Forecasting Exchange Rate Across Countries with Gold Price as Exogenous Variable Using Transfer Function and VARI-X Model

Investors and collectors hold gold as protection for their savings and wealth atlarge. Gold does not pay interest like treasure bonds or savings accounts, but current goldprices often reflect increases and decreases of an asset. This research aims to provide amodel for the relationship between the exchange rate, which is vital in exporting gold, andgold prices across countries. The Australia, Brazil, and South Africa exchange rates areused as a case study against the gold price. The ARIMA model is used for forecasting goldprice as an input for the Transfer Function and VARIX models. The Transfer Functionmodel only considers the relationship between gold prices as input with the exchange ratein each country, whereas the VARIX model also considers the interrelationship betweenexchange rates in these countries. Daily data is used for the period 1st June 2010 to the28th February 2018. The RMSE and MAPE are used as criteria for selecting the bestmodel. The results show that VARIX is the best model for forecasting the Australianexchange rate, while the Transfer function is the best model for forecasting South Africanand Brazilian exchange rates.

Analysis of Mortality Improvement on the Pension Cost Due to Aging Population

Aging is a good indicator in demographic and health areas as the lifespanof the elderly population increases. Based on the government’s Economic Outlook 2019,it was found that an aging population would increase the government pension paymentsas the pensioners and their beneficiaries have longer life expectancy. Due to mortalityrates decreasing over time, the life expectancy tends to increase in the future. Theaims of this study are to forecast the mortality rates in the years 2020 and 2025 usingthe Heligman-Pollard model and then analyse the effect of mortality improvement onthe pension cost (annuity factor) for the Malaysian population. However, this studyonly focuses on estimating the annuity factor using life annuities through the forecastedmortality rates. The findings indicated that the pension cost is expected to increase ifthe life expectancy of the Malaysian population increases due to the aging population inthe near future. Thus, to reduce pension costs and help the pensioners from insufficientfinancial income, the government needs to consider an extension of the retirement age infuture.

Sizing Optimization of Hybrid Photovoltaic-Wind-Battery System towards Zero Energy Building using Genetic Algorithm

A new topic of Zero Energy Building (ZEB) is getting famous in research areabecause of its goal of reaching zero carbon emission and low building cost. Renewableenergy system is one of the ideas to achieve the objective of ZEB. Genetic Algorithm (GA)is widely used in many research areas due to its capability to escape from a local minimalto obtain a better solution. In our study, GA is chosen in sizing optimization of thenumber of photovoltaic, wind turbine and battery of a hybrid photovoltaic-wind-batterysystem. The aim is to minimize the total annual cost (TAC) of the hybrid energy systemtowards the low cost concept of ZEB. Two GA parameters, which are generation numberand population size, have been analysed and optimized in order to meet the minimumTAC. The results show that the GA is efficient in minimizing cost function of a hybridphotovoltaic-wind-battery system with its robustness property

Gaussian Mixture Model for MRI Image Segmentation to Build a Three-Dimensional Image on Brain Tumor Area

A brain tumor is one of the deadly diseases that attack the central and nervoussystem. The treatment of brain tumor, need high accuracy and precision. Brain tumordetection through Magnetic Resonance Imaging (MRI) has two-dimensional output withthree perspectives, namely sagittal, coronal, and axial. These different perspectives needto be seen one by one to determine the location and size of the tumor. Tosolve the problem, this study constructs the three-dimensional visualization perspective ofMRI images. The tumor area in MRI image is segmented as a region of interest (ROI) byemploying the Gaussian Mixture Model (GMM) with Expectation-Maximization as theoptimization technique. These couple segmentation methods have revealed significant gainas a clear boundary of the tumor area to separate from the healthy part of the brain andan estimated tumor volume from sagittal, coronal, and axial perspectives. Furthermore,these findings have been successfully visualized in 3D construction of the tumor positionon the left side of the patient’s head with an estimated volume of 749mm3.

