Search

In the real world encountering with noisy and corrupted data is unavoidable. Auto industry sector (AIS) as a one of the significant industry encounters with noisy and corrupted data regarding to its rapid development. Therefore, developing the performance assessment in this situation is so helpful for this industry. As Data envelopment Analysis (DEA) could not deal with noisy and corrupted data, the alternative method(s) is very important. As one of excellent and promising feature of artificial neural networks (ANNs) are theirs flexibility and robustness in noisy situation, they are a good alternative. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques for efficiency assessment in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores of auto industry in various countries, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of AIS on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Another feature of proposed algorithm is its ability to calculate efficiency for multiple outputs. An example using real data is presented for illustrative purposes. In the application to the auto industries, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. To test the robustness of the efficiency results of the proposed method, the ability of proposed ANN algorithm in dealing with noisy and corrupted data is compared with Data Envelopment Analy

Current diagnosis, treatment and healthcare delivery processes in Australia are dominated by long established westernized clinically driven methods of patient-practitioner interaction. Consequently this dominant healthcare provider influence contributes to risk of miscommunication, misinformation in patient records and reciprocal misunderstandings that go unrecognised as such. For Indigenous communities, inadequate health literacy (HL) and a pervasive semantic disconnect are major barriers. Overcoming these barriers in the primary care setting presents opportunities to deliver appropriate timely and more effective care. We propose an e-health framework that enhances the Patient-Practitioner Interview Encounter (PPIE) through the use of a patient-centric linguistic interface using semantic mappings between Aboriginal English (AE) and Standard Australian English (SAE). This will ameliorate communications and interactions, so meeting the needs of all stakeholders (Patients, Physicians, Nurses, Allied Health Professionals and their Non-Critical Carers) engaged in Indigenous patient-centric primary care. It provides healthcare practitioners and their Indigenous T2DM patients with a new platform for two-way educative sharing and knowledge exchange that will increase mutually productive treatment, care and management expectations.

The global Type 2 Diabetes Mellitus (T2DM) epidemic imposes a heavy burden on communities that are ethnically vulnerable to the disease and further disadvantaged by socio-economic circumstance and cultural communications barriers. Aboriginal communities in rural and remote Western Australia are representative of these high-riskgroups. Indigenous patients needing continuous management of T2DM are also experiencing disproportionate risk of co-morbidities and hospitalizations compared with nonindigenous patients. Type 2 Diabetes Mellitus (T2DM) is often described as ’the lifestyle disease’. Within clinical care and patient quality of life management domains, T2DM presents both the healthcare practitioner and the patient with a mosaic of complexities.Information processing demands for self-management of diabetes are extensive, requiring constant self monitoring and assessment of the illness state in order to apply per instance and per condition the most appropriate form of control. In this work we introduce a primary care communications concept tool centered upon optimization of the Patient-Practitioner Interview Encounter (PPIE). The target beneficiary is the Aboriginal T2DM patient living in Western Australia. Avital part of our design effort is therefore dedicated to understanding and responding to the cultural domain barriers, challenges and opportunities of this specific health care environment.

Abnormal plasma lipoprotein levels have been found to be significantly correlated to cardiovascular disease, the leading cause of mortality and morbidity worldwide. In addition, lipoprotein dysregulation, known as dyslipidemia, is a central feature in disease states such as diabetes and hypertension, which also increases the risk to cardiovascular disease. Despite progress in lipoprotein research, a vast number of the world population suffers from dyslipidemia. One of the major challenges that researchers face is the difficulties in accessing and integrating relevant information amidst massive quantities of heterogeneous data. Ontologies target these problems by providing a semantic framework of the concepts involved in a system of related instances to support systematic querying of information, data mining, as well as form the basis for collaboration between research teams. Lipoprotein Ontology will provide the basis for the design of various applications to enable interoperability between research groups or software agents, as well as the development of tools for the diagnosis and treatment of dyslipidemia. In this paper we present a nine-step methodology for the design of Lipoprotein Ontology. This methodology can be adapted for use in the design of other domain-specific ontologies.

