Mohammed Alfraihi

Mohammed Alfraihi

الرياض السعودية
٤ آلاف متابع أكثر من 500 زميل

نبذة عني

Technology and product executive leader and entrepreneur with experience in building…

الخبرة

  • رسم بياني Rize | رايز

    Rize | رايز

    Riyadh, Saudi Arabia

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    Riyadh, Saudi Arabia

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    Riyadh, Saudi Arabia

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    Riyadh, Saudi Arabia

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    Riyadh, Saudi Arabia

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    Riyadh, Saudi Arabia

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    Columbia, Maryland, USA

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    Newport Beach, California, United States

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    Newport Beach, California, USA

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    Bloomington, Indiana Area

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    Riyadh, Saudi Arabia

التعليم

التراخيص والشهادات

المنشورات

  • Improving the Standard AntClustering Algorithm Using Genetic Algorithms

    California State University, Fullerton

    Master's Thesis
    Abstract: This thesis presents an attempt towards the improvement of the Standard Ant Clustering Algorithm by using the techniques of Genetic Algorithms. Goals of this thesis consist of multiple phases. The world of ants consists of two types of objects: artificial ants and data items. The task of the artificial ants is to wander around the world for a set number of steps, and attempt to form clusters for each type of data items. Next, ants pick-up a data item if it believes…

    Master's Thesis
    Abstract: This thesis presents an attempt towards the improvement of the Standard Ant Clustering Algorithm by using the techniques of Genetic Algorithms. Goals of this thesis consist of multiple phases. The world of ants consists of two types of objects: artificial ants and data items. The task of the artificial ants is to wander around the world for a set number of steps, and attempt to form clusters for each type of data items. Next, ants pick-up a data item if it believes the location cell is not of a cluster. Additionally, if an ant is carrying a data item, it is expected to drop it off when it believes it falls within a cluster. During this process, any carrying ant (an artificial ant that is carrying a data item of any type) looks at a fixed neighborhood edge length to determine clusters existence. Edge length is anticipated to be relative to the world size, and it is not determined whether a larger or smaller edge would allow a higher clustering quality. This thesis will use the techniques of genetic algorithms and attempt to make use of biologically powered methods to maximize the clustering formation and come up with the best possible clusters that eventually will result into a new algorithm we will call ACAGA.

    عرض المنشور

عرض ملف Mohammed الشخصي الكامل

  • مشاهدة الأشخاص المشتركين الذين تعرفهم
  • تقديم تعارف
  • تواصل مع Mohammed مباشرة
انضم لعرض الملف الشخصي الكامل

ملفات شخصية أخرى مشابهة

أعضاء آخرون يحملون اسم ⁦⁩Mohammed Alfraihi