Educators have always highlighted the importance of mathematics mastery in education for many years. With the current emphasis of Science, Technology, Engineering and Mathematics (STEMs) education, mathematics mastery is even more vital because it supports the learning and mastery of science fields such as engineering and science. Furthermore, in higher education, mathematics is essential because majority of the courses require the use of mathematical concepts in their learning. In recent years, many countries including Malaysia have seen an increase in the enrolment number of tertiary students. The increase in student enrolment has resulted in a population of students with diverse abilities, and this creates challenges for educators in providing instruction to the students. Educators need to detect students' mathematical ability at an early stage before teaching them new content. One way to gather information about students' basic mathematics skill is through the use of diagnostic test. Diagnostic test in education is a preliminary assessment mainly used to detect students' strengths and weaknesses in learning. It allows educators to cater their teaching style and content to suit to the students' basic knowledge. With researches indicating the importance and benefits of using diagnostic test in various subjects, it is important to further examine the use of diagnostic test in the local context of pre-university studies. This study investigated the relationship between students' mathematics diagnostic test results and final mathematics examination performances at a public pre-university programme. The samples of the study consisted of 250 pre-university students and the data of the study were collected through a mathematics diagnostic test paper, a questionnaire and a final mathematics examination. The outcomes of the study show that there was a strong positive correlation between mathematics diagnostic test results and students' mathematics achievement in pre-university.

We propose an improved solution to the three-stage DNA motif prediction approach. The threestage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis.

For many students, mathematics is regarded as a challenging subject to learn and master in class. One of the significant factors contributing to the students’ difficulties in learning mathematics is caused by a phenomenon called mathematics anxiety. Mathematics anxiety is a feeling of unease and anxiety toward mathematics and it can be different from person-to-person. Understanding the effects of mathematics anxiety levels on students’ mathematics performances in class can be the key to help students’ mastery of mathematics. The aim of the study is to investigate the relationship between mathematics anxiety levels and students’ mathematics performances at the foundation level. A sample of 545 students from a local foundation centre was chosen for this study. Data collection via questionnaire was used where quantitative data were analysed using correlation, t-test, and descriptive analyses. The results showed that there was a weak positive correlation between students’ anxiety levels and the students’ mathematics performance in their final examination. Recommendations and future potential for this study were further discussed in this paper.

The academic performance of students is affected by many factors, including effectiveness in teaching, the subjects taught and the environment as well as the facilities provided. The purpose of this study is to determine the relationship between students' perceptions of the teaching and learning towards the lecturers with their achievements in Mathematics at the Centre for Pre University Studies. The study was a descriptive study in which a survey research design was adopted. A total of 841 students from the centre participated in the study. The data were collected through student's questionnaire. The questionnaires consisted of 26 questions. 5-Likert Scale questionnaires used in this study focused on the five categories of students' perceptions; teaching, evaluations, subjects, guidance and environment dimensions. The findings revealed that there is no significant correlation between the average scores of students' perceptions of teaching and learning towards the Mathematics lecturer with the average scores Mathematics achievement of the students. The study also revealed that there are no significant differences between the average scores of male and female students' perceptions of the effectiveness of teaching and learning of the Mathematics lecturer. The findings of this study show that the lecturer can improve their teaching skills and techniques that are appropriate to the students.

This work proposed a new hybridised network of 3-Satisfiability structures that widens the search space and improves the effectiveness of the Hopfield network by utilising fuzzy logic and a metaheuristic algorithm. The proposed method effectively overcomes the downside of the current 3-Satisfiability structure, which uses Boolean logic by creating diversity in the search space. First, we included fuzzy logic into the system to make the bipolar structure change to continuous while keeping its logic structure. Then, a Genetic Algorithm is employed to optimise the solution. Finally, we return the answer to its initial bipolar form by casting it into the framework of the hybrid function between the two procedures. The suggested network’s performance was trained and validated using Matlab 2020b. The hybrid techniques significantly obtain better results in terms of error analysis, efficiency evaluation, energy analysis, similarity index, and computational time. The outcomes validate the significance of the results, and this comes from the fact that the proposed model has a positive impact. The information and concepts will be used to develop an efficient method of information gathering for the subsequent investigation. This new development of the Hopfield network with the 3-Satisfiability logic presents a viable strategy for logic mining applications in future.