A multilingual automated web usability evaluation agent

The research underlying this thesis explored the development of a customised, intelligent and automated approach to web usability evaluation. An extensive survey of existing web usability evaluation tools was carried out to identify to weaknesses that could be investigated. As result three different issues are addressed in this thesis: Improving and testing usability guidelines particularly for languages other than English; Customising the evaluation; Developing an intelligent (capable of learning) evaluation technique. This thesis presents a new methodology that uses agent technology, which can act and interact on behalf of its owner (the webmaster), to evaluate web pages. The evaluation involves two kinds of customisation, one which reflects the users' tastes and the other the aims of the webmaster. In investigating customisation of web pages to reflect users' tastes the research considered applying this multilingual interface agent approach to the evaluation of multilingual pages in scripts other than the usual Latin. But no guidelines appear to exist for such scripts thus the first difficulty in assessing non-English web pages is the lack of any reliable guidelines. In order to explore multilingual evaluation the researcher first had established guidelines and chose to investigate Arabic. As result usability guidelines for Arabic were established via usability testing. The guidelines are an interesting result of the research in themselves. This thesis presents a set of usability guidelines appropriate for evaluating Arabic web pages produced by testing 196 Arabic users. Also, it validates some of the current usability guidelines for Latin scripts. An interesting variation appeared between the presentations of the two dissimilar scripts, these variations affect font size, emphasized text presentation, the number of links in the web page and the meanings associated with colours. The second form of customisation is represented in the ability to modify the usability evaluation to reflect the webmaster's preferences. This requires an intelligent approach involving learning. Three different kinds of learning were considered; fuzzy average learning, fuzzy learning and Q-learning. All are examined in this thesis in order to identify the most appropriate approach to apply. As the multilingual interface agent learns form its webmaster, Q-learning produced the most accurate evaluation. This thesis represents a useful first step towards multilingual, intelligent, automated web usability evaluation using an agent technique. The automated web usability, multilingual interface agent developed can be customised to suit its users and improve its evaluation in response to the needs of its owner

Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers

Effects of E-Games on the Development of Saudi Children with Attention Deficit Hyperactivity Disorder Cognitively, Behaviourally and Socially: An Experimental Study

Attention Deficit Hyperactivity Disorder (ADHD) is a set of behavioural characteristics disorder, such as inattentiveness, hyperactivity and/or impulsiveness. It can affect people with different intelligent abilities, and it may affect their academic performance, social skills and generally, their lives. Usually, symptoms are not clearly recognized until the child enters school, most cases are identified between the ages 6 to 12. In the kingdom of Saudi Arabia (KSA), ADHD is a widely spread disorder among young children. Usually, they suffer from distraction and lack of focus, and hyperactivity, which reduce their academic achievements. As technology have been used in classrooms to facilitate the information delivery for students, and to make learning fun; some of these technologies have actually been applied in many schools in KSA with normal students, but unfortunately no studies were reported by the time of writing this paper. Specifically, there are no studies done for using any type of technology to help Saudi students with ADHD reaching up their peers academically. Because of that, our focus in this study is to investigate the effect of using technology, particularly e-games, to improve Saudi children with ADHD cognitively, behaviourally and socially. As well as evaluating the interaction between those children with the game interface. Thus, the investigation done through exploring the interaction of web-based games that runs on Tablets. The respondents are 17 ADHD children aged from 6–12 in classroom settings. The study involves focussing on interface of the games stimulate different executive functions in the brain, which is responsible for the most important cognitive capacities, such as: Sustained Attention, Working Memory, and Speed of Processing. Ethnographic method of research was used, which involved observing students’ behaviour in classroom, to gather information and feedback about their interaction with the application. National Institutes of Health (NIH) tests were used in pre- and post- intervention to measure improvements in attention, processing speed and working memory. Students’ test scores of main school subjects were taken pre- and post-intervention to measure enhancement in academic performance. Results show that using the application significantly improve cognitive capacities for participants, which affected their academic grades in Math, English and Science, as well as its positive influence on their behaviour. In addition, the application’s interface was found easy to use and subjectively pleasing. As a conclusion, the application considered effective and usable

