<span>Individuals learn in different ways using several learning styles, but lecturers may not always share material and learning experiences that match students’ learning preferences. Mismatches between learning and teaching styles can lead to disappointment with students are taking, and lead to underperformance among them. The aim of this study is to identify the learning styles of the students enrolled in Universiti Malaysia Pahang who were registered in Programming Technique course and to investigate the relationship between students’ learning styles and teachers’ teaching styles. Five lecturers and 251 students were involved in the study as participants and. Data from students were collected using Leonard, Enid’s VAK Learning Style Survey. Meanwhile, the teaching styles of the lecturers were identified using Grasha and Reichmann’s Teaching Style Survey. The findings revealed that majority of the student’s preferred visual learning style. The result also shows that the lecturers’ teaching styles give an impact towards the <br /> students’ academic performance. From this study, we can conclude that teaching styles have significant impacts on students’ learning styles and academic performances.</span>

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Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.

Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest. implementation of software that facilitates and eases the understanding of biological processes. InSrinivasan et al. [3],the most general definition of bioinformatics in addressing biological problems is discussed.Most biomedical researchers are looking for appropriate software which is not only can achieve high prediction accuracy but also includes a user friendly design in order to ease the implementation. Moreover, such softwareisvery useful if the source code is available.In addition, the software should be up-to-date with the related information to make sure that it is competitive with other software.In this paper, the classification software applications for six supervised classification methodsare reviewed. The six supervised classification methods include the Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Neural Network (NN), Bayesian Classifier, Linear Discriminant Analysis (LDA), and Random Forest (RF). Furthermore, the sources of the software and web-based applications are listed as well. Software for Support Vector Machine (SVM) LIBSVMLIBSVM, a library for SVMs, was developed by Chang and Lin [4]. The main purpose of developing this software was to help users implementing SVM. This package supports three main learning tasks: classification, regression, and estimation of probability. For classification, it supports binary and multi-class classification. It also includes various formulations of SVM such as c-classification, v-classification, ∈-regression, and vregression. Other features include support for cross-validation for performance measurement, model selection, and solving of unbalanced data using weighted SVM. It is mainly implemented in C++ and Java but there are many extensions such as R, MATLAB, Python, and Perl that have been developed by Chang and Lin and others. Moreover, it also provides different kernel settings such as linear, polynomial, and radial basis functions. This package is mainly for Windows and Linux. SVMlightSVMlight was developed...