical terms method in the preprocessing phase, the accuracy is better than the use of frequency of all terms in medical document, as well as the execution time at least. Finally, we found the performance of our system when we use the cosine similarity measure is better than his performance with the use of the similarity of common words scale.

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