A mobile cloud computing framework integrating multilevel encoding for performance monitoring in telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

Productive on-line student support : developing a system for primary mathematics trainee teachers

This paper outlines the development project for the 'Productive on-line student support system', a student "self-help" system, at Deakin University. The aim of this project was to provide Deakin primary teacher education students with a web-based learning tool that allowed them to assess and diagnose their strengths and weaknesses in mathematics, and supports students in their mathematics learning, and in so doing produce mathematically competent graduates. This project was, like similar programs, a development of peer or cross-age tutoring common in primary and secondary schools. A grant under the Deakin University Strategic Teaching and Learning Grant Scheme enabled a staff team from the mathematics education group, to develop a sophisticated and well-designed system that catered for a wide range of student needs, provided useful feedback, and was engaging and easy to use. The under-pinning software for the system was WebCT, available to staff through the Deakin Studies On-line system, to which students are connected also. The 'Productive on-line student support system' enabled students to determine their own mathematical needs, and have these addressed whenever they wished, as often as they wished, and allowed self-monitoring of progress. An outline of the system and examples of the assessment materials will be presented.

Critical care nurses' haemodynamic decision making

For cardiac surgical patients, the immediate 2-hour recovery period is distinguished by potentially life-threatening haemodynamic instability. To ensure optimum patient outcomes, nurses of varying levels of experience must make rapid and accurate decisions in response to episodes of haemodynamic instability. Decision complexity, nurses’ characteristics, and environmental characteristics, have each been found to influence nurses' decision making in some form. However, the effect of the interplay between these influences on decision outcomes has not been investigated. The aim of the research reported in this thesis was to explore variability in critical care nurses' haemodynamic decision making as a function of interplay between haemodynamic decision complexity, nurses' experience, and specific environmental characteristics by applying a naturalistic decision making design. Thirty-eight nurses were observed recovering patients in the immediate 2-hour period after cardiac surgery. A follow-up semi-structured interview was conducted. A naturalistic decision making approach was used. An organising framework for the goals of therapy related to maintaining haemodynamic stability after cardiac surgery was developed to assist the observation and analysis of practice. The three goals of therapy were the optimisation of cardiovascular performance, the promotion of haemostasia, and the reestablishment of normothermia. The research was conducted in two phases. Phase One explored issues related to observation as method, and identified emergent themes. Phase Two incorporated findings of Phase 1, investigating the variability in nurses' haemodynamic decision making in relation to the three goals of therapy. The findings showed that patients had a high acuity after cardiac surgery and suffered numerous episodes of haemodynamic instability during the immediate 2-hour recovery period. The quality of nurses' decision making in relation to the three goals of therapy was influenced by the experience of the nurse and social interactions with colleagues. Experienced nurses demonstrated decision making that reflected the ability to recognise subtle changes in haemodynamic cues, integrate complex combinations of cues, and respond rapidly to instability. The quality of inexperienced nurses' decision making varied according to the level and form of decision support as well as the complexity of the task. When assistance was provided by nursing colleagues during the reception and recovery of patients, the characteristics of team decision making were observed. Team decision making in this context was categorised as either integrated or non integrated. Team decision making influenced nurses' emotions and actions and decision making practices. Findings revealed nurses' experience affected interactions with other team members and their perceptions of assuming responsibility for complex patients. Interplay between decision complexity, nurses' experience, and the environment in which decisions were made influenced the quality of nurses' decision making and created an environment of team decision making, which, in turn, influenced nurses' emotional responses and practice outcomes. The observed variability in haemodynamic decision making has implications for nurse education, nursing practice, and system processes regarding patient allocation and clinical supervision.

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents' track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified.

Combination of decisions from a multiple neural network classifier system

In this paper, a study of the effectiveness of a multiple classifier system (MCS) in a medical diagnostic task is described. A hybrid network, based on the integration of a fuzzy ARTMAP and the probabilistic neural network, is employed as the basis of the MCS. Outputs from multiple networks are combined using some decision combination method to reach a final prediction. By using a real medical database, a set of experiments has been conducted to evaluate the performance of the MSC with different network configurations. The experimental results reveal the potential of the MCS as a useful decision support tool in the medical field.

Future integrated factories: a system of systems engineering perspective

The manufacturing sector has gone through tremendous change in the last decade. We have witnessed the transformation from stand alone, manual processes to smart and integrated systems, from hand written reports to interactive computer-based dashboards. Future integrated factories will operate as a system of systems through intelligent machines, human factors integration, and integrated supply chains. To effectively operate and manage these emerging enterprises, a systems science approach is required. Modelling and simulation is recognised as a key enabling technology, with application from stakeholder engagement and knowledge elicitation to operational decision support through self-tuning and self-assembling simulations. Our research has led to the introduction of effective modelling and simulation methods and tools to enable real time planning, dynamic risk analysis and effective visualisation for production processes, resources and systems. This paper discusses industrial applicable concepts for real-time simulation and decision support, and the implications to future integrated factories, or factories of the future, are explored through relevant case studies from aerospace manufacturing to mining and materials processing enterprises.

The influence of patient complexity and nurses` experience on haemodynamic decision-making following cardiac surgery

Critical care nurses’ haemodynamic decision-making in the immediate postoperative cardiac surgical context is complex. To optimise patient outcomes, nurses of varying levels of experience are required to make complex decisions rapidly and accurately. In a dynamic clinical context such as critical care, the quality of such decision-making is likely to vary considerably. The aim of this study was to describe variability of nurses’ haemodynamic decision-making in the 2-hour period after cardiac surgery as a function of interplay between decision complexity, nurses’ levels of experience, and the support provided. A descriptive study based on naturalistic decision-making was used. Data were collected using continuous non-participant observation of clinical practice for a 2-hour period and follow-up interview. Purposive sampling was used to recruit 38 nurses for inclusion in the study. The quality of nurses’ decision-making was influenced by interplay between the complexity of patients’ haemodynamic presentations, nurses’ levels of cardiac surgical intensive care experience, and the form of decision support provided by nursing colleagues. Two factors specifically influenced decision-making quality: nurses’ utilisation of evidence for practice and the experience levels of both nurses and their colleagues. The findings have implications for staff resourcing decisions and postoperative patient management, and may be used to inform nurses’ professional development and education.

A knowledge management approach to user support

This paper considers the problem of computer user support and workplace learning in general. Theoretically our work is influenced by ideas on knowledge management, expertise networks and communities of practice. Our approach seeks to tap into the powerful and situated learning potential of the collaborative support provided by colleagues. We consider that such support could be enhanced through the use of a collaborative support system. We outline our investigations into design issues, a generic model and various experiments related to the development of such a system. In particular, we emphasise the value of recorded demonstrations for representing computer-related practice. We present a number of design conclusions derived from our experiences, and warn that whereas active user participation is the essential ingredient in a support system it is perhaps the most difficult thing to achieve.

Simulation-based support tool for operator performance in batch manufacturing

In industry, the workload and utilization of shop floor operators is often misunderstood. In this paper, we will present several real case studies, using Discrete Event Simulation (DES) models, which allow us to better understand operators in a batch manufacturing environment. The first study investigates labour in a machining plant consisting of multiple identical CNC machines that batch produce parts. The second study investigates labour in an eight station, gravity die casting rotary table. The results from these studies have shown that there can be potential improvements made by the production planners in the current labour configuration. In the first case study, a matrix is produced that estimates what the operator's utilization levels will be for various configurations. From this, the preferred operator to machine ratio over a range of cycle times is presented. In the second study, the results have shown that by reducing the casting cycle time, the operator would be overloaded. A discrete event simulation of these two cases highlighted areas that were misunderstood by plant management, and provided them with a useful decision support tool for production planning.

Exploring creativity support systems for the NE"X"T generation

This paper explores the implementation of a creativity support system for tertiary students studying games design and development at Deakin University, Victoria, Australia. The students at the centre of this study are the ‘next’ generation of learners and are often called the net generation because of their pre-imposed affinity for all things ‘online’. The creativity support system for the games students is designed within a ‘whole’ systems context. Focusing on only one tool to augment a person’s creativity does not take into consideration social factors that are pertinent on a person ability to grow their creative behaviours. This study will present a set of factors that each creativity support system should employ to facilitate creative abilities within people, with particular focus on how social activities help to enhance creativity.

Analysis of forest policy using multi-attribute value theory

Contents:
1. Role of multi-criteria decision making in natural resource management /​ Gamini Herath and Tony Prato
2. Analysis of forest policy using multi-attribute value theory /​ Jayanath Ananda and Gamini Herath
3. Comparing Riparian revegetation policy options using the analytic hierarchy process /​ M. E. Qureshi and S. R. Harrison
4. Managing environmental and health risks from a lead and zinc smelter : an application of deliberative multi-criteria evaluation /​ Wendy Proctor, Chris McQuade and Anne Dekker
5. Multiple attribute evaluation of management alternatives for the Missouri River System /​ Tony Prato
6. Multi-criteria decision analysis for integrated watershed management /​ Zeyuan Qiu
7. Fuzzy multiple attribute evaluation of agricultural systems /​ Leonie A. Marks and Elizabeth G. Dunn
8. Multi-criteria decision support for energy supply assessment /​ Bram Noble
9. Seaport development in Vietnam : evaluation using the analytic hierarchy process /​ Tran Phuong Dong and David M. Chapman
10. Valuing wetland aquatic resources using the analytic hierarchy process /​ Premachandra Wattage and Simon Mardle
11. Multiple attribute evaluation for national park management /​ Tony Prato
12. The future of MCDA in natural resource management : some generalizations /​ Gamini Herath and Tony Prato.

Decision making effectiveness in wildfire incident management teams

During large scale wildfires, suppression activities are carried out under the direction of an Incident Management Team (IMT). The aim of the research was to increase understanding of decision processes potentially related to IMT effectiveness. An IMT comprises four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to study IMT processes: computer simulation experiments; analyses of wildfire reports; interviews with IMT members; and cognitive ethnographic studies of IMTs. Three processes were important determinants of IMT effectiveness: information management and cognitive overload; matching component function goals to overall goals; and team metacognition to detect and counter task-disruptive developments. These processes appear to be complex multi-person analogues of individual Incident Command processes identified previously. The findings have implications for issues such as: creating IMTs; training IMTs; managing IMTs; and providing decision support to IMTs.