bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular Docking of Alkaloid Compound SA2014 towards Cyclin D1 Protein in Cancer using Firefly Algorithm
Authors: Fikriya Zulfa Afiq , Irawan Mohammad Isa , Dyah Nurhayati Awik Puji 
Published: November 2019

Abstract
Molecular docking or ligand binding in proteins is a developing field of computing. Molecular docking can be used to find the most appropriate interaction pattern between protein receptors and ligands and become the basis for the drug discovery and design based structures. The development of efficient docking methods and algorithms will be very useful in drug discovery simulation. Firefly algorithm is one of the method that can be used for molecular docking simulations. Firefly algorithm is used to find the optimal conformation of proteins and ligands so that the binding energy of the whole system is minimized. In this research, proteinligand complexes from the Protein Data Bank (PDB) were used to test the performance of the algorithm. The results show that the firefly algorithm can be used to solve molecular docking. Then this algorithm is used to solve molecular docking of alkaloid compounds SA2014 from Cinachyrella anomala sea sponges towards cyclin D1 protein in cancer. The results show that the SA2014 ligand affinity for cyclin D1 protein was higher than doxorubicin (a type of chemotherapy drug) so that the SA2014 compound have a great potential as an anticancer.
Identification of Potential Landslide Disaster in East Java Using Neural Network Model (Case Study: District of Ponogoro)
Authors: Nisa’ Alvina Khairun , Irawan Mohammad Isa , Pratomo Danar Guruh 
Published: November 2019

Abstract
Indonesia is the largest archipelagic country in the world, which is geographically located in areas prone to natural disasters. One of the frequent occurrences of natural disasters is a landslide. East Java Province is one of the areas that has the potential for landslides. This is due to the topography of the most mountainous and rugged territory. Besides that, it also caused high levels of population density in the region of hills so that raises pressure on ecosystems. The tendency of the occurrence of landslides in an area can be connected with the equality of land characteristics and climate in other regions on a landslide in the past. To reduce the risk of disaster will be designed the software-based neural network for identification of potential avalanche areas. With potential landslide identification software, it can help to identify other locations that have similar physical and soil characteristics, so that the area can be suspected of being a potentially landslide area. The overall test results of this study are using Backpropagation artificial neural networks with 7 inputs, 15 hidden and 1 output. The training function used is Resilient backpropagation (RP) with an accuracy of testing data is 90.56% and MSE of 0.0944.
Numerical solution to Volterra Integro-Differential Equations of the second kind by hybrid one-step block method
Authors: Janodi M R , Majid Z A , Ismail F , Senu N 
Published: November 2019

Abstract
The hybrid block one-step method of order four is presented and implemented to solve first order Volterra Integro-Differential Equations (VIDEs). The technique is developed using Lagrange interpolation method. The numerical solutions of VIDEs will be solved at two-point concurrently using the proposed numerical method. Properties of the method such as consistency, order and region of absolute stability are explored. After that, the linear and nonlinear of VIDEs has been solved and the numerical results show that the accuracy and efficiency of the hybrid block method is better than the other methods which is RK4 and VIM.
Visualizing the Energy of Scattered Data by Using Cubic Timmer Triangular Patches
Authors: Ali F A M , Karim S A A , Dass S C , Skala V , Saaban A , Hasan M K , Hashim I 
Published: November 2019

Abstract
This paper discusses the application of the new cubic Timmer triangular patches constructed by Ali et al. [1] to interpolate the irregularly scattered data with C
1 continuity. In order to apply the cubic Timmer triangular patches for scattered data interpolation, the data is first triangulated by using the Delaunay algorithm, and then the sufficient condition for C
1 continuity is derived along the adjacent triangles. Two methods will be used to calculate the cubic Timmer ordinates on each triangle. The convex combination between three local schemes Ti
, i = 1,2,3 will be used to produce the C
1 surface everywhere. The proposed scheme will be tested to visualize one energy data set with irregular shape properties. Numerical and graphical results are presented by using MATLAB. Comparisons between the proposed scheme and existing schemes such as cubic Ball and cubic Bézier triangular patches are also carried out. The results indicate that the surface produced by cubic Timmer triangular patch is better than surface produced using cubic Ball and cubic Bezier triangular patches with overall coefficient of determination R2 value obtained to be larger than 0.9234.
Outlier detection in circular regression model using minimum spanning tree method
Authors: Muhammad Di Nur Faraidah , Satari Siti Zanariah , Zakaria Roslinazairimah 
Published: November 2019

