This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obtained rules from positive association rules and negative association rules strengthens to each other with a pretty good confidence score.
انتخابات الرئاسة الامريكية : الالية والاهداف مع اشارة خاصة الى انتخابات 2008-2012
Abstract
Oil is the most important natural resources in Iraq and represents the goal to others as well as Iraqi people. It is gift from God to all Iraqi people now and future. So we must maintain it and invest its revenue that achieve development in country and ensure the next generations' rights in it without external costs or negative externalities from extracted and invested it.
The most problems that we attempt to solve by this research are the exhausted, environmental degradation and theft from next generation that produced with oil contracts between Iraq and foreign companies. From here was th
... Show Moreيعد تحليل السلاسل الزمنية من المواضـيع الهامة في تفسير الظـواهر التي تحدث خلال فترة زمنية معينة. ان الهدف من هذا لتحليل هو الحصـول على وصف وبنـاء أنموذج مناسب من اجل اعطاء صورة مستقبلية واضحة للسلاسل الزمنية المدروسة وان السلاسل الزمنية اهم الادوات المستخدمة في بناء وتقدير والتنبؤ بالظواهر المختلفة وان الاستدامة المالية هي الحالة التي تكون فيها الدولة قادرة على الوفاء بالتزاماتها الحالية والمستقبلية من غي
... Show MoreThe production function forms one of the techniques used in evaluation the production the process for any establishment or company, and to explain the importance of contribution of element from the independent variable and it's affect on the dependent variable. Then knowing the elements which are significant or non-significant on the dependent variable.
So the importance of this study come from estimating the Cobb-Douglas production function for Al- Mansoor General Company for Engineering industries in Iraq during the period (1989-2001)
To explain the importance which effects the independent variable such as
(N
هذه الورقة البحثية تهدف الى مناقشة سبل تطوير التعليم العالي في العالم العربي والارتقاء به لخلق راسمال بشري وثقافي قادر على مواجهة تحديات العصر، فالتنمية الاقتصادية في العصر الحالي ما عادت تعتمد على المواد الاولية بل على القيمة الابداعية للانسان التي اصبحت اساسية في المنافسة الحالية بين الشعوب الانسانية و اصبح الفكر المبدع هو المنافس الحقيقي المهم حيث تصنع احدث الاجهزة الحديثة باحجام صغيرة ومواد اولية اقل
... Show MoreThe current research was aimed at the following:
1. Measurement of Personality Type Observer of the University students.
2. Identify the differences in Personality Type Observer among the University students according to variable of Sex (male / female). And according to variable of Specialization (scientific / literary)
3. Measurement of Withdrawal of the University students.
4. Identify the differences in Withdrawal among the University students according to variable of Sex (male / female). And according to variable of Specialization (scientific / literary).
5. Identify the relationship between Personality Type Observer and Withdrawal.
To achieve this aims of the research, the researchers set up the instrument is scale
The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue
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