Effects of E-Games on the Development of Saudi Children with Attention Deficit Hyperactivity Disorder Cognitively, Behaviourally and Socially: An Experimental Study

Attention Deficit Hyperactivity Disorder (ADHD) is a set of behavioural characteristics disorder, such as inattentiveness, hyperactivity and/or impulsiveness. It can affect people with different intelligent abilities, and it may affect their academic performance, social skills and generally, their lives. Usually, symptoms are not clearly recognized until the child enters school, most cases are identified between the ages 6 to 12. In the kingdom of Saudi Arabia (KSA), ADHD is a widely spread disorder among young children. Usually, they suffer from distraction and lack of focus, and hyperactivity, which reduce their academic achievements. As technology have been used in classrooms to facilitate the information delivery for students, and to make learning fun; some of these technologies have actually been applied in many schools in KSA with normal students, but unfortunately no studies were reported by the time of writing this paper. Specifically, there are no studies done for using any type of technology to help Saudi students with ADHD reaching up their peers academically. Because of that, our focus in this study is to investigate the effect of using technology, particularly e-games, to improve Saudi children with ADHD cognitively, behaviourally and socially. As well as evaluating the interaction between those children with the game interface. Thus, the investigation done through exploring the interaction of web-based games that runs on Tablets. The respondents are 17 ADHD children aged from 6–12 in classroom settings. The study involves focussing on interface of the games stimulate different executive functions in the brain, which is responsible for the most important cognitive capacities, such as: Sustained Attention, Working Memory, and Speed of Processing. Ethnographic method of research was used, which involved observing students’ behaviour in classroom, to gather information and feedback about their interaction with the application. National Institutes of Health (NIH) tests were used in pre- and post- intervention to measure improvements in attention, processing speed and working memory. Students’ test scores of main school subjects were taken pre- and post-intervention to measure enhancement in academic performance. Results show that using the application significantly improve cognitive capacities for participants, which affected their academic grades in Math, English and Science, as well as its positive influence on their behaviour. In addition, the application’s interface was found easy to use and subjectively pleasing. As a conclusion, the application considered effective and usable

Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results

An energy-efficient fog-to-cloud Internet of Medical Things architecture

In order to increase the reliability, accuracy, and efficiency in the eHealth, Internet of Medical Things is playing a vital role. Current development in telemedicine and the Internet of Things have delivered efficient and low-cost medical devices. The Internet of Medical Things architectures being developed do not completely recognize the potential of Internet of Things. The Internet of Medical Things sensor devices have limited computation power; in case if a patient is using implanted medical devices, it is not easy to recharge or replace the devices immediately. Biosensors are small devices with limited energy if these devices do not wisely utilize the energy may drain sharply and devices become inactive. The current medical solutions place the bulk of data on cloud-based systems that ultimately creates a bottleneck. In this article, an energy-efficient fog-to-cloud Internet of Medical Things architecture is proposed to optimize energy consumption. In the proposed architecture, Bluetooth enabled biosensors are used, because Bluetooth technology is an energy efficient and also helps to enable the sleep and awake modes. The proposed fog-to-cloud Internet of Medical Things works in three different modes periodic, sleep–awake, and continue to optimize the energy consumption. The proposed technique enabled the sensing modes that gathers the patients’ data efficiently based on their health conditions. The sensed data are transmitted to the relevant fog and cloud devices for further processing. The performance of fog-to-cloud Internet of Medical Things is evaluated through simulation; the results are compared with the results of existing techniques in terms of an end-to-end delay, throughput, and energy consumption. It is analyzed that the proposed technique reduces the energy consumption between 30% and 40%