Image classification : a study in age-related macular degeneration screening

This thesis presents research work conducted in the field of image mining. More specifically, the work is directed at the employment of image classification techniques to classify images where features of interest are very difficult to distinguish. In this context, three distinct approaches to image classification are proposed. The first is founded on a time series based image representation, whereby each image is defined in terms of histograms that in turn are presented as "time series" curves. A Case Based Reasoning (CBR) mechanism, coupled with a Time Series Analysis (TSA) technique, is then applied to classify new "unseen" images. The second proposed approach uses statistical parameters that are extracted from the images either directly or indirectly. These parameters are then represented in a tabular form from which a classifier can be built on. The third is founded on a tree based representation, whereby a hierarchical decomposition technique is proposed. The images are successively decomposed into smaller segments until each segment describes a uniform set of features. The resulting tree structures allow for the application of weighted frequent sub-graph mining to identify feature vectors representing each image. A standard classifier generator is then applied to this feature vector representation to produce the desired classifier. The presented evaluation, applied to all three approaches, is directed at the classification of retinal colour fundus images; the aim is to screen for an eye condition known as Age-related Macular Degeneration (AMD). Of all the approaches considered in this thesis, the tree based representation coupled with weighted frequent sub-graph mining produced the best performance. The evaluation also indicated that a sound foundation has been established for future potential AMD screening programmes

Image mining approaches for the screening of age-related macular degeneration

Age-related Macular Degeneration (AMD) is the most common cause of irreversible vision loss in those aged over 50. In this chapter we investigating two techniques to support automated AMD screening. First of all we conceptualise AMD screening in terms of a binary classification problem (disease v. no-disease). We then propose and compare two very different techniques whereby the desired classification can be undertaken. The first is founded on a histogram based retinal image representation and the second on a graph based representation. In the histogram based approach each image is defined in terms of a histograms that in turn is presented as "time series curves". Given a training set (a collection of labelled positive and negative examples) we create a Case Base (CB) of labelled curves to which a Case Based Reasoning (CBR) mechanism is applied so as to classify "unseen" images according to whether they feature AMD or not. Curve comparison is conducted using a time series comparison technique. For the graph mining based approach a hierarchical decomposition technique is proposed, whereby pre-labelled retinal images contained in a training set, are successively decomposed into smaller and smaller segments until each segment describes a uniform set of features. The resulting decomposition is stored in a tree structure, one per image, to which a frequent sub-graph (sub-tree) mining technique is applied so as to identify the frequently occurring sub-trees that exist within the overall tree data set. The identified sub-trees then form the global attribute set from which a collection of feature vectors (one per image) is derived so as to describe the training set. A standard classifier generator is then applied to this feature vector representation to produce the desired classifier. The two approaches are compared and evaluated using two publicly available data sets, ARIA and STARE, respectively comprising 161 and 97 pre-labelled retinal images. The paper details both approaches and reports on their evaluation

Evaluation on effectiveness of learning linear algebra using gamification

This study evaluate effectiveness of learning Linear Algebra using gamification strategy. In this study, gamification with storytelling strategy is used as teaching tools to attract student to learn Linear Algebra. This study using Polytechnic Malaysia syllabus with focus group of Diploma students for semester three (Mechanical Engineering) and semester four (Electrical Engineering) for two topics; Matrix and Numerical Method. They are five methods of calculation simultaneous linear equations which is „Inverse‟, „Cramer's Rule‟, „Gauss Elimination‟, „Lower Upper Doolittle‟ and „Lower Upper Crout‟. They are three main phases to develop this gamification; Pedagogy Phase, Design Phase and Evaluation Phase. Mixed methods approach combining quantitative (survey) and qualitative (Electroencephalogram) is used to evaluate students learning process using Linear Algebra gamification application. The findings of the five items surveyed showed that the acceptance of the prototype of the Linear Algebra Gamification Application was very encouraging from a total of 104 students. This is because all 38 questions for the five items earn a median of four and this indicates the majority of students choose “Agree” and “Strongly Agree”. The findings also show the percent “Agree” and “Strongly Agree” for all questions having a high percentage of between 61.5 and 94.2. This shows more than half satisfied and likes to use the Linear Algebra Gamification Application prototype. With the development of the Linear Algebra Gamification Application prototype, it is hoped that the use of learning based can be extended to a variety of subjects as well as topics to make the learning process more interesting and fun as well as helping to motivate students to learn

Fast robot path planning with Laplacian Behaviour-Based control via four-point explicit decoupled group SOR

This study proposed a robot path planning technique that employs Laplacian Behaviour-Based Control (LBBC) for space exploration which relies on the use of Laplace’s equation to constrain the generation of the potential function of the configuration space of a mobile point-robot. The LBBC provides the Searching algorithm with the capability to escape from flat region, whilst iteration via Four-point Explicit Decoupled Group SOR (4EDGSOR) provides fast computation for solving the Laplace’s equation that represent the potential values of the configuration space

Modified canny edge detection technique for identifying endpoints

Edge detection is an image processing technique that retains the edges of an object in an image while discarding other features. The Canny edge detection technique is regarded as one of the most successful edge detection algorithms because of the good edge detection effect. However, one of its problems is the discontinued edges. In this paper, we present an endpoint identification algorithm that can pinpoint the position of the discontinued edges. After the endpoints are identified, they are paired together based on distance, and the broken gaps are filled by connecting the endpoints. Results have shown that, visually, our method has fewer discontinued edges when compared to Canny. Also, the mean square error of our method is lower than traditional Canny, indicating that our technique produces edge images that are more accurate than the traditional Canny

Mechanism for sarcasm detection and classification in Malay social media

The classification of users’ sentiment from social media data can be used to learn public opinion on certain issues. The presence of sarcasm in sentences can hamper the performance of the classification as it tends to “fool” the system. In this paper, we investigate mechanisms for detecting sarcasm in Malay social media data that contain sarcastic contents; more specifically the public comments on economic related posts on Facebook. Two features were investigated; the n-gram and punctuation marks. Features selection in the form of Pearson’s correlation was then applied to reduce the features size. To measure the performances of the selected features, two supervised classification techniques were employed which are k-Nearest Neighbors and non-linear Support Vector Machine. Experiments on sarcasm detection and classification were conducted. Results show that combination of n-gram and punctuation marks produced the best F -measure and Area Under Curve of 0.818 for sarcasm detection. Extended experiment on sarcasm classification recorded F -measure of 0.991 with Area Under Curve of 0.994 for sarcasm positivity while F -measure of 0.902 with Area Under Curve of 0.846 for sarcasm negativity

Enhanced Canny edge detection for Covid-19 and pneumonia X-Ray images

In image processing, one of the most fundamental technique is edge detection. It is a process to detect edges from images by identifying discontinuities in brightness. In this research, we present an enhanced Canny edge detection technique. This method integrates local morphological contrast enhancement and Canny edge detection. Furthermore, the proposed edge detection technique was also applied for pneumonia and COVID-19 detection in digital x-ray images by utilising convolutional neural networks. Results show that this enhanced Canny edge detection technique is better than the traditional Canny technique. Also, we were able to produce classifiers that can classify edge x-ray images into COVID-19, normal, and pneumonia classes with high accuracy, sensitivity, and specificity

A review on complex event processing in RFID system

The motivation of this paper is to review some of the leading research issues encountered by current Complex Event Processing (CEP) techniques. General CEP system undoubtedly laid a certain degree of uncertainties due to some unforeseen reasons such as inaccurate measurements through network failures or unpredicted interference from the system failures. A variety of traditional to the most current techniques was disclosed with some features, which are rarely considered in typical CEP problems. Besides, this paper also discusses a broad review of modern and future solutions, including techniques beyond mainstreams in complex event processing. Based on the critical analysis of prior techniques and solutions, therefore, we further the research and propose a solution for a new type of event processing engine especially in RFID for future reference

A survey on proof of retrievability for cloud data integrity and availability: cloud storage state-of-the-art, issues, solutions and future trends

Cloud storage has emerged as the latest trend for data storage over the traditional storage method which consume more storage spaces of data owner resources for backup and disaster recovery purposes. Due to the openness nature of cloud storage, trustworthy to the storage providers remains a critical issue amongst data owners. Hence, a huge number of businesses around the world remains choosing traditional storage method over cloud storage. This indicates a need for cloud storage providers to adopt cloud integrity schemes to ensure the outsourced data is secured to gain trustworthiness from clients. There are two main cloud integrity schemes available to ensure data integrity and availability: (i) Provable Data Possession (PDP) and (ii) Proof of Retrievability (PoR). PDP and PoR are protocols designed for cloud storage to proof to clients that the stored data is intact. Although PDP and PoR have similar functionality for providing cloud data integrity and availability, PoR is found to be much better than PDP with respect to full data retrievability as PoR provides recovery to faulty or corrupted outsourced data in which PDP does not cover. The objective of this paper is to examine the state-of-the-art of PoR and subsequently to identify the issues of employing PoR on cloud storage and suggest possible solutions. We analyse available PoR schemes. Then, the issues and challenges as a result of employing PoR specifically and cloud storage generally are described. Some possible countermeasures to address the identified issues are suggested. Finally, the potential future work of PoR schemes and future trends of cloud storage are presente