Starfruit classification based on linear hue computation

In this paper, a classification process to group starfruit into six maturity indices is proposed based on 1- dimensional color feature called hue, which is extracted from the starfruit image. As the original hue is quantified from the nonlinear transformation of the 3-dimensional Red, Green and Blue color, this paper proposes a linear hue transformation computation based on the 2 colors of Red and Green. The proposed hue computation leads to a reduced computational burden, less computational complexity and better class discriminant capability. The hue is then applied as the input for the maturity classification process. The classification process is based on the hypothesis that for each of the maturity index, certain area of the starfruit surface is supposed to have distinctive value of the hue. In this work, the said starfruit surface area is set as 70% of the total area and based on 600 samples, the proposed technique results in 93% classification accuracy

Analysis and classification of myocardial infarction tissue from echocardiography images based on texture analysis

Texture analysis is an important characteristic for automatic visual inspection for surface and object identification from medical images and other type of images. This paper presents an application of wavelet extension and Gray level cooccurrence matrix (GLCM) for diagnosis of myocardial infarction tissue from echocardiography images. Many of applications approach have provided good result in different fields of application, but could not implemented at all when texture samples are small dimensions caused by low quality of images. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposition images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not

Development of bambangan (Mangifera pajang) carbonated drink

Mangifera pajang Kostermans or bambangan is a popular fruit among Sabahan due to its health and economic values. However, the fruit is not fully commercialized since it is usually been used as traditional cuisine by local people. Thus, development of bambangan fruit into carbonated drink was conducted to produce new product concept. The objectives of this study were to conceptualize, formulate, evaluate consumer acceptance, and determine physicochemical properties and nutritional composition of the accepted product. Method used in conceptualising the product was based on questionnaire. The consumer acceptance was evaluated based on descriptive and affective tests with four product formulations tested. The physicochemical properties on carbon dioxide volume, colour, pH, total acidity, total soluble solid (TSS) and viscosity were highlighted, meanwhile nutritional composition on fat, protein, carbohydrates and energy content were determined. About 77% respondents gave positive feedback, and 69% respondents decided this product is within their budget. The formulation of 5% bambangan pulp, 70% water, 25% sugar and 0.2% citric acid was highly accepted in descriptive and affective tests with 4.4 and 6.39 mean scores, respectively. The physicochemical properties and nutritional composition of the acceptance product were in optimum value except for colour, total acidity and TSS. Overall, this study showed that the product has high potential to be commercialized as new product concept, and heritage of indigenous people can be preserved when this fruit is known regionally

Enterprise architecture development and implementation in public sector: The Malaysian perspective

Enterprise Architecture (EA) is gaining the attention from the public sector as a solution to improve the function of e-Government. However, public sector agencies are having difficulties with its development and implementation due to inflexibility and complexity of the agencies’ business function and information technology structures. The objective of this paper is to identify the challenges faced by the Malaysian public sector agencies that are in development and implementation phase of EA. In order to get the holistic perspective of EA development and implementation scenario in each organisation, a Balanced Scorecard (BSC) approach is applied. A multiple case study research approach is utilized to achieve this study objective. Data were collected through interviews with the agencies EA team, general observation during the EA workshops as well as review of EA related documents. The result shows there are twenty challenges identified which is consistent with other challenges stated in literature except for talent management issue. Thus, this provides a new insight on how the public sector should implement their EA as compared to any other organisation

Dynamic Forecasting method for Shariah-compliant Share Price of Healthcare sector in Malaysian Stock Exchange

The healthcare sector is the category of stocks relating to medical and healthcare goods or services. The healthcare sector includes hospital management firms, health maintenance organizations (HMOs), biotechnology and a variety of medical products. The objective of this research paper is to forecast the performance of share price for healthcare sector in Malaysia. The research methodology implemented in this study is forecasting method using autoregressive integrated moving average (ARIMA). Data selection in this study is share price of healthcare company sector namely IHH Healthcare Berhad. Result shows the ARIMA(1,1,1) model exhibits r-squared value of 0.184 and Akaike Information Criterion (AIC) value is -1.112. Residual diagnostics shows ARIMA (1,1,1) is reliable model for forecasting of healthcare sector in Malaysia. The findings from this study will help economists to analyze the stock market performance especially in healthcare sector in Malaysia. This result also will help investors to decide appropriate decision in portfolio selection of capital investment

Weighted Moving Average of Forecasting Method for Predicting Bitcoin Share Price using High Frequency Data: A Statistical Method in Financial Cryptocurrency Technology

Bitcoin is a type of cryptocurrency that implemented decentralized digital currency method. The transaction is monitored and validated by peer-to peer system using hash programming. These transactions are verified by network nodes through the use of cryptography and recorded in a public distributed ledger called a blockchain. The objective of this study is to forecast the Bitcoin exchange rate using weighted moving average method. Data selected in this study are selected hourly from 14th December 2017 until 18th December 2017. The forecasting method is using weighted moving average. Then, the validity of the forecasting model is validated using mean absolute percentage error (MAPE) calculation. Results indicated mean absolute percentage error is 0.72%. Therefore, the moving average method is considered as reliable forecasting method for Bitcoin exchange rate. The finding of this study will help investors to make best decision regarding suitable portfolio for their investment

Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction

The cryptocurrency is a decentralized digital money. Bitcoin is a digital asset designed to work as a medium of exchange using cryptography to secure the transactions, to control the creation of additional units, and to verify the transfer of assets. The objective of this study is to forecast Bitcoin exchange rate in high volatility environment. Methodology implemented in this study is forecasting using autoregressive integrated moving average (ARIMA). This study performed autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis in determining the parameter of ARIMA model. Result shows the first difference of Bitcoin exchange rate is a stationary data series. The forecast model implemented in this study is ARIMA (2, 1, 2). This model shows the value of R-squared is 0.444432. This value indicates the model explains 44.44% from all the variability of the response data around its mean. The Akaike information criterion is 13.7805. This model is considered a model with good fitness. The error analysis between forecasting value and actual data was performed and mean absolute percentage error for ex-post forecasting is 5.36%. The findings of this study are important to predict the Bitcoin exchange rate in high volatility environment. This information will help investors to predict the future exchange rate of Bitcoin and in the same time volatility need to be monitor closely. This action will help investors to gain better profit and reduce loss in investment decision

Evaluation of Risk Reduction for Portfolio in Islamic Investment Using Modern Portfolio Theory

Main objective of this study is to maximize expected return and in the same time lowering investment risk. The methodology implemented in this study is modern portfolio theory through diversification assets that has low or negative correlation factor. This study tried to discover the portfolio expected risk and portfolios risk for 3 stocks namely Top Glove Corporation Berhad, AirAsia X Berhad and Axiata Group Berhad. Data for the analysis is selected from June 2015 until September 2018 involving 40 monthly observations. Result indicates the correlation factor between Top Glove and Airasia X is negative; meanwhile other correlation is not significant. Therefore, the selection of these three stocks is complying with the requirement of modern portfolio theory. Result indicates there are nine optimal combinations that calculated in this study which are suitable to develop non-linear line of efficient frontier. Data shows with the increment weightage in Top Glove stock, the expected average return will be increase. This is because the mean average return for share price of Top Glove 3.65%. This is the highest return comparing to other two stocks. However the risk of share price of Top Glove is 8.6 %, which is on the risky side. In addition, this study concludes the portfolio can attained lower risk by combining three stocks. The important implication of this study is it will help investors to develop optimal investment to attain maximum expected return based on a given level of market risk