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When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

Capturing eye images within visible wavelength illumination in the non-cooperative environment lead to the low quality of eye images. Thus, this study is motivated to investigate the effectiveness of image enhancement technique that able to solve the abovementioned issue. A comparative study has been conducted in which three image enhancement techniques namely Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were evaluated and analysed. UBIRIS.v2 eye image database was used as a dataset to evaluate those techniques. Moreover, each of enhancement techniques was tested against the different distance of eye image captured. Results were compared in terms of image interpretation by using Peak-Signal Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean Absolute Error (MAE). The effectiveness of the enhancement techniques on the different distance of image captured was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR). As a result, CLAHE has proven to be the most reliable technique in enhancing the eye image which improved the localization accuracy by 7%. In addition, the results showed that by implementing CLAHE technique at a four-meter distance was an ideal distance to capture eye images in a non-cooperative environment where it provides high recognition accuracy, 74%.

Information Management and PSM Evaluation System is a system developed to replace the existing system at the Faculty of Computing. The existing system at the Faculty of Computing is a manual system in which all the evaluation process still utilises paper forms. PSM is divided into two phases; PSM1 and PSM2 and each phase has a different form for evaluation. This process is seen to be less systematic and imposes much time on the evaluator, coordinator and supervisor who are also lecturers. Information Management and PSM Evaluation System is designed to automate information management and evaluation of PSM to keep the information in the database. The scope of these systems focuses on admin, supervisor, evaluator and coordinator bound to PSM1 and PSM2. Some of the functions that can be operated on the system are evaluation, updating PSM students' information and generating reports. The chosen methodology is an Evolutionary Prototype which needs are taken care of the system during modifications. Requirements established during the interview is employed to form a common structure with the essential basic functions of the system. Therefore, Information Management and PSM Evaluation System was developed to automate the manual system to increase efficiency. The system was developed using ASP.net technology and Microsoft Visual Studio 2010 and has been successfully completed within the specified time.

Existence of bioinformatics is to increase the further understanding of biological process. Proteins structure is one of the major challenges in structural bioinformatics. With former knowledge of the structure, the quality of secondary structure, prediction of tertiary structure, and prediction function of amino acid from its sequence increase significantly. Recently, the gap between sequence known and structure known proteins had increase dramatically. So it is compulsory to understand on proteins structure to overcome this problem so further functional analysis could be easier. The research applying RPCA algorithm to extract the essential features from the original high-dimensional input vectors. Then the process followed by experimenting SVM with RBF kernel. The proposed method obtains accuracy by 84.41% for training dataset and 89.09% for testing dataset. The result then compared with the same method but PCA was applied as the feature extraction. The prediction assessment is conducted by analyzing the accuracy and number of principal component selected. It shows that combination of RPCA and SVM produce a high quality classification of protein structure