The aim of this study is to develop basic artificial neural network models in forecasting the in-sample gross domestic product (GDP) of Malaysia. GDP is one of the main indicators in presenting the macro economic condition of a country as set by the world authority bodies such as the World Bank. Hence, this study uses an artificial neural network-based approach to make predictions concerning the economic growth of Malaysia. This method has been proposed due to its ability to overcome multicollinearity among variables, as well as the ability to cope with non-linear problems in Malaysia’s growth data. The selected inputs and outputs are based on the previous literatures as well as the economic growth theory. Therefore, the selected inputs are exports, imports, private consumption, government expenditure, consumer price index (CPI), inflation rate, foreign direct investment (FDI) and money supply, which includes M1 and M2. Whilst, the output is real gross domestic product growth rate. The results of this study showed that the neural network method gives the smallest value of mean error which is 0.81 percent with a total difference of 0.70 percent. This implies that the neural network model is appropriate and is a relevant method in forecasting the economic growth of Malaysia.

The gross domestic product(GDP) measures the welfare of a nation’s economy through the aggregation of products and services produced in a nation. Although GDP is a proficient measure of the magnitude of the economy, many economists, environmentalists, and citizens have recently criticized the gross domestic product. The criticism stems from the fact that this measurement of domestic product does not account for environmental degradation and resource depletion. We need to estimate the environmentally adjusted net domestic product. The gross domestic product was 913 trillion won while environmental protection expenditure was 32.9 trillion won by monetary accounts of Korea, 2010. Loss of natural assets was 76.6 trillion emwon by emergy analysis of Korea, 2010. The Green GDP was accounted for 88.0% of the GDP to 803.5 trillion won.

Ascorbic acid is a great antioxidant and helps protect the body against pollutants. GDP-mannose pyrophosphorylase (GMPase) is a key enzyme in manufacturing GDP-mannose, a glycosyl donor for ascorbate and cell wall biosynthesis as well as for protein glycosylation. In this study, we described molecular cloning of a full-length cDNA from Potato (Solanum tuberosum L. cv. Jasim), using tuber. The cDNA isolated encoded a GDP-mannose pyrophosphrylase. The nucleotide sequence of pGMPC showed about 95%, 89% and 80% homology with S. tuberosum (AF022716), N. tabacum (AB066279) and A. thaliana (AF076484) cDNAs clone known as GMPase, respectively. We detected the expression of GMPase using RT-PCR. The highest expression of GMPase was found in stems, and the largest amount of ascorbic acid was also presented in stems. In contrast, the leaf showed minimal level of GMPase transcript and ascorbic acid content. We propose that GMPase expression patterns were similar to the changes of ascorbic acid content in the leaves treated with diverse stresses.