Enhanced image annotations based on spatial information extraction and ontologies

Current research on image annotation often represents images in terms of labelled regions or objects, but pays little attention to the spatial positions or relationships between those regions or objects. To be effective, general purpose image retrieval systems require images with comprehensive annotations describing fully the content of the image. Much research is being done on automatic image annotation schemes but few authors address the issue of spatial annotations directly. This paper begins with a brief analysis of real picture queries to librarians showing how spatial terms are used to formulate queries. The paper is then concerned with the development of an enhanced automatic image annotation system, which extracts spatial information about objects in the image. The approach uses region boundaries and region labels to generate annotations describing absolute object positions and also relative positions between pairs of objects. A domain ontology and spatial information ontology are also used to extract more complex information about the relative closeness of objects to the viewer

Multiclass classification for chest x-ray images based on lesion location in lung zones

Innovation in radiology technology has generated numerous kinds of medical images like the chest X-ray (CXR).This image is used to find common problem in lung like the lesion through scanning process in lung area which is divided into six zones.By classifying the CXR images with common feature like the lesion location, we can ensure efficient image retrieval.Recently, Support Vector Machine (SVM) has turn out to be a well-known method for image classification.While many previous studies have reported the achievement of SVM in classifying images, yet there is still problem with this technique for multiclass classification.Since SVM is a binary classification technique, its ability is limited to classifying features between two classes at one time. Therefore, it is difficult to classify CXR images which contain many image features.Realizing the problem, we proposed an application method for multiclass classification with SVM to the CXR images based on the lesion position in the lung zones.The multiclass classification application is executed on the CXR images taken from Japan Society of Radiology Technology dataset.Lesion coordinates were selected as the classification input while the lung zones becomes the labels. The multiclass classification is performed with RBF kernel and the classification accuracy is tested to attain the classifiers performance.Overall, it can be concluded that the percentage of the classification accuracy is high with the highest accuracy percentage recorded at 98.7% while the lowest was 94.8%.Meanwhile, the average classification accuracy was recorded at 96.9%. The result obtained revealed that the SVM classifiers generated have successfully classified the lesion location correctly according to the lung zones

Remaja bercinta: kajian terhadap tiga jenis sekolah menengah kebangsaan, agama dan agama swasta

Remaja bercinta adalah fenomena percintaan antara dua pasangan berlainan jantina di alam persekolahan. Fenomena remaja bercinta yang tidak terkawal boleh membawa kepada permasalahan gejala sosial remaja seperti lari rumah, buang anak, keruntuhan akhlak, dan lemah dalam pelajaran. Kebanyakan kehidupan zaman remaja dihabiskan di alam persekolahan. Di samping peranan ibu bapa, sekolah adalah tempat yang penting untuk memberi pendidikan berkesan terhadap remaja. Selain daripada memberi penekanan terhadap akademik, sekolah juga bertanggungjawab memberi pendidikan tentang isu percintaan dalam kalangan remaja, agar mereka memahami hakikat bercinta. Oleh itu kajian dilakukan terhadap tiga jenis sekolah menengah; sekolah menengah kebangsaan, sekolah menengah agama dan sekolah menengah agama persendirian bertujuan untuk mengenalpasti sejauhmana remaja bercinta, faktor remaja bercinta dan hubungannya terhadap pencapaian akademik. Sebanyak 431 soal selidik telah diterima dari 6 buah sekolah, 2 sekolah mewakili setiap jenis sekolah. Tiga jenis sekolah ini mempunyai persamaan dari aspek kandungan pembelajaran akademik, persekitaran fizikal seperti kemudahan, sekolah jenis campuran jantina dan guru yang bertauliah. Manakala ia mempunyai perbezaan dari perlaksanaan pendidikan, aktiviti kerohanian dan suasana sekolah yang diwujudkan oleh pihak pengurusan. Hasil dapatan mendapati sekolah menengah agama swasta mempunyai paling kurang peratusan remaja bercinta (<10%) berbanding sekolah menengah agama (50%) dan sekolah menengah kebangsaan (80%). Hasil kajian juga mendapati bahawa remaja bercinta tidak memberi pengaruh terhadap akademik mereka. Kelima-lima faktor seperti diri, rakan, keluarga, media dan internet dikenalpasti mempengaruhi remaja bercinta, bagaimanapun kajian mendapati terdapat perbezaan yang ketara pada jenis sekolah. Hasil kajian dapat disimpulkan bahawa perlaksanaan pendidikan, jenis aktiviti kerohanian dan suasana sekolah sangat mempengaruhi sikap remaja dan secara tidak langsung memberi kesedaran kepada remaja akan hakikat bercinta yang sebenar dan seterusnya menghindarkan mereka terjebak cinta dalam zaman remaja

Identification of persuasive elements in Islamic knowledge website using Kansei engineering

The global explosions of the internet and direct use by individuals, organizations and Islamic scholars to spread the knowledge and events have witnessed massive growth over the years. The users refuse to use online Islamic websites as references which causes the acceptance rate of the online learning method still low. This paper presents the identifications of persuasive elements in Islamic knowledge website. The objective of this study is to identify the persuasive element using Kansei Engineering (KE) method for online Islamic knowledge website design. Therefore, Kansei words related to online Islamic knowledge were identified. After that, the Kansei words (KWs) were evaluated in order to be used as persuasive elements. Ten specimens of Islamic websites were chosen and evaluated with 30 emotions of KWs. 38 students from one of the public universities accomplished the evaluation experiment. The gathered KWs were then analyzed using multivariate analysis such as Factor Analysis to identify the persuasive elements. Based on Factor Analysis, the finding of this research revealed four main pillars which are credibility-motivated, reliability, Islamic identity and functional. The two additional pillars are updated and belief. The pillars were then compared with the persuasive elements in the previous research. Although there are some limitations and constraint during the conduct of the research, this study also contributes to the design persuasive elements for Islamic website knowledge in the future

