Aim: To build wounds volume(3D) and area(2D) measuring system and device. Background: The measurement of the wound depth has been troublesome due to difficulty fo the procedures, physicians mostly avoid inspecting the wound depth as it could cause wound inflammation and infection. Objective: To build a contactless device for measuring wound volume and develop the system to support the wound treatment process which offers precise measurement and wound healing progression. Methods: Build a machine to control and stabilize 3D-scanner over the wound using a servo motor and apply the image processing technique to calculate the wound's area and volume. Comparing the machine accuracy by using Archimedes's principle testing with various wound model sizes, made from folding clay and pork rinds. Results: The device and system generate an error value of less than 15% which is within a satisfactory level. Conclusion: Knowing the wound depth is vital for the treatment, direct contact to the wound area can cause inflammation, infection, and increase time to heal. This device will help physicians to get more insight into the wound and improve the treatment plan for the patients. There are certain limitations to be considered for future work. Firstly, different software components used in the image processing and estimation process could be integrated to enhance user experience. Secondly, it is possible to apply Machine Learning techniques to identify the wounded area on the wound image file.

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Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast gold prices. Nonetheless, studies employing the linear relationship method usually fail to explain the change in the pattern of the gold price. This study introduces a new paradigm that incorporates association rules and long short-term memory (LSTM) as a nonlinear-based method. For simulation, the proposed method was analyzed with data from Yahoo Finance from January 2010 to December 2020. The association rule was used to choose features relevant to the gold spot (GS) in the US Dollar Index (DXY). The LSTM forecast the gold price with a range of hyperparameter settings. The simulation results showed that the proposed method—the LSTM with GS and DXY, or LSTM-GS-DXY—resulted in low mean absolute percentage error (MAPE) metrics. In addition, the proposed LSTM-GS-DXY system outperformed the simple moving average (SMA), weight moving average (WMA), exponential moving average (EMA), and auto-regressive integrated moving average (ARIMA).

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