Abstract
The existence of outliers in circular-circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using minimum spanning tree method. The proposed method is examined via simulation study with different number of sample sizes and level of contaminations. Then, the performance of the proposed method was measured using “success” probability, masking effect, and swamping effect. The results revealed that the proposed method were performed well and able to detect all the outliers planted in various conditions.
Rasch measurement model: a review of Bayesian estimation for estimating the person and item parameters
Authors: Binti Azizan Nurul Hafizah , Mahmud Zamalia Binti , Rambli Adzhar Bin 
Published: November 2019

Abstract
This paper focuses on the methods used for estimating the parameters in Rasch Measurement Model (RMM). These include the MLE and Bayesian Estimation (BE) techniques. The accuracy and precision of the parameter estimates based on these two MLE and BE were discussed and compared. A questionnaire is a well-known measurement instrument used by most of the researchers. It is a powerful tool for collecting data in survey research. It should be noted that the quality of a measurement instrument used plays a key role in ensuring the quality of data obtained in the survey. Therefore, it has become essential for the researchers to carefully design their questionnaire so that the quality of the data obtained can be preserved. Then, it is also vital for the researchers to assess the quality of the data obtained before it can be successfully used for further analysis. Review of the literature shows that RMM is a psychometric approach widely used as an assessment tool of many measurement instruments developed in various fields of study. At present, the Maximum Likelihood Estimation (MLE) techniques were used to estimate the parameters in the RMM. In order to obtain more precise and accurate parameter estimates, a certain number of sample size and normality assumption are usually required. However, in a small sample, MLE could produce bias, imprecise and less accurate estimates with bigger standard error. A proper selection of the parameter estimation techniques to deal with small sample and non-normality of data is required to obtain more precise and accurate parameter estimates. From the review, it reveals that BE has successfully dealt with the issues of small sample and non-normality of the data. It produced a more accurate parameter estimate with smallest Mean Squared Error (MSE), particularly in a small sample compared to MLE.
Prioritising critical success factors of TQM in Malaysia aerospace industry using fuzzy AHP
Authors: Halim Faridah Abdul , Azman Adillah , Malim Muhammad Rozi 
Published: November 2019

Abstract
Malaysia has set a target to become the first aerospace nation in South East Asia by 2030. In efforts to ensure industry players are able to achieve the target, the critical success factors (CSFs) that affecting the successful implementation of total quality management (TQM) in aerospace industry, especially in Malaysia, need to be identified and ranked. Ranking CSFs is a sensitive task that requires extra attention. Self-judgment, previous experiences and references by industry experts, including the existence of uncertainty in decision making, results in inaccurate ranking. Therefore, this study aims to prioritise (identify and rank) the CSFs for successful implementation of TQM in Malaysia aerospace industry (manufacturing sector). Through an in-depth review of the literature, 11 CSFs were identified and categorised into four main criteria. These criteria were analysed empirically using Fuzzy Analytic Hierarchy Process (FAHP) approach to rank the CSFs based on their relative importance weights. FAHP approach was used since the judgments from industry experts have been taken into account as recommended by National Aerospace Industry Coordinating Office (NAICO). The results showed that the main criterion for successful TQM implementation is culture and people with the highest weight of 0.434, followed by organising (0.296), systems and technique (0.151), and measurement and feedback (0.119). Therefore, the top management and decision makers need to give more attention on culture and people factors before implementing TQM which include employee involvement and role of quality department. However, the relationship between CSFs and the performance of TQM implementation need to be analysed further.
Tourism demand forecasting – a review on the variables and models
Authors: Khaidi Sarah Mohd , Abu Noratikah , Muhammad Noryanti 
Published: November 2019

Abstract
With the growth of the world’s tourism industry, researchers took advantage to conduct numerous studies in forecasting of tourism demand. The objective of this paper is to review the studies on tourism demand starting from 2010 to 2018 which varies on the explanatory variables, such as tourist income, exchange rate, gross domestic product, and others. In addition, this study also reviewed the models used to forecast and analyse tourism demand which are time-series model, econometric causal model and artificial intelligence model. The result from this review shows it is difficult to conclude which models performed the best for tourism demand. However, in most of the studies, combined models outperformed single model. Furthermore, the authors mentioned about the roles of tourism practitioners in the industry, tourism seasonality and suggestions for further studies in the future.
A Comparative Study between Ridge MM and Ridge Least Trimmed Squares Estimators in Handling Multicollinearity and Outliers
Authors: Affindi A N , Ahmad S , Mohamad M 
Published: November 2019

