Monthly Mathematical Colloquium MMC 3, 2023

On 19 May 2023, Centre for Mathematical Sciences has been successfully organised the third Monthly Mathematical Colloquium in 2023

Monthly Mathematical Colloquium MMC 2, 2023

On 17 March 2023, Pusat Sains Matematik (PSM) has been successfully organised the second Monthly Mathematical Colloquium for 2013

Unlocking Statistical Insights: Global Classroom Collaboration with Universitas Islam Negeri Sultan Syarif Kasim Riau Indonesia Leaves a Lasting Impact

On May 8, 2024, a global classroom session was conducted via the Google Meet platform, organized collaboratively by the Department of Mathematics, Universitas Islam Negeri Sultan Syarif Kasim Riau Indonesia, and Pusat Sains Matematik (PSM), Universiti Malaysia Pahang Al-Sultan Abdullah

Determination of the best single imputation algorithm for missing rainfall data treatment

The presence of missing rainfall data is inevitable due to error of recording, meteorological extremes and malfunction of instruments. Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. There are several such efficient algorithms which have been introduced in earlier studies. However, the limitations of current algorithms are they are highly dependent on the information and homogeneity of adjoining rainfall stations. Therefore, this study is intended to introduce several single imputation algorithms for missing data treatment, which believed to be more competent in treating missing daily rainfall data without the need to depend on the information of adjoining rainfall stations. The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. These algorithms are tested on hourly rainfall data records from six selected rainfall stations located in the Kuantan River Basin. Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations

A predictive model of the enrolment in the key subject of STEM education using the machine learning paradigm

The presence of a global health crisis on coronavirus pandemic (COVID-19) has been accelerated the global uptakes the transformation towards the digital economy. Consequently, the rapid digital transformation has risen the demands of technologically competent workforces in which open the big doors for the education and careers of Sciences, Technology, Engineering and Mathematics (STEM). Due to Additional Mathematics is the principal subject for the STEM related subjects in producing qualified and skilful human capital demanded in 21st digital economy era. Therefore, this article presented a predictive model of the enrolment in Additional Mathematics using a supervised machine learning model, namely binary logistic regression model. The findings of this article can be beneficial the decision makers by taking appropriate initiatives in increasing the number upper secondary students enrol in STEM education, particularly school teachers and students’ parents

FACTORS INFLUENCING MALAYSIAN PINEAPPLE SMALLHOLDERS INTENTION TO ADOPT MYGAP AND MPIB ROLES TO INSPIRE THE GROWERS TO OBTAIN MYGAP CERTIFICATION

Purpose of the study: The study aims to identify the factors influencing growers’ intention to adopt MyGAP and MPIB roles and to inspire the pineapple growers to obtain MyGAP certificate. Methodology: Questionnaires were distributed to a sample of 52 pineapple smallholder respondents in the study area. Descriptive analysis and binary logistic regressions were conducted using IMB SPSS version 23. Main Findings and Novelty: The results of this study show that the three factors influencing pineapple smallholders’ intention to adopt MyGAP are training, attitude and barriers. The odd ratios show growers who received training are four times more likely to adopt MyGAP. Applications of this study: MPIB has to conduct more training in order to inspire pineapple growers to obtain MyGAP certification. Meanwhile, the growers also have to change their attitude to accept MyGAP and overcome the perceived barriers for adopting MyGAP

Robust hotelling's T2 statistic based on M-estimator

Hotelling’s T2 statistic is the multivariate generalization of the student’s t statistic. Hotelling’s T2 statistic is a method for testing hypotheses about multidimensional means. However, the classical Hotelling’s T2 statistic is very sensitive to the presence of outliers. In order to overcome this limitation, a modification is needed so that Hotelling’s T2 is robust. In this paper, classical Hotelling’s T2 statistic has been modified by substituting mean vector and covariance matrix with a robust estimator. M-estimator has been used for this modification. The performance of modified Hotelling’s T2 statistic has been compared with the classical Hotelling’s T 2 statistic and discussed in this paper to illustrate the advantage of modified Hotelling’s T2 statistic towards outliers. The performance of modified Hotelling’s T 2 statistic is better than classical Hotelling’s T2 when number of sample, n and dimension, p is small

A comparison study between Doane’s and Freedman-Diaconis’ binning rule in characterizing potential water resources availability

One of the primary constraints for development and management of water resources is the spatial and temporal uncertainty of rainfall. This is due to the stability and reliability of water supply is dynamically associated with the spatial and temporal uncertainty of rainfall. However, this spatial and temporal uncertainty can be assessed using the intensity entropy (IE) and apportionment entropy (AE). The main objective of this study is to investigate the implications of the use of Doane's and Freedman-Diaconis' binning rule in characterizing potential water resource availability (PWRA), which the PWRA is assessed via the standardized intensity entropy (IE') against the standardized apportionment entropy (AE') scatter diagram. To pursue the objective of this study, the daily rainfall data recorded ranging from January 2008 to December 2016 at four rainfall monitoring stations located Coastal region of Kuantan District Pahang are analyzed. The analysis results illustrated that the use of Doane's binning rule is more appropriate than Freedman-Diaconis' binning rule. This is due to the resulted PWRA characteristics using Doane's binning rule is relatively consistent with practical climate such that the study region is experiencing poor-in-water zone with less amount and high uncertainty of rainfall during the Southwest Monsoon, while abundant and perennial rainfall during the Northeast Monsoon. Furthermore, the use of Doane's binning rule is more advantages compared to the Freedman-Diaconis' binning rule with the abstraction of computational cost and time

Modelling the impacts of climate change and air pollutants on the agricultural production yields in Malaysia using Random-Effects Error Components Regression model

The occurrence of climate change is attributable to anthropogenic emissions of greenhouse gases (GHG) which have affected the C3 plants’ agricultural production yields in past decades. Therefore, this article aims to model the linear association among these C3 plants’ agricultural production yields with several climatic and non-climatic explanatory variables using one-way random-effects error components regression model. To be congruent with the main objective of this study, the balanced longitudinal dataset period 1980 to 2018 under big data was acquired. The analysis results revealed that merely maximum temperature