Multiclass Classification Application using SVM Kernel to Classify Chest X-ray Images Based on Nodule Location in Lung Zones

Support Vector Machine (SVM) has long been known as an excellent approach for image classification. While many studies have reported on its achievement, yet it still weak to handle multiclass classification problem because it is originally designed as a binary classification technique. It is challenging task to transform SVM to solve multiclass problems like classifying chest X-ray images based on the lung zone location. Classified X-ray images improved image retrieval hence reducing time taken to assessed back the images. Realizing this difficulties, therefore, we proposed an application method for multiclass classification using SVM kernel to classify chest X-ray images based on nodule location in lung zones. The multiclass classification experiment is performed using four popular SVM kernels namely linear, polynomial, radial based function (RBF) and sigmoid. Overall, we obtained high classification accuracy (&gt;90%) for three classifiers that are RBF, polynomial and linear kernel while sigmoid kernel classifier is only moderately good at 82.7% accuracy. Besides, values in the confusion matrices revealed that the RBF and polynomial classifiers managed to classify test data into all classification classes. Conversely, classifiers based on linear and sigmoid kernel have missed at least one classification class. Since each classifier work differently based on their kernel types, we noticed that it is better to view them as a complimentary rather than treating them as competing options. This condition also revealed that we can modify the original SVM classification method to handle multiclass classification problem

Awareness of digital footprint management in the new media amongst youth

The industrial revolution creates a new era where everything can be accessed via the Internet. However, despite all the advantages, the new era of technology comes with disadvantages as well. Studies have shown that the youth category is the group that is a synonym to technology. Most of the youth nowadays enjoy using the features but lacked the information on the footprint of their Internet browsing history. When individuals engaged with the Internet, they generated a complex trail of these digital footprints that may include their various presentation of self, based on social profiles and comments, traces of their activities, interest, interaction and anything else they choose to share online. However, the extent to which netizens are aware that they are creating these tracks and traces and have some ability to manipulate and control them is unclear at present. Thus, the objective of this paper is to identify the level of awareness among youth about the digital footprint. The research started with a structured survey distributed to respondents as a data collection requirement for analysis. A quantitative research method is employed in this research. Survey was conducted to the three main areas namely urban, suburban and rural as a sample of the population. Results were presented in the form of description analysis with respondents demographic and perception of respondents based on digital footprint awareness

Designing Domain Model For Adaptive Web-based Educational System According to Herrmann Whole Brain Model

Educational materials represent a domain model of Adaptive Web-based Educational System (AWBES). However, these materials should be designed to cover the differences of learners’ preferences. Herrmann Whole Brain Model (HWBM) is a reliable Learning Style (LS) model which can be used to extract the learner’s preferences in educational environment according to brain structure of learner. In this paper, the learning materials of an essential programming language course (C++) are organized to cover all learners’ differences according to their brain dominance. The learning materials were described and classified by instructional metadata to fit the preferences of four brain quadrants (rational, organizational, interpersonal and intuitive) within diverse learning objects. The main advantage of this approach is that it is not related to particular type of learners, but it covers the different learners according to their brain-structure. The system which could apply this model can be used to detect the learner preferences dynamically and thus personalize the learning materials within Web-based Educational System (WBES)

User experience evaluation towards interface design of digital footprint awareness application

User experience (UX) towards designs of an application or product affects the way such user receives the product. UX also becomes increasingly crucial as users obtain a rating of downloaded app experience that may affect sales and improve the application performance expectations. This study aims to evaluate user experience in order to validate user needs on digital footprint awareness applications that have been developed through the triangulation technique that involves three user experience testing methods which are guessability, think aloud and observation to acquire feedback and suggestions from experts and end users for the application development. The findings indicate that an important aspect of user satisfaction within this app is the use of attractive colours, clear and appropriate texts and clear and systematic guidelines to reduce the user's cognitive strains. In addition, the emphasis is on enhancing the fun, entertaining and interface design based on using different perspectives of its use in fun, motivational challenges and skills to enhance the application usability and learning ability

Classification and Image Annotation for Bridging the Semantic Gap

The use of digital images is rapidly increasing in digital archives, community databases, as well as on the Web. This creates new challenges for image management and retrieval and promotes the importance of automatic image classification and annotation research. In general current content-based image retrieval methods are still struggling to deal with the semantic gap between low-level visual features and the high-level abstractions perceived by humans. Manual annotation is typically a difficult and tedious task involving a process of describing the content and context of an image to provide direct access to the semantics. Automatic classification can allocate images or image regions to specific object classes and automatic annotation also aims to add descriptive labels to images. This paper will explore classification and image annotation in bridging the semantic gap and present some related projects which illustrate the advantages of these techniques for image retrieval in the medical and cultural heritage domains

Ontological Description of Image Content Using Regions Relationships

Much initial research on image annotation pays little attention to the spatial relationships between regions. Annotations are most frequently assigned globally and even when assigned locally the extraction of such relations is often neglected. For rich semantic description of visual information, it is essential to capture such relations. The aim of this research is to attempt and enhance annotation systems, either through automatic or semi-automatic means, by capturing the spatial relationships between regions or objects in images and incorporating such knowledge in a knowledge base such as ontology. As part of a preliminary experiment, a comparative analysis of three existing tools has been performed and the result obtained. This research will contribute a new approach to automate image linking to an ontology based on regions with automatic spatial relationships extraction between the composite regions or objects in images, which reliable with images in the real world