Elliott Wave Pattern Recognition for Forecasting GBP/USD Foreign Exchange Market

This research presents an approach to the Elliott wave pattern implicates a forecast of future movements in foreign exchange (forex) rates of the previous movement inductive analysis. Elliott wave is defined that each individual wave has its own characteristic or pattern, which as expected reflects the psychology of the moment. The forex market is one of the utmost intricate markets through the characteristics of high volatility, nonlinearity and irregularity. Meantime, these characteristics also make it very difficult to forecast forex. The problem is contained pattern recognition, classification, and forecasting. The research objectives are to recognize the pattern using the Elliott wave pattern, to validate accuracy patterns classification using Linear Discriminant Analysis (LDA) and to forecast short-term forex market using Elliott wave method. LDA is employed to obtain in term of classification’s accuracy between 2 classes of selected data. The result shows the accuracy selected data is equal to 99.43%. Among of three levels of Fibonacci retracement which are 38.2%, 50.0%, and 61.8% results, the 38.2% shows the best forecasting for GBP/USD currency by using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (r) as the statistical measurements

Implementation of high dynamic range rendering on acute leukemia slide images using contrast stretching

Acute leukemia is one of the critical disease that requires immediate treatment due to the rapid progression and accumulation of the cancerous cells. In recent years, image processing techniques had been explored to enhance the diagnosis of acute leukemia. However, microscopic image captured from the light microscope usually has poor quality due to the capability of the camera and improper operation by human operator. High Dynamic Range (HDR) imaging technique has been explored to solve the problem by increasing the dynamic range of the images captured. This paper presents a HDR rendering technique by using contrast stretching technique to enhance the morphological features of blast cells. The technique called Partial contrast stretching had been used to render HDR image. The results showed that the proposed method had enhanced the overall contrast and morphological features of the blast cells in the acute leukemia slide images

Image segmentation for Acute Leukemia Cells using color thresholding and median filter

Acute leukemia is a kind of the malignant disease which may lead to death due to its characteristic of rapid development of immature blood cells. Recently, several image processing techniques have been implemented to assist the task of acute leukemia diagnosis. The segmentation of acute leukemia cells is an important key to determine the accuracy of its classification task. This paper proposed a combined technique of color thresholding based on the RGB color information from acute leukemia slide images and median filter to segment the leukemia cells from the unwanted regions such as background and red blood cells. The presented results proved that the proposed technique was successfully segmented the acute leukemia cells from the Acute Myeloid Leukemia and Acute Lymphocytic Leukemia slide images, with the average accuracy rate of 97.63% and 97.64% respectively. Therefore, the proposed image segmentation technique could benefits the classification process of acute leukemia