Spatio-Temporal Semantic Representation of Cardiac MRI in Heart Attack Patients

Semantic Web technologies, applications and tools have made great steps forward in the life science and health care data exchange. However, developing appropriate semantic representations, including designing spatio-temporal ontologies, remains difficult and challenging. In this paper, we describe a framework to engineer a spatio-temporal semantic representation for the Cardiac MRI images using the current existing case studies conducted in Sarawak General Hospital Heart Centre

Automatic Segmentation Measuring Function for Cardiac MR-Left Ventricle (LV) Images

Automatic segmentation approaches are a desirable solution for Endocardium (inner) and Epicardium (outer) contours delineation using cardiac magnetic resonance left ventricle (CMR-LV) short axis images. The Level Set Model (LSM) and Variational LSM (VLSM) is the state-of-the-art in detecting the inner and outer contour for medical images. However, in CMR-LV images segmentation the LSM and VLSM are facing with the issue of re-initialisation because of irregular circle shape. In this paper, we developed an automatic segmentation measuring function based on statistical formulation to solve the re-initialisation issues in huge set of data images. The sign Euclidean distance function successfully classified the negative (inner contour) and positive (outer contour) features. The Fuzzy C mean interaction operator intersects the high membership degree that initialises the centre point. The experiments were conducted using the Sunnybrook and Pusat Juntung Hospital Umum Sarawak (PJHUS) cardiac datasets. This paper aims at developing a distance function to guide the automatic segmentation for LV contours and also to reduce segmentation error

Using Latent Semantic Analysis for Automated Grading Programming Assignments

Traditionally, computer programming assignments are graded manually by educators. As this task is tedious, timeconsuming and prone to bias, the need for automated grading tool is necessary to reduce the educators' burden and avoid inconsistency and favoritism. Recent researches have claimed that Latent Semantic Analysis (LSA) has the ability to represent human cognitive knowledge to assess essays, retrieving information, classification of documents and indexing. In this paper, we adapt LSA technique to grade computer programming assignments and observe how far it can be applied as an alternative approach to traditional grading methods by human. The grades of the assignments are generated from the cosine similarity that shows how close students' assignments to the model answers in the latent semantic vector space. The results show that LSA is not able to detect orders of computer programming and symbols; however, LSA is able to grade assignments faster and consistently, which avoid bias and reduces the time spent by human

Service Learning Support for Academic Learning and Skills Development

The change in higher education policies internationally and nationally is requiring institutes of higher learning to adopt experiential learning practices worldwide. The purpose of this study is to investigate the student’s perspectives on service learning support for academic learning and skills development. As a case study, an online questionnaire survey was conducted for the third year undergraduate students in the Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS) who enrolled in service learning program and participated in community service. The collected data was analysed using the second generation structural equation modelling (PLS-SEM). The findings of this study show that the service learning support positively influences academic learning and skills development. Moreover, the findings specify that critical service learning program provides an opportunity for students to enhance their academic learning as well as assists them to develop various skills. These findings offer new insights towards better understanding of service learning benefits for undergraduate students

The Effectiveness of Ellipsoidal Shape Representation Technique for 3D Object Recognition System

Shape representation methods play an important role in 3D shape recognition system. Three-dimensional shape recognition is widely used in 3D search engines, gravitational field, medical imaging, computer vision and face recognition. In this paper we propose an ellipsoidal shape representation technique for 3D shape recognition. We present some experimental and comparison results of our approach for shape matching using a standard database, Princeton Shape Benchmark. The effectiveness of our proposed algorithm is measured using nearest neighborhood. We then introduced a new idea which is a possible extension of the proposed approach and evaluate the results against human observation

Automated Machine for Sorting Sarawak Pepper Berries

White pepper berries is one of the Malaysia’s key export as it is categorised as high valued commodity product. At present, processed white pepper berries are graded semiautomatically. This process is time consuming as it dependent on the experience of the pepper grader. In this paper we present a solution for Sarawak White Pepper grading using a combination of image processing technique and robotic solutions to sort pepper berries into their respective grades. In particular, we present the result of using different colour sensors. With the automated sorting machine, more high grades pepper berries are able to be sorted; this means more income to the smallholders, which are the local pepper farmers