Sexual dysfunction induces distress, strongly affects all aspects of life, and results in poor mental health.Preliminary research studies have provided some evidence on the relationship between sexuality and mental health. Nevertheless, a number of research issues are hindering effective progress in this research arena. Computer-based technologies could be applied to address the existing research issues and obtain new knowledge in the field. Applying advanced computerbased techniques such as data mining to analyze the collected data will help gain valuable knowledge. Such knowledge could be used in development of better sexual education programs, better counseling approaches and better therapies.

This study was designed to investigate the hemodynamics in the left coronary artery with the aim of identifying the relationship between angulations of left coronary bifurcation and development of atherosclerosis. Six 3D left coronary models were simulated for computational fluid dynamic analysis. The left coronary model was composed of left main stem, left anterior descending and left circumflex branches. The angulations at the left bifurcation weresimulated with angles ranging from 90°, 75°, 60°, 45°, 30° to 15°. The computational fluid dynamic was used for analysis of flow velocity, wall pressure and wall shear stress. Our results showed that apparent low shear stress and high wall pressure was noticed at the left coronary bifurcation regions with wide angled models. Flow pattern was also changed with angled becoming wide in the simulated models. Our analysis shows direction relationship between coronary angulation and development of atherosclerosis. Future studies are required to perform computational fluid dynamic analysis in coronary models from patients' data with different degree of coronary stenosis.

In this paper we introduce an ontology specifically designed for product innovation. We discuss the specifics and advantages of domain ontology construction for product innovation. We then look at the application ofthe framework to the scuba diving equipment manufacturing industry domain.

Cloud computing has enabled users to access various resources and applications as a service and in return pay the provider only for the time for which they are used. Service Level Agreements (SLA) are formed between the user and provider to ensure that the required services and applications are delivered as expected. With the increase of public cloud providers, challenges such as availability, reliability, security, privacy and transactional risk demand detail assessment while forming SLAs. This paper focuses on one sub-category of transactional risk while forming SLAs; namely performance risk. We argue that performance risk assessment should be done by the user before entering into a SLA with a service provider. We propose to measure performance risk according to the specific context and assessment criteria with the aid of a semantic similarity model for the SLA requirement being negotiated in a cloud computing environment. We show through simulations that the performance risk analysis is more accurate using semantic similarity matching compared with risk analysis without semantic similarity matching.

This paper presents a proposal for the development of an ontology evolution strategy which refines ontological relations in scientific ontologies. In addition to experts’ consensus, it is desirable to define ontological relations between any two concepts in a scientific ontology based on scientific evidence. To address this issue, we can relate ontological relations to different research results obtained from various studies. To implement this solution, our envisaged evidence/discovery-based methodology integrates a higher-level ontology (systematic review ontology) into a systematic review agent which employs a Fuzzy Inference System in order to automatically modifyontological relations of a domain ontology based on the evidence received from information resources. The evidence/discovery-based methodology will further use the domain ontology to discover novel connections between distinct literatures, thereby, enrich its conceptualization.

The ontology layer of the semantic web is now mature enough (i.e. standards like RDF, RDFs, OWL, OWL 2) and the next step is to work on a logic layer for the development of advanced reasoning capabilities for knowledge extraction and efficient decision making. Adding logic to the web means using rules to make inferences. Rules are a means of expressing business processes, policies, contracts etc but most of the studies have focused on the use of monotonic logics in layered development of the semantic web which provides no mechanism for representing or handling incomplete or contradictory information respectively. This paper discusses argumentation, semantic web and defeasible logic programming with their distinct features and identifies the different research issues that need to be addressed in order to realize defeasible argumentative reasoning in the semantic web applications.

