Neuro-fuzzy risk prediction model for computational grids

Prediction of risk assessment is demanding because it is one of the most important contributory factors towards grid computing. Hence, researchers were motivated for developing and deploying grids on diverse computers, which is responsible for spreading resources across administrative domains so that resource sharing becomes effective. Risk assessment in grid computing can analyze possible risks, that is, the risk of growing computational requirements of an organization. Thus, risk assessment helps in determining these risks. In this, we present an adaptive neuro-fuzzy inference system that can predict the risk environment. The main goal of this paper is to obtain empirical results with an illustration of high performance and accurate results. We used data mining tools to determine the contributing attributes to obtain the risk prediction accurately

A risk management system for healthcare facilities service operators

The 24-hour post-modern society in which the NHS delivers healthcare today in the UK as a business has resulted in purchasers and providers of non clinical/FM services continuing to face more and more service delivery and operational risks (Payne and Rees, 1999). These business risks are mainly caused by uncertainties in customer supply and demand service chain, limited support resources (human, capital, modern healthcare facilities and information technology) and the dynamic NHS service scape (environment). This has resulted in non clinical service decisions being reached in an ad-hoc manner and often with no effective business strategy. Furthermore, this approach has led to disastrous business planning and caring consequences, particularly in a highly politicised and consumer-sensitive environment like healthcare service provision (Wagstaff, 1997). These risks are also mainly attributed to the apparent lack of best practice guidelines that are available to assist FM service operators in identifying and managing non-clinical service operations effectively. In addition, there is evidence from NHS literature that clearly indicates the lack of best practice models for managing business risks associated with hotel, estates and site (non-clinical/FM) services delivery (Okoroh et al., 2000; DoH, 1999; CFM, 1993; Smith, 1997; Featherstone, 1999; HFN 17,1998). To date, no research has been carried out in the NHS using FM service operators' (domain experts) knowledge to develop an integrated risk management system for managing non-clinical services using modern business approaches. This thesis presents research findings from healthcare executives and FM experts on business risks faced by service operators (purchasers and providers) when managing non- clinical services effectively in the UK NHS. The research methodology used were, a detailed analysis of a best practice hospital case study, structured interviews with domain healthcare FM experts, pilot and major questionnaire surveys and Repertory Grid interviews. The research has established that in managing non clinical/FM services in the NHS, there are seven major common management-related risk classes identified as critical; customer care; financial and economic; commercial; legal; facility-transmitted; business transfer and corporate. Further research using second factor analysis established that these classical non-clinical risk factors could further be subdivided into forty-eight (48) constructs/sub-attributes highly rated by healthcare facilities executives. Using these risks factors and sub-attributes the research has developed a decision support system for risk management that can be used by FM operators to manage business risks in NHS trust hospitals

Resilience-Building Technologies: State of Knowledge -- ReSIST NoE Deliverable D12

This document is the first product of work package WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellenc

Model driven certification of Cloud service security based on continuous monitoring

Cloud Computing technology offers an advanced approach for the provision of infrastructure, platform and software services without the need of extensive cost of owning, operating or maintaining the computational infrastructures required. However, despite being cost effective, this technology has raised concerns regarding the security, privacy and compliance of data or services offered through cloud systems. This is mainly due to the lack of transparency of services to the consumers, or due to the fact that service providers are unwilling to take full responsibility for the security of services that they offer through cloud systems, and accept liability for security breaches [18]. In such circumstances, there is a trust deficiency that needs to be addressed. The potential of certification as a means of addressing the lack of trust regarding the security of different types of services, including the cloud, has been widely recognised [149]. However, the recognition of this potential has not led to a wide adoption, as it was expected. The reason could be that certification has traditionally been carried out through standards and certification schemes (e.g., ISO27001 [149], ISO27002 [149] and Common Criteria [65]), which involve predominantly manual systems for security auditing, testing and inspection processes. Such processes tend to be lengthy and have a significant financial cost, which often prevents small technology vendors from adopting it [87]. In this thesis, we present an automated approach for cloud service certification, where the evidence is gathered through continuous monitoring. This approach can be used to: (a) define and execute automatically certification models, to continuously acquire and analyse evidence regarding the provision of services on cloud infrastructures through continuous monitoring; (b) use this evidence to assess whether the provision is compliant with required security properties; and (c) generate and manage digital certificates to confirm the compliance of services with specific security properties

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios