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Fusion of information from multiple modalities in Human Computer Interfaces (HCI) has gained a lot of attention in recent years, and has far reaching implications in many areas of human-machine interaction. However, a major limitation of current HCI fusion systems is that the fusion process tends to ignore the semantic nature of modalities, which may reinforce, complement or contradict each other over time. Also, most systems are not robust in representing the ambiguity inherent in human gestures. In this work, we investigate an evidential reasoning based approach for intelligent multimodal fusion, and apply this algorithm to a proposed multimodal system consisting of a Hand Gesture sensor and a Brain Computing Interface (BCI). There are three major contributions of this work to the area of human computer interaction. First, we propose an algorithm for reconstruction of the 3D hand pose given a 2D input video. Second, we develop a BCI using Steady State Visually Evoked Potentials, and show how a multimodal system consisting of the two sensors can improve the efficiency and the complexity of the system, while retaining the same levels of accuracy. Finally, we propose an semantic fusion algorithm based on Transferable Belief Models, which can successfully fuse information from these two sensors, to form meaningful concepts and resolve ambiguity. We also analyze this system for robustness under various operating scenarios.

Fusion of information from multiple modalities in Human Computer Interfaces (HCI) has gained a lot of attention in recent years, and has far reaching implications in many areas of human-machine interaction. However, a major limitation of current HCI fusion systems is that the fusion process tends to ignore the semantic nature of modalities, which may reinforce, complement or contradict each other over time. Also, most systems are not robust in representing the ambiguity inherent in human gestures. In this work, we investigate an evidential reasoning based approach for intelligent multimodal fusion, and apply this algorithm to a proposed multimodal system consisting of a Hand Gesture sensor and a Brain Computing Interface (BCI). There are three major contributions of this work to the area of human computer interaction. First, we propose an algorithm for reconstruction of the 3D hand pose given a 2D input video. Second, we develop a BCI using Steady State Visually Evoked Potentials, and show how a multimodal system consisting of the two sensors can improve the efficiency and the complexity of the system, while retaining the same levels of accuracy. Finally, we propose an semantic fusion algorithm based on Transferable Belief Models, which can successfully fuse information from these two sensors, to form meaningful concepts and resolve ambiguity. We also analyze this system for robustness under various operating scenarios.

This study investigates the influence of unscheduled maintenance activities on delays and disruptions during the execution of aircraft heavy maintenance services by developing a simulation model based on Systems Dynamics (SD) and supported by an Evidential Reasoning (ER) rule model. The SD model studies the complex interrelationship between scheduled and unscheduled tasks and its impact on delays during a maintenance service execution. It was found that the uncertain nature of the unscheduled maintenance tasks hinders the planning, control and allocation of resources, increasing the chances to miss deadlines and incur in cost overruns. Utilising causal loop diagrams and SD simulation the research explored the relevance that the resource allocation management, the precise estimation of the unscheduled tasks and their prompt identification have on the maintenance check duration. The influence that delays and attitudes in the decision-making process have on project performance was also investigated. The ER rule model investigates the uncertainty present during the execution of a maintenance check by providing a belief distribution of the expected unscheduled maintenance tasks. Through a non-parametric discretisation process, it was found that the size and array of distribution intervals play a key role in the model estimation accuracy. Additionally, a sensitivity analysis allowed the examination of the significance that the weight, reliability and dependence of the different pieces of evidence have on model performance. By analysing and combining historical data, the ER rule model provides a more realistic and accurate prediction to analyse variability and ambiguity. This research extends SD capabilities by incorporating the ER rule for analysing system uncertainty. By using the belief distributions provided by the ER model, the SD model can simulate the variability of the process given certain pieces of evidence. This study contributes to the existing knowledge in aircraft maintenance management by analysing, from a different perspective, the impact of uncertain unscheduled maintenance activities on delays and disruptions through an integrated approach using SD and the ER rule. Despite the fact that this research focuses on studying a particular problem in the airline industry, the findings and conclusions obtained could be used to understand and address problems embodying similar characteristics. Therefore, it can be argued that, due to the close similarities between the heavy maintenance process and complex projects, these contributions can be extended to the Project Management field.

Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.

This research explores the theory and practice of risk assessment and project evaluationand proposes novel alternatives. Reviewing literature revealed a continuous endeavourfor better project risk modelling and analysis. A number of proposals for improving theprevailing Probability-Impact (P-I) risk model can be found in literature. Moreover,researchers have investigated the feasibility of different theories in analysing projectrisk. Furthermore, various decision support systems (DSSs) are available for aidingpractitioners in risk assessment and decision making. Unfortunately, they are sufferingfrom a low take-up. Instead, personal judgment and past experience are mainly used foranalysing risk and making decisions.In this research, a new risk model is proposed through extending the P-I risk model toinclude a third dimension: probability of impact materialisation. Such an extensionreflects the characteristics of a risk, its surrounding environment and the ability ofmitigating its impact. A new assessment methodology is devised. Dempster-ShaferTheory of Evidence (DST) is researched and presented as a novel alternative toProbability Theory (PT) and Fuzzy Sets Theory (FST) which dominate the literature ofproject risks analysis. A DST-based assessment methodology was developed forstructuring the personal experience and professional judgment of risk analysts andutilising them for risk analysis. Benefiting from the unique features of the EvidentialReasoning (ER) approach, the proposed methodology enables analysts to express theirevaluations in distributed forms, so that they can provide degrees of belief in apredefined set of assessment grades based on available information. This is a veryeffective way for tackling the problem of lack of information which is an inherentfeature of most projects during the tendering stage. It is the first time that such anapproach is ever used for handling construction risk assessment. Monetary equivalent isused as a common scale for measuring risk impact on various project success objectives,and the evidential reasoning (ER) algorithm is used as an assessment aggregation toolinstead of the simple averaging procedure which might not be appropriate in allsituations. A DST-based project evaluation framework was developed using projectrisks and benefits as evaluation attributes. Monetary equivalent was used also as acommon scale for measuring project risks and benefits and the ER algorithm as anaggregation tool.The viability of the proposed risk model, assessment methodology and projectevaluation framework was investigated through conducting interviews with constructionprofessionals and administering postal and online questionnaires. A decision supportsystem (DSS) was devised to facilitate the proposed approaches and to perform therequired calculations. The DSS was developed in light of the research findingsregarding the reasons of low take-up of the existing tools. Four validation case studieswere conducted. Senior managers in separate British construction companies tested thetool and found it useful, helpful and easy to use.It is concluded that the proposed risk model, risk assessment methodology and projectevaluation framework could be viable alternatives to the existing ones. Professionalexperience was modelled and utilised systematically for risk and benefit analysis. Thismay help closing the gap between theory and practice of risk analysis and decisionmaking in construction. The research findings recommend further exploration of thepotential applications of DST and ER in construction management domain.

Decision making study has been the multi-disciplinary research involving operations researchers, management scientists, statisticians, mathematical psychologists and economists as well as others. This study aims to investigate the theory and methodology of decision making research and apply them to different contexts in real cases. The study has reviewed the literature of Multiple Criteria Decision Making (MCDM), Evidential Reasoning (ER) approach, Naturalistic Decision Making (NDM) movement, Social Judgment Theory (SJT), and Adaptive Toolbox (AT) program. On the basis of these literatures, two methods, Evidence-based Trade-Off (EBTO) and Judgment Analysis with Heuristic Modelling (JA-HM), have been proposed and developed to accomplish decision making problems under different conditions. In the EBTO method, we propose a novel framework to aid people s decision making under uncertainty and imprecise goal. Under the framework, the imprecise goal is objectively modelled through an analytical structure, and is independent of the task requirement; the task requirement is specified by the trade-off strategy among criteria of the analytical structure through an importance weighting process, and is subject to the requirement change of a particular decision making task; the evidence available, that could contribute to the evaluation of general performance of the decision alternatives, are formulated with belief structures which are capable of capturing various format of uncertainties that arise from the absence of data, incomplete information and subjective judgments. The EBTO method was further applied in a case study of Soldier system decision making. The application has demonstrated that EBTO, as a tool, is able to provide a holistic analysis regarding the requirements of Soldier missions, the physical conditions of Soldiers, and the capability of their equipment and weapon systems, which is critical in domain. By drawing the cross-disciplinary literature from NDM and AT, the JA-HM extended the traditional Judgment Analysis (JA) method, through a number of novel methodological procedures, to account for the unique features of decision making tasks under extreme time pressure and dynamic shifting situations. These novel methodological procedures include, the notion of decision point to deconstruct the dynamic shifting situations in a way that decision problem could be identified and formulated; the classification of routine and non-routine problems, and associated data alignment process to enable meaningful decision data analysis across different decision makers (DMs); the notion of composite cue to account for the DMs iterative process of information perception and comprehension in dynamic task environment; the application of computational models of heuristics to account for the time constraints and process dynamics of DMs decision making process; and the application of cross-validation process to enable the methodological principle of competitive testing of decision models. The JA-HM was further applied in a case study of fire emergency decision making. The application has been the first behavioural test of the validity of the computational models of heuristics, in predicting the DMs decision making during fire emergency response. It has also been the first behavioural test of the validity of the non-compensatory heuristics in predicting the DMs decisions on ranking task. The findings identified extend the literature of AT and NDM, and have implications for the fire emergency decision making.

ERP system has shaped the way modern organisations design, control, and execute business processes. It has not only paved the way for efficient use of organisational resources but also offered the opportunity to utilise data logged in the system for ensuring internal control. The key contribution of this research is that it has resulted in a method which can practically be employed by internal auditors for measuring internal fraud risk of business organisations with ERP systems, by utilising process mining technique and evidential reasoning in the form of Bayesian theorem, in a much more effective way compared to conventional frequentist method. The other significant contribution is that it has paved the way for combining process mining technique and evidential reasoning in addressing problems prevalent within organisational contexts. This research has contributed in developing IS theories for design and action especially in the area of soft systems methodology as it has relied on business process modelling in addressing the issue of internal fraud risk. The chosen method has contributed in facilitating incorporation of design science method in problem solving. Researchers have focused on applying data mining techniques within organisational contexts for extracting valuable information. Process mining is a comparatively new technique which allows business processes to be analysed based on event logs. Analysis of business processes can be useful for organisations not only for attaining greater efficiency but also for ensuring internal control inside the organisation. Large organisations have various measures in place for ensuring internal control. Measuring the risk of fraud within a business process is an important practice for preventing fraud as accurate measurement of fraud risk provides business experts with the opportunity to comprehend the extent of the problem. Business experts, such as internal auditors, still heavily rely upon conventional methods for measuring internal fraud risk way by of random check of process compliance. Organisations with ERP systems in place can avail themselves of the opportunity to use event logs for extending the scope of assessing process conformance. This has not been put into practice as there is a lack of well researched methods which can allow event logs to be utilised for enhancing internal control. This research can be proved to be useful for practitioners as it has developed a method for measuring internal fraud risk within organisations. This research aimed to utilise process mining technique that allows business experts to exert greater control over business process execution by allowing the internal fraud risk to be measured effectively. A method has been developed for measuring internal fraud risk of business originations with ERP systems by using process mining and Bayesian theorem. In this method, rate of process deviation is calculated by conducting process mining on relevant logs of events and then that process deviation rate is applied in Bayesian theorem along with historic internal fraud risk rate and process deviation rate calculated manually for arriving at a revised internal fraud risk rate. Bayesian theorem has been relied upon for the purpose of developing this new method as it allows evidential reasoning to be incorporated. The method has been developed as a Design Science Research Method (DSRM) artefact by conducting three case-studies. Data has been collected from three case companies, operating in readymade garments manufacturing industry, pharmaceuticals industry, and aviation industry, regarding their procurement process for conducting process mining. The revised internal fraud risk rates were then evaluated by considering the feedback received from respective business experts of each of the case company. The proposed method is beneficial as it has paved the way for practitioners to utilise process mining using a soft system methodology. The developed method is of immense significance as it has contributed in the field of business intelligence and analytics (BI&A) and the big data analytics which have become significantly important to both academics and practitioners over the past couple of decades.

The aim of this thesis is to explore the adaptability of the Evidential Reasoning (ER) Rule as a method to provide a useful supporting tool for helping investors make decisions on financial investments. Decision making in financial investment often involves conflicting information and subjective judgment of the investors. Accordingly, the ER Rule, extended from the original popular Evidential Reasoning algorithm and developed for MCDM (Multiple Criteria Decision Making), is particularly suited for handling conflicts in information and to allow for judgmental weighting on the sources of evidence. In order to do so, a specific EIA (Efficient Information Assessment) process modeled by the mass function of Dempster-Shafer Theory has been constructed such that the underlying architecture of the model satisfies the requirement of the ER rule. The fundamental concern is to define and assess “efficient information”. For this purpose, a process denoted the Efficient Information Assessment (EIA) is defined which applies the mass function of Dempster-Shafer theory. Any relevant information selected from an expert’s knowledge database is “efficient” if the data is fully in compliance with the requirement of the ER rule. The logical process of the EIA model proceeds with a set of portfolio strategies from the information recommended by top financial analysts. Then, as a result, the model enables the ER rule to make an evaluation of all strategies for helping investors make decisions. Experiments were carried out to back-test the investment strategy using data from the China Stock Market & Accounting Research (CSMAR) Database for the four-year period between 2009 and 2012. The data contained more than 270,000 reports from more than 4,600 financial analysts. The risk-adjusted average annual return of the strategy outperformed that of the CSI300 index by as much as 10.69% for an investment horizon of six months, with the p value from Student’s t-test as low as 0.02%. The EIA model serves as the first successful application adapting the ER Rule for a new and effective decision-making process in financial investment, and this work is the only empirical study applying the ER Rule to the opinions of financial analysts, to the best of my knowledge.

The real estate selection process might be regarded as a typical Multi-Criteria Decision Making (MCDM) problem. With current literature concentrating predominantly on institutional investment decision making, additional effort should be directed towards studying inexperienced homebuyers who want to buy a property in which to live. In this context, authors have described the decision environment as a complex decision process with restricted access to property data, high financial burdens partially due to the illiquid nature of the investment, the unfamiliarity with the decision task and low transparency in information aggregation. Consequently, this situation could benefit from a more structured approach that assists homebuyers in their actions. In order to guide the decision making process and provide a suitable support mechanism, it is necessary to first structure the problem and extract the required information. A thorough literature review shows that little guidance is available for MCDM problem structuring. Consequently, this research first proposes an MCDM problem-structuring framework to decompose complex problems into smaller parts. Foremost, the application is intended for high-involvement consumer products and services. This framework is derived from MCDM and methodology literature, where the former provides the elements that need to be defined in any MCDM problem situation, and the latter suggests suitable data collection and analysis methods to obtain the information. As a result, the first contribution to existing literature is the introduction of an MCDM problem-structuring framework, which consists of a carefully designed sequential exploratory mixed method procedure. Next, following the proposed structure, the real estate selection problem in Majorca (Spain) is defined. Whilst providing the inherent problem elements and establishing a comprehensive list of evaluation criteria to assess luxury properties, the fieldwork also offers behavioural insights, contributing and supplementing existing real estate research. In particular, major misunderstandings and false assumptions during real estate agent and client interactions are observed, stressing the need to optimise communication and targeting strategies. On the basis of the relevant real estate evaluation criteria, a dataset of alternative houses is created and subsequently rated by prospective luxury-homebuyers. This provides the basis for the third research focus, the construction of a decision support model for real estate selection. In accordance to the problem features and model requirements, the Evidential Reasoning (ER) rule is identified to offer a powerful and transparent evidence aggregation process, with the potential to have a superior performance than other methods in addressing the selection decision. Due to the ER rule’s short history (2013), application studies in general are practically non-existing and unprecedented in the real estate domain. Therefore, the use of a modified ER model can provide the real estate literature with a prescriptive multi-criteria decision support mechanism, whilst simultaneously offering an application study for the MCDM community and other relevant decision analysis domains. In closing, modelling a real problem using the ER rule highlights the method’s advantages and might in turn increase awareness, leading to more applications.