Our research focus is the implementation of agent-based systems within the health domain, more specifically, in the study of total wellbeing. We use an evidence-based total wellbeing ontological model where the total well-being is seen as a function of physical health, mental health, emotions, relation-ships, financial situation and spirituality. We use the TICSA methodology to design a multi-agent system. This multi-agent system is based on the Total Wellbeing Ontology and helps intelligent retrieval, management and analysis of information related to total wellbeing. We hope this system to expose evidence that will support general public in managing their personal wellbeing better, and health professionals in adapting their services to address patients needs more systematically and effectively.

Ontology plays a vital role in sharing a common understanding of the domain among groups of people and provides terminology interpretable by machines. Recently, ontology has grown and continued to evolve constantly, but there are not many tools to provide an environment to support ontology evolution. This paper introduces a framework to support the management and maintenance leading to the evolution of Ontology by focusing on Software Engineering Ontology. The proposed framework will take into account the users' perspectives on the ontology and keep track of the comments in a formal manner. We propose the use of technology such as Semantic Media Wiki as a means to overcome the aforementioned problems.

With the rapid growth of the amount of information available in the Web, webpage classification technologies arewidely employed by many search engines in order to formulate user queries and make users? search tasks easier. Knowledge Grid is a new form of Web environment, in which a Resource Space Model is employed in order to classify available semantic documents within the Web environment. However, it is well known that the semantic documents are proportionally small in relation to the whole Web documents, and the Resource SpaceModel cannot process these Web documents without semantic supports. In order to solve the above issue, in this paper, we present a novel ontology-based webpage classification method for the Knowledge Grid environment, which utilizes generated metadata from webpages as the intermedium to classify the webpages by ontology concepts. We design a conceptual model of a Webpage Classification Agent and build the prototype in a chosen domain. A series of experiments have been conducted using the prototype in order to evaluate the conceptual model. Conclusions about the evaluation are drawn in the final section.

In this paper, we present a hybrid concept similarity measure model for the ontology environment. Whilst to date many similar technologies have been developed for semantic networks, few of them can be directly applied to the semantic-rich ontology environment. Before the measure model is adopted, an ontology is required to be converted into a lightweight ontology space, and within it all the ontology concepts need to be transformed into the pseudo concepts. By means of this model, ontology concept similarities are measured respectively based on the content of pseudo-concepts and the structure of the lightweight ontology space. Afterwards, the two aspects of concept similarity are leveraged as the eventual product. In addition, an experiment is conducted to evaluate the measure model based on a small ontology. Conclusions are drawn and future works are planned in the final section.

We have developed an evidence-based mental health ontological model that represents mental health in multiple dimensions. The ongoing addition of new mental health knowledge requires a continual update of the Mental Health Ontology. In this paper, we describe how the ontology evolution can be realized using a multi-agent system in combination with data mining algorithms. We use the TICSA methodology to design this multi-agent system which is composed of four different types of agents: Information agent, Data Warehouse agent, Data Mining agents and Ontology agent. We use UML 2.1 sequence diagrams to model the collaborative nature of the agents and a UML 2.1 composite structure diagram to model the structure of individual agents. The Mental Heath Ontology has the potential to underpin various mental health research experiments of a collaborative nature which are greatly needed in times of increasing mental distress and illness.

The success of the semantic web relies heavily on ontologies. However, using ontologies for this specific area poses a number of new problems. One of these problems, extracting a high quality ontology from a given base ontology, is currently receiving increasing attention. Areas such as versioning, distribution and maintenance of ontologies often involve this problem. Here, a formalism is presented that enables grouping ontology extraction requirements into different categories, called optimization schemes. These optimization schemes provide a way to introduce quality in the extraction process. An overview of the formalism is discussed, as well as a demonstration of several example optimization schemes. Each of these optimization schemes meets a certain requirement, and consists of rules and algorithms. Examples of how the formalism is deployed to reach a high-quality result, called a materialized ontology view, are covered. The presented methodology provides a foundation for further developments, and shows the possibility of obtaining usable ontologies in a highly automated way.