Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

Assessing the evidence: Student response system versus computer based testing for undertaking multiple choice question assessment in undergraduate nursing education

There is a dearth of evidence focusing on student preferences for computer-based testing versus
testing via student response systems for summative assessment in undergraduate education.
This quantitative study compared the preference and acceptability of computer-based testing
and a student response system for completing multiple choice questions in undergraduate
nursing education. After using both computer-based testing and a student response system to
complete multiple choice questions, 192 first year undergraduate nursing students rated their
preferences and attitudes towards using computer-based testing and a student response system.
Results indicated that seventy four percent felt the student response system was easy to use.
Fifty six percent felt the student response system took more time than the computer-based testing
to become familiar with. Sixty Percent felt computer-based testing was more users friendly.
Seventy Percent of students would prefer to take a multiple choice question summative exam
via computer-based testing, although Fifty percent would be happy to take using student response
system. Results are useful for undergraduate educators in relation to student’s preference
for using computer-based testing or student response system to undertake a summative
multiple choice question exam

Turbo channel estimation and equalisation for a superposition-based cooperative system

This study investigates the superposition-based cooperative transmission system. In this system, a key point is for the relay node to detect data transmitted from the source node. This issued was less considered in the existing literature as the channel is usually assumed to be flat fading and a priori known. In practice, however, the channel is not only a priori unknown but subject to frequency selective fading. Channel estimation is thus necessary. Of particular interest is the channel estimation at the relay node which imposes extra requirement for the system resources. The authors propose a novel turbo least-square channel estimator by exploring the superposition structure of the transmission data. The proposed channel estimator not only requires no pilot symbols but also has significantly better performance than the classic approach. The soft-in-soft-out minimum mean square error (MMSE) equaliser is also re-derived to match the superimposed data structure. Finally computer simulation results are shown to verify the proposed algorithm.

A Proposed Framework for Simulation Based Learning of Inheritance

Different types of serious games have been used in elucidating computer science areas such as computer games, mobile games, Lego-based games, virtual worlds and webbased games. Different evaluation techniques have been conducted like questionnaires, interviews, discussions and tests. Simulation have been widely used in computer science as a motivational and interactive learning tool. This paper aims to evaluate the possibility of successful implementation of simulation in computer programming modules. A framework is proposed to measure the impact of serious games on enhancing students understanding of key computer science concepts. Experiments will be held on the EEECS of Queen’s University Belfast students to test the framework and attain results.

Approved Social Work Training in Northern Ireland: Using Research to Examine Competence-Based Learning and Influence Policy Change

This article reports on how research activity helped describe and analyse ASW (Approved Social Worker) learning experience as well as acting as a catalyst for change and development in policy and practice in Northern Ireland. The paper contextualizes the study by outlining the legislation, the main features of the ASW role and the approach to ASW training in Northern Ireland, and by reviewing the literature on the efficacy and value of competence-based learning. While the findings do not provide conclusive evidence that a competence-based approach is inherently more effective than previous courses, they do indicate that candidates who were trained in this way were moderately more satisfied than those who had participated in non-competence based programmes. The research also highlights the importance of the interrelationship between training, practice experience and support in developing and sustaining competence. The paper concludes with a review of the recommendations arising from the study and an analysis of the developments in training and regulations relating to practice experience and re-approval of ASWs since publication of the research. The study is of contemporary interest given the proposed changes to the role of ASWs/Mental Health Officers in the context of the reviews of UK mental health law.