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Master of Philosophy (M.Phil.) Mon, 05 Jun 2023 21:45:44 GMT 2023-06-05T21:45:44Z Selection of JPEG steganography algorithms using a feature based model Selection of JPEG steganography algorithms using a feature based model Senthooran, V JPEG image steganographic techniques use the DCT coefficients scaled by quantization table to make secure data hiding without degrading the image quality. The selection process of data embedding locations in lower frequency DCT coefficients should be carefully considered in each image blocks as these lower frequency coefficients are high sensitive to human eyes. Some of the existing related JPEG steganographic methods have been proposed with primary quantization table modification to hide message bits in the quantized DCT coefficients with minimal distortion by analyzing the properties of quantization table entry and relevant DCT coefficients. The performance of the JPEG steganographic methods is evaluated by the imperceptibility and embedding capacity. In the literature of quantization table modification based JPEG steganography, the middle frequency coefficients in each image block are utilized to embed maximum message size by modifying the middle part of the relevant quantization table values with minimizing the effect of visual perception. However, the data hiding techniques in lower frequency coefficients from the existing studies endure from imperceptibility while increasing the message size. This study suggests the lower frequency data hiding algorithms with utilizing middle frequency data hiding in terms of the modification of lower and middle part of the quantization table values by evaluating image quality parameters and it doesn’t affect the perceptual detectability and improves embedding capacity. The proposed JPEG steganography investigates the modification of quantization table values with regarding to selected lower frequency DCT coefficients for data hiding and selects different data hiding patterns in lower frequency area in terms of modification of quantization table. Finally, it returns the pair of relevant modified quantization table and generated data hiding pattern for an image based on the empirical results of the PSNR values. The pair that contains modified quantization table and data hiding pattern shared by the sender is used as a secrete key to extract the message at the receiver side. From the preliminary studies, the selection of appropriate lower frequency coefficients in image block to hide the optimum size of secrete message with perceptual un-detectability is dependent on the combination of image features, message size and the hiding algorithm. Further, this study recommends a dynamic model to keep the consistency of the combination of image features, message size and the hiding algorithm in terms of quantization table modification and this model based steganography suggests a dynamic model to cover image statistics. Eventually, the model prevents visually perceptible changes for maximum embedding message bits. The proposed method achieves a good imperceptibility level and it is evaluated by the PSNR value range 30dB to 45dB and maximum message size more than 52 bits per block for the selected JPEG image dataset. The dynamic model fitted between the quantization tables and cover image statistics shows the statistical significance with the p-value 0.0007634 and the model generated between the data hiding pattern and statistical features of DCT coefficients shows the statistical significance with the p-value 4.598e-13. The dynamic model for the selected data hiding patterns in the lower frequency coefficients hides the message and it is stego invariant for message analyzers. Improvement of coronary angiography for quantitative coronary analysis by using a computer vision technique Improvement of coronary angiography for quantitative coronary analysis by using a computer vision technique Kulathilake, KASH Coronary cine-angiography is an invasive medical image modality, which is widely used in Interventional Cardiology for the detection of stenosis in Coronary arteries. Quantitative coronary analysis is one of the demanding areas in medical imaging and in this study a semi automated quantitative coronary analysis method has been proposed. Direct coronary cineangiogram frames are processed in order to obtain the features of lumen such as, vessel boundary, skeleton and luminal diameter along the vessels’ skeleton as the results. The proposed method consists of four main implementation phases namely, pre-processing, segmentation, vessel path tracking and quantitative analysis. The visual quality of the input frames is enhanced within the pre-processing phase. The proposed segmentation phase is implemented based on a spatial filtering and region growing approach. A clinically important vessel region is processed to detect the vessel boundary and skeleton, which is required as prior knowledge for quantitative analysis. Moreover, the vessel diameter is computed while tracking the vessel skeleton path starting from a given seed. The proposed segmentation method possesses 93.73% mean segmentation accuracy and 0.053 mean fallout rate. Moreover, the proposed quantitative analysis method has been validated for assessing its’ technical supportability using a clinically approved data set. As a result of that, this proposed method computes the vessel diameter along the vessel skeleton in single pixel gap and develops the ability to determine the diameter stenosis as the quantitative analysis results. Additionally, the clinical feasibility of the proposed method has been validated to emphasize the clinical usability. Moreover, this study can be further extended to make clinical decisions on stenosis through the functional significance of the vasculature by using proper medical image modality like biplane angiography. Automated pedagogical expert for evaluating web-based e-learning content Automated pedagogical expert for evaluating