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Register a new userSupport marketing campaigns decisions in mobile Telecommunications to provide Location-Based services, by using Big Data technologies
دعم قرارات الحملات التّسويقيّة في شركات الاتصالات الخليوية لتقديم خدمات محددة الموقع باستخدام تقانات المعطيات الكبيرة
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تركز عملنا على تقديم آلية للجمع بين الزّبون النّشط و الزّبون الخام ، مما يساعد على معرفة الزبائن الموجودين في الموقع حتى و إن كانوا لا يجرون مكالمات عبر الشّبكة باستخدام هواتفهم.
Our proposed work is to introduce a combination of active and passive customer modes. And this will enhance the detecting the customers, even though they are not using their mobile by making a call.
References used
Bob Fox, Rob van den Dam and Rebecca Shockley, Analytics: Real-world use of big data in telecommunications, 2013
Tsang, M., Ho, S.-C., & Liang, T.-P. (2004). Consumer Attitudes Toward Mobile Advertising:An Empirical Study. International Journal of Electronic Commerce / Spring, 8, No.(3), 65–78
Min , C., Shiwen, M., & Yunhao , L. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209
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The study aimed to study the relationship between the use of the Syrian mobile
telecommunications companies' customers for social media (Facebook) for marketing
purposes, and their ability to influence customers at each stage of the purchase proces
s,
which includes:: the generation phase of the need, the stage of the search for alternatives,
the stage of evaluating the alternatives, the stage of making the purchase decision, and the
post-purchase phase. In order to achieve this, a main hypothesis was formulated with five
sub-hypotheses. The researcher used the questionnaire technique to collect the data
analyzed using statistical tests, the most important being the one-sample T. test and the
Pearson Correlation test. The researcher has reached several results, the most important of
which is: There is a strong positive correlation between the use of the Syrian mobile
telecommunications companies' customers for social media (Facebook) for marketing
purposes and all stages of the purchase process.
location-based service applications have become increasingly popular in recent years as the increased use of these services has prompted researchers to pay attention to many factors affecting quality of service, such as accuracy, availability, respon
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Through this study we will explain the application of data mining
and business intelligence using the data existed in the library of
the Arab International University. This data has been linked to the
data of the students on the academic system of
the university.
The study will also answer questions that affect the work of the
educational institution in general and the library in particular,
propose solutions to improve the work of the library and its
services, enhance library working methods, and specify indicators
related to the role of information resources in the educational
operation.
Most robotic industries depend on using (servo motors) and (stepper
motors) orcontinuous current motors (DC motors) for movement
transition, which increases the cost and complicates the robot’s
controlling process, as well as its driving circuits.
This essay deals with using pneumatic in the new design and
building of a robot’s arm, that can manage to do many tasks with a
much lower budget than the one needed for any of the above
mentioned methods, that’s because it can be used for tasks that need
high speed and capacity, but don’t need precision.
Educational data mining aims to study the available data in the educational field and extract the hidden knowledge from it in order to benefit from this knowledge in enhancing the education process and making successful decisions that will improve th
e student’s academic performance. This study proposes the use of data mining techniques to improve student performance prediction. Three classification algorithms (Naïve Bayes,J48, Support Vector Machine) were applied to the student performance database, and then a new classifier was designed to combine the results of those individual classifiers using Voting Method. The WEKA tool was used, which supports a lot of data mining algorithms and methods. The results show that the ensemble classifier has the highest accuracy for predicting students' levels compared to other classifiers, as it has achieved a recognition accuracy of 74.8084%. The simple k-means clustering algorithm was useful in grouping similar students into separate groups, thus understanding the characteristics of each group, which helps to lead and direct each group separately.
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