Abstract
Common problems found in multiple linear regression models are the existence of multicollinearity and outliers. These obstacles usually produce undesirable effects on least squares estimators. Ridge regression estimator is suggested in handling severe multicollinearity while robust estimators such as MM estimator and Least Trimmed Squares (LTS) estimator are recommended in tackling the outlier issues. An even worse scenario is when these two problems occur simultaneously. Combination of both leads to robust ridge regression methods which can be used to handle both conditions simultaneously. In this study, a comparative investigation is carried out to compare the performance between ridge MM and ridge LTS estimators. The Root Mean Square Error (RMSE) and Bias are obtained for each estimator to compare their performances. By using simulation study, Laplace and Cauchy distributions are used in introducing outliers to the simulated data with high multicollinearity ρ = 0.90, 0.95 and 0.98 for sample sizes n=25, 50 and 100. From the results, it is found that Ridge LTS is the best estimator for many combinations of error distributions and degrees of multicollinearity. Similar results were obtained when using two sets of real data.
An approach of estimating school enrolment with Reconstructive Cohort Approach
Authors: Pajankar Vishal D , Srivastava Sridhar 
Published: November 2019

Abstract
Policy makers and planners require enrolment data for undertaking an exercise of future planning in respect of educational development. The enrolment projection is an important planning tool to assist in long-range planning and developing action-plans related to resource allocation and system capacity. Various statistical and mathematical models or methods are discussed in past literature and articles which have some prior condition about certain parameters. Accuracy in data is another crucial aspect during the estimation. Reconstructive Cohort could be one such best approach which may be used to estimate the school enrolment figures for the next ‘n’ period. It does not require particular prior information other than repetition rate and transition rate. Enrolment is a function of population. So, the estimation of the age-appropriate population of school-entry age i.e. age 6 years (in India) and conversion of population age-groups data into school fitted age-groups data is required. In this paper, we discussed how Reconstructive Cohort Approach is applicable in certain situation and process of estimating school enrolment of India for the next decade along with other parameters.
A stochastic model for relative risk estimation of leptospirosis in Malaysia
Authors: Ideris Sufi Hafawati , Malim Muhammad Rozi , Shaadan Norshahida 
Published: November 2019

Abstract
The relative risk of a disease is the observed probability that a member of an exposed group will develop the disease relative to the expected probability that a member of a susceptible group will develop the same disease. The estimation of relative risk is important for disease mapping; it is a method used to illustrate the geographical distribution of a disease occurrence for identifying areas that need more attention. Better estimates of risk would subsequently produce more accurate maps of disease risk. The study on relative risk estimation of leptospirosis in Malaysia is very scarce. Most of the related studies involved only the demographic of the disease. Furthermore, most of the mathematical modelling and statistical analyses used for disease transmission models have been deterministic; do not consider the potential of random effects. Thus, the objective of this study is to propose a discrete-time discrete-space stochastic model for relative risk estimation of leptospirosis in Malaysia based on a SIR-SI transmission model. The proposed model was demonstrated using Malaysia leptospirosis dataset (2012-2016) to estimate and analyse the expected relative risks of leptospirosis for all states. The results showed that the averages of estimated relative risks are between 0.340 and 2.898. Kelantan and Terengganu are the two most vulnerable states of leptospirosis for every epidemiology year from 2012 to 2016.
Application of Rasch rating scale model in calibrating youth adversities in Malaysia
Authors: Effendi Mohd , Matore Ewan Mohd 
Published: November 2019

Abstract
The traditional statistical summation of Likert scale is based on the a priori assumption that all of the items are of equal difficulty and that the thresholds between steps are of equal distance or equal value. The Rasch Rating Scale Model (RSM) emphasize that not only each item have a difficulty estimate, but the rating scale has a series of thresholds to show the progression of the scale used compared to dichotomous data. In order to check the quality of the adapted items based on Malaysian Youth Challenges and also how functions the RSM in calibrating the adversities among youth. The research aimed to investigate the reliability and separation index of adapted items developed and calibrating the main adversities among youth. A set of items in online survey questionnaires with a four-point Likert scale were administered to 500 youth that categorized into early, mid and end youth from five different zones using convenience sampling technique. The data of the questionnaire were analysed using RSM. The items with -0.09 logit from the data set (second quartile or 50 percent), and above are considering as hard to endorse by youth. The findings revealed that all items have good reliability and separation index. The most dominant challenge for all age categories of youth is the economy and the least dominant is technology. The results exposed the potential of logit on the RSM is useful to help organize the adversities. The logit served to convert the ordinal to the interval type of data. The research was contributed to the applications of modern psychometric theory. This information is valuable to redesign the content of the self-development for youth self-development programs, especially to face adversities in their life.
The Log Beta Generalized Weibull Regression Model for Lifetime Data
Authors: Chanasriphum N , Seenoi P , Srisodaphol W 
Published: November 2019

Abstract
A log beta generalized Weibull regression model was proposed by modification from beta generalized Weibull distribution. To predict the survival time, the maximum likelihood method was used to estimate parameters in the proposed regression model and then, the proposed regression model was applied with lifetime data. The Akaike information criterion, Bayesian information criterion and likelihood ratio statistic are criterion to compare the performance of the proposed regression model with sub models which are log beta Weibull regression model, log generalized Weibull regression and log Weibull regression model. The result showed that the proposed regression model performed better than the sub models.
Comparison on the Performance of Several Outlier Detection Methods in Univariate Circular Wrapped Normal Sample
Authors: Zulkipli Nur Syahirah , Rambli Adzhar 
Published: November 2019

