Comparative Study of Machine Learning Algorithms and Correlation Between Input Parameters

The availability of big data and computing power have triggered a big success in Artificial Intelligence (AI) field. Machine Learning (ML) becomes major highlights in AI due to the ability of self-improved as it is fed with more data. Therefore, Machine Learning is suitable to be applied in financial industry especially in detecting financial fraud which is one of the main challenges in financial system. In this paper, 15 different types of supervised machine learning algorithms are studied in order to find the highest accuracy that should be able to detect credit card fraudulent transactions. The best algorithm among these algorithms is then further used and studied to find the correlation between the input variables and the accuracy of the results produced. The results have shown that Multilayer Perceptron (MLP) produced the highest accuracy among the 15 other algorithms with 98% accuracy of detection. Besides that, the input parameters also play an important role in determining the accuracy of the results. Based on the result, when input parameter known as ‘V4’ decreased, the recorded accuracy has increased to 99.17%

Multi-type Noise Removal in Lead Frame Image Using Enhanced Hybrid Median Filter

Image filtering technique plays a very important role in digital image processing. It is one of the major steps in image enhancement and restoration. This filtering technique can remove noise and preserve the details of the image for feature extraction processes. However, filtering process can still be considered as a huge challenge for image filtering technique. Common noises in the image such as Salt & Pepper, Gaussian, Speckle, and Poisson Noise are still posing problems in filtering process where the quality and the originality of the images can be degraded and disturbed. Meanwhile, a single filtering technique is usually fit to deal with only certain specific noise. This paper presents an enhanced Hybrid Median Filter (H6F) technique to improve image filtering process. The technique involves 3x3 sub-mask and determination of new pixel value from the median value of the three steps which are the median calculation of ‘+’-neighbours, median calculation of all sub-masks and selection of centre pixel value. The H6F technique has been computed on lead frame inspection system. The results have shown that the technique has been able to remove multi-type of noises efficiently and produce exceptionally low Mean-Square Error (MSE) while consuming the acceptable amount of execution time when compared to other filtering techniques