The construction of Bezier curves is one of the curves that are commonly discussed in Computer-Aided Geometric Design (CAGD). This study focuses on cubic Bezier curve. The objectives in this study are to review the properties of cubic Bezier curve and construct the cubic Bezier curves. In this study, the expanding equations from the basis function of the curve is used to construct the cubic Bezier curve. Future researchers can expand the degree of the Bezier curves, which is more useful in Computer-Aided Design (CAD), CAGD and engineering. The next studies in Bezier curve are recommended as a contribution for further research.
Gold and all kinds of gold alloys are commonly used in the manufacture of jewelry, coins and inexchange for trade in many countries. In addition, gold can conduct electricity efficiently and withstandcorrosion. This has made gold becomes an important industrial metal in the late 20th century. It is alsoimportant for the investors and public to know the trend of changes on gold’s price in order to assist themin making a good decision on their business. This research is done to forecast the Malaysia gold’s priceby using artificial Neural Network (NN). The forecasting models are implemented by using AlyudaNeurointelligence software. A monthly gold’s price data from January 2013 until March 2018 is used andapplied to the models and comparing their error measures. The results show that the Conjugate GradientAlgorithm (CGA) is chosen as the best neural network algorithm to forecast gold price since it has ahigher value of correlation and R square with the best architecture design [2-5-1]. Then, the future priceof gold starting from April 2018 until December 2018 is forecasted by using the best model. Keywords: Malaysian gold price, forecasting, neural networks
Obesity has been linked to several heart diseases .Unfortunately, Malaysian today are the most overweight or obese people among Asian nations. That is, half of the population is overweight. These are serious social consequences. Therefore, specific and serious actions are needed to reduce or eliminate this threat. The rapid increase in obesity rates has directed the link with the factors such as low physical activities, ease of transportation, food intake and emotional factor. Thus, this study aims at identifying the most significant factors affecting obesity in Peninsular Malaysia. The factors that are detected to be the cause of obesity can help manage individuals with obesity problems. Fuzzy Analytic Hierarchy Process (F-AHP) approach was used for the identification and ranking of each factor. The method adopted was one of the most reliable methods for both identification and the ranking off the most significant factors affecting obesity. The study selected seven respondents and applied the calculations. The calculations consist of seven steps. Initially, the goals and criteria to construct the hierarchy process were determined. This followed by questionnaires distribution to the respondents by using the linguistic scale. After that, two parameters were calculated, these include geometric mean weight and fuzzy weight. Lastly, the fuzzy weight and all the calculation were defuzzified and normalized. The results show the factors that affect obesity the most in Peninsular Malaysia is physical inactivity with the weight of 41.6% after normalized. The physical inactivity has been ranked the first factor, the food intake ranked second, the third rank is emotional factor and lastly is technology. The precise identification and ranking are very meaningful to the management of obesity problem. Firstly, there is a need to increase healthy physical activity. Secondly, the need to change their diet according to experts. Lastly, they need to manage their emotions and technology well.
Corner point detection are the important technique for many image processing applications including image enhancement, object detection and pattern recognition. The purpose of this study is to detect the corner points of a map of Kariah Kampung Bukit Kapar image by using Harris Corner Detector. Corner points in an image represents a lot of important information of the image. Detection of corner points accurately is significant to image processing, which can reduce much of the calculations. In this study, the initial technique is smoothing the image and extract the boundary of the image. Then, Harris Corner Detector is used to detect the corner points by considering the amount of corner point detection and run time processing. This study proposed the Harris Corner Detector which can detect 154 points with 12.9552 second.
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