Abstract
This study focuses on detecting a single outlier in circular data generated from a wrapped normal (WN) distribution. The discordancy tests of M, A and G
1 statistics are used to detect single outlier in simulated data generated from wrapped normal distribution. The purpose of this study is to make a comparison on the performance of these statistics via Monte Carlo simulation by obtaining the proportion of correct outlier detection for each statistic. In this study, Splus-language and R-language programming are used to carry out the simulation study. The power performance of these statistics have been investigated and the result revealed that these statistics performed better as the increment in contamination value, λ and the value of concentration parameter, ρ gets larger and close to 1 in the case of small and large sample size, n. In general, the A statistic is found to be outperformed the M and G
1statistics in all cases. As an illustration, a practical example is included in this study by using the Kuantan wind direction dataset.
The productiveness of Bootstrap simulator in evaluating the accuracy parameters of measurement system for ball screw
Authors: Chuan Zun Liang , Mohd Noh Muhamad Husnain , Mohd Romlay Mohd Akramin , Chuan Mu Wen 
Published: November 2019

Abstract
Ball screw is an essential mechanical component of computer numerical controlled (CNC) milling machine, which the positioning accuracy of ball screw is highly associated with lead angle accuracy and axial clearance. In particular, the inaccuracy of lead angle and axial clearance of ball screw can be massively affected the inaccuracy of positioning, leading to the degraded quality of manufactured products. Therefore, a reliable and productive measurement system analysis is indeed in monitoring the accuracy parameters of the ball screw. The main objective of this study is to propose using the Bootstrap simulator in monitoring the accuracy parameters of measurement system for ball screw, with the abstraction of cost and time. The accuracy parameters of the measurement system are including stability, bias and linearity. Based on the simulation results, it can be concluded that the Bootstrap simulator is more productive in monitoring the accuracy parameters of measurement system for ball screw compared to the Monte Carlo simulator. This is due to the Bootstrap simulator can be yielded a lower uncertainty of simulation compared to the Monte Carlo simulator. Furthermore, the Bootstrap simulator is also more advantages compared to the Monte Carlo simulator as this simulator can be carried out with small sample size of measurement data.
Modelling and forecasting S&P 500 stock prices using hybrid Arima-Garch Model
Authors: Mustapa Farah Hayati , Ismail Mohd Tahir 
Published: November 2019

Abstract
The S&P 500 is a bellwether and leading indicator for the economy as well as the default vehicle for passive investors who want exposure to the U.S. economy via index funds. Since 1957, the S&P 500 has performed amazingly, outpacing other leading asset classes such as bonds or commodities. This study seeks to develop an appropriate ARIMA model that best fit the monthly stock price of S&P 500 for a period of 17 years, 2001–2017, thus make a short-term forecast in a way to give an overview and help the investor or portfolio manager in decision making. EViews software was used to run the analysis of the data. Our analysis involved 2-step procedure, which were identifying ARIMA model then fitting GARCH (1,1) into the model. As a result, ARIMA (2,1,2)-GARCH (1,1) model was found to be the best model for forecasting the S&P500 stock prices. The research findings indicate that a dynamic forecast gave a better result compared to a static forecast.
Lipid profile of type 2 diabetics in Almajmaah, Saudi Arabia
Authors: Sami W , Hamid M R Ab 
Published: November 2019

Abstract
This research evaluated the lipid profile of type 2 diabetics in Almajmaah, Saudi Arabia. There is scarcity of literature about the assessment of lipid profile of type 2 diabetics in Saudi Arabia with very few studies addressing this issue. The data was collected from 350 diabetic patients visiting the primary healthcare centres in Almajmaah, Saudi Arabia. The questionnaire comprised of socio-demographic and clinical data information. Systematic random sampling technique was used to collect the data from available sample frame of type 2 diabetics. The median age of the patients was 45 (40 – 51) years. Majority of the patients were males as compared to females (57.7% vs 42.3%). A significant dyslipidaemia was observed in diabetics. Patients had significantly high LDL, and TG level (p < 0.001) respectively. HDL-C of males was significantly high, whereas, for females, it was at borderline (p < 0.001) respectively. However, TC was not statistically significant (p = 0.405). The prime focus when treating diabetics is to introduce preventive and therapeutic lifestyle changes measures into a comprehensive treatment programme. The treating doctor must keep an eye on the lipid profile of diabetics that can be useful for the patients to reduce the morbidity from macrovascular complications.