Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.

Epidemiological models play a vital role in understanding the spread and severity of a pandemic of infectious disease, such as the COVID-19 global pandemic. The mathematical modeling of infectious diseases in the form of compartmental models are often employed in studying the probable outbreak growth. Such models heavily rely on a good estimation of the epidemiological parameters for simulating the outbreak trajectory. In this paper, the parameter estimation is formulated as an optimization problem and a metaheuristic algorithm is applied, namely Harmony Search (HS), in order to obtain the optimized epidemiological parameters. The application of HS in epidemiological modeling is demonstrated by implementing ten variants of HS algorithm on five COVID-19 data sets that were calibrated with the prototypical Susceptible-Infectious-Removed (SIR) compartmental model. Computational experiments indicated the ability of HS to be successfully applied to epidemiological modeling and as an efficacious estimator for the model parameters. In essence, HS is proposed as a potential alternative estimation tool for parameters of interest in compartmental epidemiological models.

This study identifies the success indicators of mathematical problem-solving performances among Malaysian matriculation students divided into four indicators: mathematical beliefs, mathematics attitudes, mathematics self-efficacy and metacognitive skills. For this purpose, 368 matriculation students from three matriculation colleges were selected as respondents using proportioned stratified sampling. This study utilized a descriptive correlational design approach. A set of questionnaires and a mathematics test were used as the instruments. Independent variables were measured using a questionnaire, while mathematical problem-solving performance was measured using a mathematics test. The findings show students had a high level in mathematics beliefs, attitude towards mathematics, mathematics self-efficacy and metacognitive skills. Statistical tests to determine success indicators predicting mathematical problem-solving performance revealed that mathematics self-efficacy does not contribute significantly to these variables and that metacognitive skills make the most decisive contribution, followed by mathematics attitude and mathematics beliefs. Hence, this study suggests that problem-solving should be included as an essential part of the mathematics matriculation syllabus to enable students to improve their problem-solving abilities.

Recent advances in phytochemical analysis have allowed the accumulation of data for crop researchers due to its capacity to footprint and distinguish metabolites that are present within an organisms, tissues or cells. Apart from genotypic traits, slight changes either by biotic or abiotic stimuli will have significant impact on the metabolite abundances and will eventually be observed through physicochemical characteristics. Apposite data mining to interpret the mounds of phytochemical information from such a dynamic system is thus incumbent. In this investigation, several statistical software platforms ranging from exploratory and confirmatory technique of multivariate data analysis from four different statistical tools of COVAIN, SIMCA-P+, MetaboAnalyst and RIKEN Excel Macro were appraised using an oil palm phytochemical data set. As different software tool encompasses its own advantages and limitations, the insights gained from this assessment were documented to enlighten several aspects of functions and suitability for the adaptation of the tools into the oil palm phytochemistry pipeline. This comparative analysis will certainly provide scientists with salient notes on data assessment and data mining that will later allow the depiction of the overall oil palm status in-situ and ex-situ.

Students’ self-efficacy is the belief about their capabilities to learn and said to have control over thoughts, feelings and actions. Self-efficacy is one of the important factors influencing statistics engagement. This paper aims to show the influence of statistics self-efficacy towards statistics engagement among undergraduate students taking Statistics in Applied Sciences course in Universiti Putra Malaysia. A total of 293 students were randomly selected from eight different faculties taking this statistics course as a compulsory subject. A pilot study was conducted on 25 students prior to the actual study and had reliability indices of 0.84 for statistics self-efficacy and 0.93 for statistics engagement. Descriptive analysis showed that students have low statistics self-efficacy with the median score of 2.00. On the other hand, the median score for statistics engagement is 4.00 indicating that students’ statistics engagement was moderate. Spearman’s rank Correlation Analysis showed that there is a positive relationship between statistics self-efficacy and overall statistics engagement (r_s = 0.36* p > 0.05). Further analysis shows that statistics self-efficacy influence statistics engagement among students with a contribution of 25%. These findings indicate the need for students to have statistics self-efficacy because this would influence students’ statistics engagement and in turn enables them to achieve good results in statistics.