Fuzzy expert systems in civil engineering

Imperial Users onl

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

This book aims to provide state-of-the-art information on computer architecture and simulation in industry, engineering, and clinical scenarios. Accepted submissions are high in scientific value and provide a significant contribution to computer architecture. Each submission expands upon novel and innovative research where the methods, analysis, and conclusions are robust and of the highest standard. This book is a valuable resource for researchers, students, non-governmental organizations, and key decision-makers involved in earthquake disaster management systems at the national, regional, and local levels

A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice

Algebraic Structures of Neutrosophic Triplets, Neutrosophic Duplets, or Neutrosophic Multisets

Neutrosophy (1995) is a new branch of philosophy that studies triads of the form (, , ), where is an entity {i.e. element, concept, idea, theory, logical proposition, etc.}, is the opposite of , while is the neutral (or indeterminate) between them, i.e., neither nor .Based on neutrosophy, the neutrosophic triplets were founded, which have a similar form (x, neut(x), anti(x)), that satisfy several axioms, for each element x in a given set.This collective book presents original research papers by many neutrosophic researchers from around the world, that report on the state-of-the-art and recent advancements of neutrosophic triplets, neutrosophic duplets, neutrosophic multisets and their algebraic structures – that have been defined recently in 2016 but have gained interest from world researchers. Connections between classical algebraic structures and neutrosophic triplet / duplet / multiset structures are also studied. And numerous neutrosophic applications in various fields, such as: multi-criteria decision making, image segmentation, medical diagnosis, fault diagnosis, clustering data, neutrosophic probability, human resource management, strategic planning, forecasting model, multi-granulation, supplier selection problems, typhoon disaster evaluation, skin lesson detection, mining algorithm for big data analysis, etc

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Biomedical applications of belief networks

Biomedicine is an area in which computers have long been expected to play a significant role. Although many of the early claims have proved unrealistic, computers are gradually becoming accepted in the biomedical, clinical and research environment. Within these application areas, expert systems appear to have met with the most resistance, especially when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is necessary to provide the information needed to make rational judgements concerning the inferences the system has made. This entails an explanation of what inferences were made, how the inferences were made and how the results of the inference are to be interpreted. Furthermore there must be a consistent approach to the combining of information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses. Until recently ad hoc formalisms were seen as the only tractable approach to reasoning under uncertainty. A review of some of these formalisms suggests that they are less than ideal for the purposes of decision making. Belief networks provide a tractable way of utilising probability theory as an inference formalism by combining the theoretical consistency of probability for inference and decision making, with the ability to use the knowledge of domain experts.nowledge of domain experts. The potential of belief networks in biomedical applications has already been recog¬ nised and there has been substantial research into the use of belief networks for medical diagnosis and methods for handling large, interconnected networks. In this thesis the use of belief networks is extended to include detailed image model matching to show how, in principle, feature measurement can be undertaken in a fully probabilistic way. The belief networks employed are usually cyclic and have strong influences between adjacent nodes, so new techniques for probabilistic updating based on a model of the matching process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used to apply the belief network formalism to two application domains. The first application is model-based matching in fetal ultrasound images. The imaging modality and biological variation in the subject make model matching a highly uncertain process. A dynamic, deformable model, similar to active contour models, is used. A belief network combines constraints derived from local evidence in the image, with global constraints derived from trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of evidence occurring during the classification of objects on a cervical smear slide as part of an automated pre-screening system. A belief network provides both an explicit domain model and a mechanism for the incremental aggregation of evidence, two attributes important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features required of a decision support system with desirable qualitative features that will lead to improved acceptability of expert systems in the biomedical domain

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic