An investigation into the use of macroeconometric model simulation and optimal control for policy planning in the Malaysian rubber and oil palm industry

This study examines the use of macroeconometric model simulation and optimal control techniques, for sectoral policy planning concerning the optimal allocation of rubber and oil palm in the Malaysian plantation industry. Optimal crop allocation, which illustrates sectoral planning problems in the developing economy, provides an alternative diversification strategy for tackling the commonly encountered problem of income instability associated with the exports of primary commodities. Rubber and palm oil contribute significantly to the Malaysian economy and this leads to the selection of a macromodelling approach as the appropriate methodology for studying the impact of sectoral policy changes in the plantation industry. Both historical and futuristic policy experiments were carried out within the consistent framework of a macroeconometric model constructed for this study. An evaluation of the competitiveness of rubber and palm oil is also presented to complement the results from the macroeconometric model. The simulation results show that the pattern of crop allocation for the plantation industry was not optimal, especially for the rubber smallholding sector, and that an optimal strategy would have been to maximise the planting of oil palm during the 1970-1983 period. Policies for the projected period of 1984-1995 were examined by the optimal control technique. Besides providing some ideas of the optimal paths for various planting strategies for rubber and oil palm, the technique was also shown to be complementary to traditional simulation procedures in macromodel analysis. The results, though exploratory, support the formulation of policies which slightly favour reverting towards increased planting of rubber relative to oil palm for the 1990s. It was shown that the macromodel simulation and optimal control techniques could be effectively used for sectoral planning, and they provided a way of quantifying the impact of past and future sectoral policies on the country's economy. Ways of improving and adapting the model for actual applications were discussed

Cross-domain sentiment analysis model on Indonesian YouTube comment

A cross-domain sentiment analysis (CDSA) study in the Indonesian language and tree-based ensemble machine learning is quite interesting. CDSA is useful to support the labeling process of cross-domain sentiment and reduce any dependence on the experts; however, the mechanism in the opinion unstructured by stop word, language expressions, and Indonesian slang words is unidentified yet. This study aimed to obtain the best model of CDSA for the opinion in Indonesia language that commonly is full of stop words and slang words in the Indonesian dialect. This study was purposely to observe the benefits of the stop words cleaning and slang words conversion in CDSA in the Indonesian language form. It was also to find out which machine learning method is suitable for this model. This study started by crawling five datasets of the comments on YouTube from 5 different domains. The dataset was copied into two groups: the dataset group without any process of stop word cleaning and slang word conversion and the dataset group to stop word cleaning and slang word conversion. CDSA model was built for each dataset group and then tested using two types of tree-based ensemble machine learning, i.e., Random Forest (RF) and Extra Tree (ET) classifier, and tested using three types of non-ensemble machine learning, including Naïve Bayes (NB), SVM, and Decision Tree (DT) as the comparison. Then, It can be suggested that the accuracy of CDSA in Indonesia Language increased if it still removed the stop words and converted the slang words. The best classifier model was built using tree-based ensemble machine learning, particularly ET, as in this study, the ET model could achieve the highest accuracy by 91.19%. This model is expected to be the CDSA technique alternative in the Indonesian language

Optimization of growth media components for polyhydroxyalkanoate (PHA) production from organic acids by Ralstonia eutropha

We employed systematic mixture analysis to determine optimal levels of acetate, propionate, and butyrate for cell growth and polyhydroxyalkanoate (PHA) production by Ralstonia eutropha H16. Butyrate was the preferred acid for robust cell growth and high PHA production. The 3-hydroxyvalerate content in the resulting PHA depended on the proportion of propionate initially present in the growth medium. The proportion of acetate dramatically affected the final pH of the growth medium. A model was constructed using our data that predicts the effects of these acids, individually and in combination, on cell dry weight (CDW), PHA content (%CDW), PHA production, 3HV in the polymer, and final culture pH. Cell growth and PHA production improved approximately 1.5-fold over initial conditions when the proportion of butyrate was increased. Optimization of the phosphate buffer content in medium containing higher amounts of butyrate improved cell growth and PHA production more than 4-fold. The validated organic acid mixture analysis model can be used to optimize R. eutropha culture conditions, in order to meet targets for PHA production and/or polymer HV content. By modifying the growth medium made from treated industrial waste, such as palm oil mill effluent, more PHA can be produced.Malaysia. Ministry of Science, Technology and Innovation (MOSTI

preliminary

ABSTRACT As the palm oil industry progresses, its many aspect

preliminary

ABSTRACT As the palm oil industry progresses, its many aspect

Semi-supervised Learning for Sentiment Classification with Ensemble Multi-classifier Approach

Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance

Semi-supervised Learning for Sentiment Classification with Ensemble Multi-classifier Approach

Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance