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.

Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes

Global positioning system (GPS) technology has improved the speed, accuracy, and ease of time-motion analyses of field sport athletes. The large volume of numerical data generated by GPS technology is usually summarized by reporting the distance traveled and time spent in various locomotor categories (e.g., walking, jogging, and running). There are a variety of definitions used in the literature to represent these categories, which makes it nearly impossible to compare findings among studies.

The purpose of this work was to propose standard definitions (velocity ranges) that were determined by an objective analysis of time-motion data. In addition, we discuss the limitations of the existing definition of a sprint and present a new definition of sprinting for field sport athletes.

Twenty-five GPS data files collected from 5 different sports (men’s and women’s field hockey, men’s and women’s soccer, and Australian Rules Football) were analyzed to identify the average velocity distribution. A curve fitting process was then used to determine the optimal placement of 4 Gaussian curves representing the typical locomotor categories. 

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.

Social media as an information system : improving the technological agility

There is an increased focus on research involving social media. This research however has failed to catch up with the pace of the technology development and may prove disadvantageous for both practice and theory. The longitudinal study presented in the paper was conducted over a 3-year period involving Australian banks and popular social media technologies. The paper empirically tests the Honeycomb model as a tool that enhances the technological agility of social media. The paper fills a key research gap and provides dynamism to social media strategy formation, continuous improvement of strategy development in support of greater business agility.

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.

Strategic facilities management system in relation with building performance; the significance and relationship

Healthcare design frequently involves complex concepts that are difficult to measure and evaluate because the building require a modern, quality, functional and therapeutic environment. For this specific reason, facilities management has become a very important support system to ensure smoothness in healthcare business. Facilities management in healthcare building is a complicated system involving multiple layers of administrative division and sub-divisions. Building performance such as building impact, function and quality prove to have significant impact on strategic facilities management. This paper will do an extensive review of strategic healthcare business management as a holistic approach and examine how facilities management can effectively manage their division with consideration and understanding of building performance. The correlation between strategic facilities management and building performance will be identified and a framework for strategic FM system with regards to building performance will be developed.

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.

Enhancing RFID data quality and reliability

This thesis addressed the problem of data quality, reliability and energy consumption of networked Radio Frequency Identification systems for business intelligence applications decision making processes. The outcome of the research substantially improved the accuracy and reliability of RFID generated data as well as energy depletion thus prolonging RFID system lifetime.