New Three-Term Conjugate Gradient Method with Exact Line Search

Conjugate Gradient (CG) methods have an important role in solving largescale unconstrained optimization problems. Nowadays, the Three-Term CG method hasbecome a research trend of the CG methods. However, the existing Three-Term CGmethods could only be used with the inexact line search. When the exact line searchis applied, this Three-Term CG method will be reduced to the standard CG method.Hence in this paper, a new Three-Term CG method that could be used with the exactline search is proposed. This new Three-Term CG method satisfies the descent conditionusing the exact line search. Performance profile based on numerical results show thatthis proposed method outperforms the well-known classical CG method and some relatedhybrid methods. In addition, the proposed method is also robust in term of number ofiterations and CPU time.

The Effects of Amplification and Dilution Agent in Transmitting Sin Nombre Virus (SNV) in Deer Mouse Population

Numerous studies have linked biodiversity with zoonotic disease control. However, researchers have warned against simply believing that the increase in biodiversity can reduce the infection disease in the community. They proposed that amplification effect (increase in biodiversity accompanied by an increase in disease prevalence) might sometimes occur. Thus, we formulated a deterministic model to consider the impact of an amplification or dilution agent on the SNV transmission in the deer mouse population. Bifurcation analysis was carried out to examine the combined influences of the environmental carrying capacity, the interspecific competition strength and the impact of amplification or dilution agent on the deer mouse population. Our results showed that the system with amplification agent required a higher carrying capacity or stronger interspecific strength to compensate for its amplification effect in suppressing the SNV prevalence; this situation explains the lack of reduction in SNV prevalence despite the presence of high biodiversity in some empirical studies. In this study, we highlight the importance of investigating the roles of the additional species in an assemblage to better understand their relationship with the SNV prevalence in deer mouse population.

Deep Learning for Social Media Sentiment Analysis

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

Stock Daily Price Regime Model Detection using Markov Switching Model

Changes in stock prices randomly occur due to market forces with reoccurrencepossibilities. This process, also known as the structural break model, is captured throughchanges in the linear model parameters among periods with the Markov Switching Model(MSwM) used for detection. Furthermore, using the smallest Akaike Information Criterion(AIC) value on all feasible MSwM alternatives formed for a daily stock price, the completeMSwM model with its Markov transition is determined. This method has been tested andapplied to daily stock price data in several sectors. The result showed that the number ofregime models coupled with its transition probability helped investors make investmentdecisions.

A Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting

ABSTRACTThis paper proposes A Hybrid Wavelet-Auto-Regressive Integrated MovingAverage (W-ARIMA) model to explore the ability of the hybrid model over an ARIMAmodel. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMAmodel using the Standardized Precipitation Index (SPI) drought data for forecastingdrought modeling development. SPI data from January 1954 to December 2008 used wasdivided into two - (80%/20% for training/testing respectively). The results were comparedwith the conventional ARIMA model with Mean Square Error (MSE) and Mean AverageError (MAE) as an error measure. The results of the proposed method achieved the bestforecasting performance.

Single-machine Integrated Production Preventive Maintenance Scheduling: A Simheuristic Approach

Preventive maintenance (PM) planning becomes a crucial issue in the real world of the manufacturing process. It is important in the manufacturing industry to maintain the optimum level of production and minimize its investments. Thus, this paper focuses on multiple jobs with a single production line by considering stochastic machine breakdown time. The aim of this paper is to propose a good integration of production and PM schedule that will minimize total completion time. In this study, a hybrid method, which is a genetic algorithm (GA), is used with the Monte Carlo simulation (MCS) technique to deal with the uncertain behavior of machine breakdown time. A deterministic model is adopted and tested under different levels of complexity. Its performance is evaluated based on the value of average completion time. The result clearly shows that the proposed integrated production with PM schedule can reduce the average completion time by 11.68% compared to the production scheduling with machine breakdown time.