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Register a new userAnti-Money Laundering using Data Mining techniques
مكافحة غسيل الأموال باستخدام تقنيات التنقيب عن المعطيات
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Added by
Damascus University حلقة بحث
Publication date
2018
fields
Informatics Engineering
and research's language is
العربية
Created by
Baraa Youzbashi
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يعرض هذا البحث دراسة مرجعية حول استخدام تقنيات الذكاء الصنعي والتنقيب عن المعطيات في أنظمة مكافحة غسيل الأموال. نقارن بين عدة منهجيات متبعة في أوراق بحثية مختلفة بهدف تسليط الضوء على تطبيقات الذكاء الصنعي في حل مشاكل الحياة الواقعية.
This research presents literature review on using Artificial intelligence and Data Mining techniques in Anti Money Laundering systems. We compare many methodologies used in different research papers with the purpose of shedding some light on real life applications using Artificial intelligence
References used
Salehi, A., Ghazanfari, M., & Fathian, M. (2017). Data Mining Techniques for Anti Money Laundering. International Journal of Applied Engineering Research, 12(20), 10084-10094
El-Din, A. K., & El Khamesy, N. (2016). Data Mining Techniques for Anti-Money Laundering. International Journal of Computer Applications, 146(12), 28-33. doi:10.5120/ijca2016910953
R. Drezewski et al., The application of social network analysis algorithms in a system supporting money laundering detection, Inform. Sci. (2014), http://dx.doi.org/10.1016/j.ins.2014.10.015
Alexandre C., Balsa J. (2016) Integrating Client Profiling in an Anti-money Laundering Multi-agent Based System. In: Rocha Á., Correia A., Adeli H., Reis L., Mendonça Teixeira M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham
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تقترح هذه الورقة استخدام تقنيات استخراج المعرفة للكشف عن غسيل الاموال في الأنظمة المصرفية بالاضافة الى ذكر نظام مطبق للكشف عن غسيل الاموال باستخدام خوارزمية clope
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.
The objective of this paper is presenting an educational data
mining case study, by applying data mining techniques.
Data Mining
التنقيب في البيانات
التنقيب في بيانات المؤسسات التعليمية
برنامج ذكاء الأعمال الخاص بشركة مايكروسوفت
خوارزمية مايكروسوفت لأشجار القرار
خوارزمية مايكروسوفت للتجميع و الكشف عن الحالات الشاذة
خوارزمية مايكروسوفت لقواعد الارتباط
Educational Data Mining
SQL Server Business Intelligence Development Studio
Microsoft Decision Trees
Microsoft Clustering
Outlier Detection
Microsoft Association Rules
المزيد..
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.
Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15 minutes
late) and they are estimated to have an annual cost of several tens of billion dollars. This scenario makes
the prediction of flight delays a pr
imary issue for airlines and travelers. The main goal of this work is to
implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted
arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled
departure and arrival time) and weather conditions at origin airport and destination airport according to
the flight timetable. Airline flights and weather observations datasets have been analyzed and mined using
parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show
a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15
minutes we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of
60 minutes the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results
demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks
as MapReduce applications on the Cloud.
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