web-based e-learning content Sirisuriya SCMDS e-Learning has been revolutionizing education system based on the concept of learning occurring at any time and any place. The advent of e-Learning has not only bridged the gap between distance and education but also in student learning and student performance by allowing for more personalized teaching. Behind any successful e-Learning program, it is a necessity to maintain careful design and attractive content that can keep the audience focused and interested. Hence, the importance of evaluating web-based e-Learning content is non-secondary in the e-Learning content development. The evaluation process usually consists of pedagogical evaluation and content evaluation, because e-Learning course material is a combination of the course’s content, as well as the way it is delivered. This research study is mainly focused on automating the pedagogical evaluation component of web-based e-Learning content. In automating the pedagogical evaluation, identifying inconsistencies is the biggest challenge faced by pedagogical experts in the current manual reviewing process, because different institutions use different checklists to pedagogically evaluate their web-based e-Learning content. Developing a calibrated checklist that can be used in the pedagogical evaluation process is the solution to this matter. This calibrated checklist was devised based on studying existing checklists and then a questionnaire was created, and a survey conducted with pedagogical experts to identify the most important review factors which are considered in the pedagogical evaluation process. Additionally, a quantitative formula was devised to weigh the importance of each review factor along with their related SRFs. This study achieves the following objectives. First to build a calibrated checklist that indicates the most important factors for evaluating the pedagogical effectiveness of Web based e-Learning content. Secondly, to prepare a quantitative formulation for determining the pedagogical effectiveness of Web based e-Learning content. Both the checklist and the quantitative formulation can be instrumental towards the development of a theoretical framework for pedagogical compliance of e-Learning content. This framework can provide the foundation to design and develop a tool for assisting pedagogical experts in their evaluation process prior to making a decision whether a particular e-Learning content is well designed or not. Further, it will pave the path to elicit a quantitative approach for pedagogical evaluation. The benchmarked results of automated pedagogical expert results and the manual evaluation results with respect to the variation within one times standard deviation of mean values of manual evaluation have shown the validity of the framework. Further, this study has elicited a quantitative measure to align with manual evaluation to provide consistence evaluation framework. Mon, 01 Jan 2018 00:00:00 GMT 2018-01-01T00:00:00Z A Computational model for recognising students emotions in E-learning systems A Computational model for recognising students emotions in E-learning systems Sandanayake, TC Online learning is a support tool for educators as well as a medium of delivery of any-time, any-where delivery of a content to a dispersed learner community. Web-based learning environments are a relatively new medium of learning to Sri Lankan universities. Like any learning process, online learning depends on effective communication of human knowledge, whether this occurs in a face-to-face classroom or across the Internet. The effectiveness of online learning also depends on establishing two-way communication between facilitators and learners, and among learners themselves. Although both emotions and interest can increase learners’ likelihood to engage in traditional learning, little is known about the influence of emotions and interest in learning activities in a digital environment. Emotions play an essential role in decision making, managing, perceiving and learning and influence the rational thinking process of humans. Emotions are also important in teaching and learning and often find expression in particular ways, such as interactions with others and motivation in learning. The influence of emotions on e-learning is still not emphasized. Continuous and increasing exploration of the complex set of parameters surrounding online learning reveals the importance of the emotional states of learners and especially the relationship between learning and affective behaviour. Previous research have identified that emotions occur while individuals assess events in their environment that are related to the needs, goals and well-being. Moreover, recent research on the emotional response to online learning has focused on the importance of learners’ feelings in relation to the community of learning. The aim of the research is to develop a model to recognize leaner emotions in online learning environment. Through a critical literature review on affective computing, the study has identified several models and selected Barry Kort’s Learning Spiral Model as the prototype model of the research study. The learning spiral model is a four quadrant learning model in which emotions change while the learner moves through quadrants and up the spiral. This study will be presenting a model which describes the relationship between the online learners learning performances and emotions that occur during online learning process. The research study has built a high-level architecture which consists of three sub modules representing the current context on online learning and two sub modules representing the novel approach of affective learning. Experiments were conducted based on the sub modules developed. The research was focused on identifying a suitable tool to recognise the online learner’s emotions. During the comprehensive literature survey, different tools enabling recognising learner emotions were identified and the study has sel