No abstract

No abstract

No abstract

The pattern of deviation patterns can be identified from the results of cluster transactions and transactions that are transaction irregularities, will be detected. DBSCAN as a density-based clustering algorithm forms clusters that agglomerate and make it easier to detect unclustered data, which is considered as data noise (data outlier). The nature of density in the data clamping process will make it easier to determine noise data objects.The DBSCAN has two parameters, Eps and MinPts. The values entered in both parameters play a role in forming clusters. Stock trading transactions are stated as data objects to be clustered. The noise from clustering with DBSCAN shows outlier transactions, which have diferrent pattern with ordinary transactions. In the results of this clustering, the stock transaction pattern which includes outliers is obtained, marking the close occurs. This result can help to detect stock price manipulation in outlier transactions carried out by securities brokers

Intelligent Transportation System (ITS) is a concept to manage transportation based on technology development. Video from sur veillance cameras can be used for monitoring the number of vehicles and speed using digital image processing. Shadows on the vehicle is one of the noise that must be removed in order to obtain better accuracy. Shadow is caused by the reflection of objects exposed to the light. In this study, we combined two methods to eliminate shadows on moving vehicle, the subregion illumination transfer method and the background-based Gaussian mixture model. Foreground image is used for sub-Region Illumination Transfer and gamma decoding processes is used to detect the presence of shadows The detected shadow is removed by replacing it with the background in that position. Experiments are done by making simulated video of moving objects without shadows and objects that have a shadow. By using the proposed method, the shadow will be omitted, and the results are compared with the object without the shadow. The experimental results are: mean value of PSNR for objects moving closer to the camera with a light intensity of 0.8 is 53.47. While on the moving object with a small shadow area, we obtained an average PSNR of 51.87927dB.