A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system.support decision system, diagnosis, multi-agent system, fish diseases

Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

IRS II: a framework and infrastructure for semantic web services

In this paper we describe IRS–II (Internet Reasoning Service) a framework and implemented infrastructure, whose main goal is to support the publication, location, composition and execution of heterogeneous web services, augmented with semantic descriptions of their functionalities. IRS–II has three main classes of features which distinguish it from other work on semantic web services. Firstly, it supports one-click publishing of standalone software: IRS–II automatically creates the appropriate wrappers, given pointers to the standalone code. Secondly, it explicitly distinguishes between tasks (what to do) and methods (how to achieve tasks) and as a result supports capability-driven service invocation; flexible mappings between services and problem specifications; and dynamic, knowledge-based service selection. Finally, IRS–II services are web service compatible – standard web services can be trivially published through the IRS–II and any IRS–II service automatically appears as a standard web service to other web service infrastructures. In the paper we illustrate the main functionalities of IRS–II through a scenario involving a distributed application in the healthcare domain

Data management of on-line partial discharge monitoring using wireless sensor nodes integrated with a multi-agent system

On-line partial discharge monitoring has been the subject of significant research in previous years but little work has been carried out with regard to the management of on-site data. To date, on-line partial discharge monitoring within a substation has only been concerned with single plant items, so the data management problem has been minimal. As the age of plant equipment increases, so does the need for condition monitoring to ensure maximum lifespan. This paper presents an approach to the management of partial discharge data through the use of embedded monitoring techniques running on wireless sensor nodes. This method is illustrated by a case study on partial discharge monitoring data from an ageing HVDC reactor

The Integration of Positron Emission Tomography With Magnetic Resonance Imaging

A number of laboratories and companies are currently exploring the development of integrated imaging systems for magnetic resonance imaging (MRI) and positron emission tomography (PET). Scanners for both preclinical and human research applications are being pursued. In contrast to the widely distributed and now quite mature PET/computed tomography technology, most PET/MRI designs allow for simultaneous rather than sequential acquisition of PET and MRI data. While this offers the possibility of novel imaging strategies, it also creates considerable challenges for acquiring artifact-free images from both modalities. This paper discusses the motivation for developing combined PET/MRI technology, outlines the obstacles in realizing such an integrated instrument, and presents recent progress in the development of both the instrumentation and of novel imaging agents for combined PET/MRI studies. The performance of the first-generation PET/MRI systems is described. Finally, a range of possible biomedical applications for PET/MRI are outlined

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version