Controlled Experiment for Assessing the Contribution of Ontology Based Software Redocumentation Approach to Support Program Understanding

Redocumentation is an approach that is used to recover knowledge from raw software artifacts by using alternative presentations. Several legacy systems have been developed based on event-driven programming which require redocumentation. However, these existing repository and query techniques emphasize only on lexical and syntactical based queries which come with limitations in providing the semantic relationship for program understanding. We are using ontology based approach that uses both ontology reasoning and querying techniques to generate software documentation from the knowledge repository. We present a controlled experiment for the empirical evaluation on the proposed ontology based approach and implemented in a tool called Ontology Based Software Redocumentation (OBSR). In this experiment, two existing tools namely Universal Report (UR) and Microsoft Visual Studio specifically for Visual Basic (VB) programming environment have been selected to be compared with the OBSR tool. The goal is to provide experimental evidence of the viability of our approach in the context of program understanding using HTML based semantic software documentation. The experiment shows that the software maintainers are able to understand and provide significant improvement in program understanding to accomplish the maintenance task easily. We describe in detail the experiment performed, discuss its results and reflect the lesson learned from the experiment

A review on shoreline detection framework using remote sensing satellite image

Shoreline is usually defined as the intersection of the land with the water surface of the mean high water line with the beach profile. In relation, most research in recent years has set the focus on remote sensing which makes it possible to collect data on this shoreline areas. Furthermore, shoreline detection is the ability to recognise and evaluate shoreline detection, so that facilitates decision makers to adapt, mitigate and manage the shoreline risks. Thus, this paper aims to investigate current works on shoreline detection framework using remote sensing satellite images. This investigation includes current research trends on the computational method in shoreline detection, image segmentation, and image filtering method

A review on shoreline detection framework using remote sensing satellite image

Shoreline is usually defined as the intersection of the land with the water surface of the mean high water line with the beach profile. In relation, most research in recent years has set the focus on remote sensing which makes it possible to collect data on this shoreline areas. Furthermore, shoreline detection is the ability to recognise and evaluate shoreline detection, so that facilitates decision makers to adapt, mitigate and manage the shoreline risks. Thus, this paper aims to investigate current works on shoreline detection framework using remote sensing satellite images. This investigation includes current research trends on the computational method in shoreline detection, image segmentation, and image filtering method

Shoreline detection, in Tanjung Piai, Malaysia by improving the low brightness and contrast of SPOT-5 images using the NIR-HE method

Satellite images taken in hazy conditions tend to have low brightness and contrast, and to address this problem, a simple and optimised method using near-infrared (NIR) histogram equalisation has been proposed, in order to improve the brightness and contrast of the SPOT-5 images taken by satellite. This helps to improve the detection of shoreline with a higher efficiency in edge detection, and produces results with a high success rate of more than 90%. In order to remove haze from SPOT-5 images, a false colour composite is used to prevent misclassified pixels of the shoreline, and ensure minimal error in image classification. The presence of an NIR channel is suitable for the detection of vegetation and waterbodies. The NIR channel is important for this study, in ensuring that contrast between vegetated areas and sea can be clarified, due to the high reflectance of vegetation leaves in the NIR wavelength region. This research used three areas of interest to show different results in shoreline detection. The results showed that the proposed method performed better than existing methods, when dealing with the low brightness or contrast ratio of SPOT-5 images

Breast Abnormality Boundary Extraction in Mammography Image Using Variational Level Set and Self-Organizing Map (SOM)

A mammography provides a grayscale image of the breast. The main challenge of analyzing mammography images is to extract the region boundary of the breast abnormality for further analysis. In computer vision, this method is also known as image segmentation. The variational level set mathematical model has been proven to be effective for image segmentation. Several selective types of variational level set models have recently been formulated to accurately segment a specific object on images. However, these models are incapable of handling complex intensity inhomogeneity images, and the segmentation process tends to be slow. Therefore, this study formulated a new selective type of the variational level set model to segment mammography images that incorporate a machine learning algorithm known as Self-Organizing Map (SOM). In addition to that, the Gaussian function was applied in the model as a regularizer to speed up the processing time. Then, the accuracy of the segmentation’s output was evaluated using the Jaccard, Dice, Accuracy and Error metrics, while the efficiency was assessed by recording the computational time. Experimental results indicated that the new proposed model is able to segment mammography images with the highest segmentation accuracy and fastest computational speed compared to other iterative models