An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.

A learning approach for performance evaluation of local network

In this paper, a novel approach is developed to evaluate the overall performance of a local area network as well as to monitor some possible intrusion detections. The data is obtained via system utility 'ping' and huge data is analyzed via statistical methods. Finally, an overall performance index is defined and simulation experiments in three months proved the effectiveness of the proposed performance index. A software package is developed based on these ideas.

Bridging the divide between theory and practice. Experiential learning approaches for tourism and hospitality education

Experiential learning approaches such as role-play have been found to be valuable methods of bridging the divide between academic knowledge and practical skills, a problem often cited in tourism and hospitality management education. Such approaches have been found to contribute towards deeper learning by enhancing students' interest, motivation, participation, knowledge and skill development. This paper reports on the implementation of an experiential learning approach designed to encourage and facilitate deeper learning approaches, with the contributing aims of providing students with a more interesting learning experience and a broader set of skills for future employment.

A reflexive course for Masters students to understand and plan their own continuing professional development

Continuing Professional Development (CPD) is seen as a vital part of a professional engineer’s career, by professional engineering institutions as well as individual engineers. Factors such as ever-changing workforce requirements and rapid technological change have resulted in engineers no longer being able to rely just on the skills they learnt at university or can pick up on the job; they must undergo a structured professional development with clear objectives to develop further professional knowledge, values and skills. This paper presents a course developed for students undertaking a Master of Engineering or Master of Project Management at the University of Queensland. This course was specifically designed to help students plan their continuing professional development, while developing professional skills such as communication, ethical reasoning, critical judgement and the need for sustainable development. The course utilised a work integrated learning pedagogy applied within a formal learning environment, and followed the competency based chartered membership program of Engineers Australia, the peak professional body of engineers in Australia. The course was developed and analysed using an action learning approach. The main research question was “Can extra teaching and learning activities be developed that will simulate workplace learning?” The students continually assessed and reflected upon their current competencies, skills and abilities, and planed for the future attainment of specific competencies which they identified as important to their future careers. Various evaluation methods, including surveys before and after the course, were used to evaluate the action learning intervention. It was found that the assessment developed for the course was one of the most important factors, not only in driving student learning, as is widely accepted, but also in changing the students’ understandings and acceptance of the need for continuous professional development. The students also felt that the knowledge, values and skills they developed would be beneficial for their future careers, as they were developed within the context of their own professional development, rather than to just get through the course. © 2005, American Society for Engineering Education

Listening to the 'voices' of students with severe learning difficulties through a task-based approach to research and learning in physical education

In this article, Hayley Fitzgerald, Anne Jobling and David Kirk consider the physical education and sporting experiences of a group of students with severe learning difficulties. Their study is thought provoking, not only because of the important and somewhat neglected subject matter, but equally for the research approach adopted. The way in which the study engaged with the students and the insights gained from that engagement will be of particular interest to practitioner researchers.

Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.