Abeer Alsadoon

Using simulators for teaching computer organization and architecture

An important problem in teaching the subjects of Computer Architecture and Organization (CO&CA) is the linking of the theoretical knowledge with the practical experience. Visualization of different computer hardware architectures with the use of the simulators enhances the learning process among students. Many useful computer simulators can help address this issue by covering various aspects of CO&CA. The programs range from simple simulators to specific teaching tools to advanced and specialized software. We assessed several simulators and selected one that was freely available and that enabled the students to learn the concepts to the fullest. This paper describes our experience of incorporating simulation tools into teaching the CO&CA to information technology and computing students. We demonstrate that the use of simulators helped students understand better how a computer was constructed and how it functioned internally.

Ethical Implications of User Perceptions of Wearable Devices

Health Wearable Devices enhance the quality of life, promote positive lifestyle changes and save time and money in medical appointments. However, Wearable Devices store large amounts of personal information that is accessed by third parties without user consent. This creates ethical issues regarding privacy, security and informed consent. This paper aims to demonstrate users’ ethical perceptions of the use of Wearable Devices in the health sector. The impact of ethics is determined by an online survey which was conducted from patients and users with random female and male division. Results from this survey demonstrate that Wearable Device users are highly concerned regarding privacy issues and consider informed consent as “very important” when sharing information with third parties. However, users do not appear to relate privacy issues with informed consent. Additionally, users expressed the need for having shorter privacy policies that are easier to read, a more understandable informed consent form that involves regulatory authorities and there should be legal consequences the violation or misuse of health information provided to Wearable Devices. The survey results present an ethical framework that will enhance the ethical development of Wearable Technology.

Enhanced e-health framework for security and privacy in healthcare system

Patient health record (PHR) is a rising patient centric model which is frequently outsourced to store at third party. This addresses the issue in privacy such as hiding the sensitive health data of a patient which can be assessed by unauthorized users. In this paper, a new secured e-health framework has proposed. In this framework, patient centric personal data and access control scheme with enhanced encryption method has been considered. Security and privacy of personal health information have been identified by digital signature and patient pseudo identity as well as. This paper address the enhanced security model for more authentication and authorization functionality and expects to discover the new technique that can be utilized to build the efficiency in e-health care system based on security, privacy and user satisfaction. The survey has been conducted to test the proposed e-health framework. The data has been analyzed using SPSS tool.

Incorporating skin color for improved face detection and tracking system

Face detection and tracking have wide applications, for example, law enforcement, gaming, image search, marketing, etc. The detection and tracking tasks are quite challenging. Tracking the face in an image includes gender identification and segmenting the face using the skin color. In the past, many methods have been proposed to identify a face in videos, such as feature-based detection, skin color segmentation, appearance, and eigen-based identification. These techniques use image classifiers (both weak and strong) but are not very accurate. This paper introduces a hybrid method that utilizes skin color for increasing accuracy of detection and tracking. In our method, an image is rescaled and divided based on skin color, using RGB (red-green-blue) color combinations. The divided skin tone image is combined with the edges of images before applying the morphological operations. As a final step, a bounding box appears on the detected face.

Enhanced Apriori Algorithm model in course suggestion system

Association Rule Mining (ARM) includes one of the most popular algorithms called Apriori Algorithm (AA) in it. AA has some limitations and areas for improvement related to the execution time consumption. The extended AA has some improvements based on the existing algorithm AA. The main findings indicate that not much has been done in an educational data mining considering the volume of data that is observed. Therefore, it is intended to evaluate to what extent an ARM can be utilized in an educational data mining context. Specifically, this paper develops an extended AA mining algorithm and applies it to the higher education system, focusing on the students course planning system. This project focuses on introducing AA and ARM, implementing the enhanced features of the AA in educational data, develop the course recommender model. Finally, it evaluates the enhanced algorithm compared to the existing AA to help build a course suggestion system for students.

A novel rotational matrix and translation vector algorithm: geometric accuracy for augmented reality in oral and maxillofacial surgeries

Augmented reality‐based surgeries have not been successfully implemented in oral and maxillofacial areas due to limitations in geometric accuracy and image registration. This paper aims to improve the accuracy and depth perception of the augmented video.

Hybrid Secure and Scalable Electronic Health Record Sharing in Hybrid Cloud

Cloud computing has become an integral part of theoperation of health. However, there are major security andprivacy issues in terms of accessing medical records from thehybrid cloud environment. In this paper, a new secure hybridElectronic Health Record system is proposed. In this framework, two efficient encryption methods are combined for fine grainedaccess control and protection of data privacy. Multi-authority andKey-based encryption schemes are used for the encryption of eachpart of health records after dividing those records using a verticalpartitioning method. Multi-authority encryption schemes areprimarily used in the Public Domains (PUDs), while Key-basedencryption schemes are prevalent in Personal Domains (PSDs). Together, they provide, secure data access and authentication ofusers. Implementation is facilitated using Windows Azure CloudComputing platform.

Cloud Computing Potability with Risk Assessment

The purpose of this study is to identify the risks associated with job migration from private to public cloud. Meeting the resource requirements of a particular task in the private environment causes only minimal exposure of the core applications to public cloud. The potential risk issues to the private data and applications can thus be minimized. However, risks are significantly higher when core applications are migrated to public cloud. This places high importance on accurate identification of risk factor in job migration is extremely important. This report proposes to identify categories of risk arising from internal and external environment. This will require use of mitigating strategies to eliminate risk factors associated with the phases of a job migration. Such migration is achieved by taking in to account the roles of the dedicated users and set of common constraints. The findings from this research results indicate that safe job migration can be achieved with effective risk management and mitigation techniques.

SIFT based approach on Bangla sign language recognition

This paper presents a SIFT-based geometrically computational approach to vigorously recognize Bangla sign language (BdSL). Gaussian distribution and grayscaling techniques are applied for image processing and normalizing the sign image. After this pre-processing, features are extracted from the sign image by implementing scale invariant feature transform. Acquiring all descriptors from the sign image, k-means clustering is executed on all the descriptors which are previously computed by SIFT. Based on the sample training set, each of the cluster denotes as a visual word. Considering the histograms of the clustering descriptors, Bag of words model is introduced on this hybrid approach which develops a set of visual vocabulary. Finally for each of sign word, a binary linear support vector machine (SVM) classifier is trained with a respective training data set. Considering these binary classifiers, we obtained a respective recognition rate on both Bangla signs of expressions and alphabets.

Early diagnosis of breast cancer using contrast limited adaptive histogram equalization (CLAHE) and Morphology methods

The main aim of this paper is to enhance using hybrid solution for early diagnosis of breast cancer using mammogram images. For the CAD system for any image processing system follows mainly four steps that is image pre processing, segmentation, features extraction and classification and evaluation. For this research purpose follows the same structure using best possible methods in each stage found in literature review and proposed solution. From the experiment hybrid image enhancement method using CLAHE and Morphology method helps to enhance image for further computation in CAD system to early diagnosis of breast cancer using mammogram images while maintaining processing time as previous best solution. From the study depict that the purposed solution can help to enhance mammogram images for the further processing for segmentation, feature extraction and classification. However, further testing and implementation of proper classification method needed for before clinical use.