Neural Networks, C# and telecoms fraud detection final year project

One of the things I have regretted since I left university is that I have not followed up on my Final year Project [I will try and upload it soon, it was pretty smart.]. Bascially I made a system that created Telephone CDR records based off user determined call profiles. The data from these CDR was then passed into a neural network [Created using MATLAB] to see if the call history for a particalular telephone customer was fraudulent or not. It worked really well, and MATLAB was amazing for creating the nerual networks for training and validating.

I promised myself that I would create a little neural network framework in what ever language I was working with at the time so that I could learn more about the algorithms and functions of the network. MATLAB was great but it was easy to hide the detialed understanding of the inner workings of a neural network.

So, soon hopefully I will create a little C# neural network package. Just mainly as a learning exercise. I know there are a lot of resources out there that already do it in c#, but I don't really want to use them for many reasons. Some of them seem convulted, some of them seem very specialised and all of them will not really help me understand neural networks the way that I should, it will be MATLAB all over again.

On the neural network side of things I used a Multi-layer perceptron. Which had an astonishing high true-positive/negative rate.

Basically my next series if posts will be about my Final year project and the work that I did with neural networks. Additionally, I will try and talk about some of the c# aspects of me learning about neural networks again.

Technorati Tags
[feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed], [feed]

Related Wikipedia Documents
, , , , , , , , , , , , , , ,

My Related Documents

Related Amazon Books
Webkdd 2002 - Mining Web Data for Discovering Usage Patterns and Profiles: 4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers (Lecture Notes in Computer Science S.): /, Information Theory, Inference and Learning Algorithms: /, The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov Models, Fuzzy Logic with C++, Java and SymbolicC++: /, Mastering Matlab 7: /, A Guide to MATLAB: For Beginners and Experienced Users: /, Introduction to Neural Networks: /, Neural Networks for Pattern Recognition: /, Learning Bayesian Networks: /, Neural Networks (Grassroots S.): /, The Art of Computer Programming, Volume 4, Fascicle 2 - Generating All Tuples and Permutations: /, The Art of Computer Programming, Volume 4, Fascicle 3 - Generating All Combinations and Partitions: /

Related Images From Flickr