EPC Global-Based Document Tracing System Using CBR and Fuzzy Decision Tree for TTQS

In the document management, theinformation found by users can be consideredseparately to the work productivity and decreasetime of searching to the related database. Documentmanagement model not only recommends whatdocument user needed but tell user the location ofdocuments. This paper will propose a documentstracing system approach to combine EPCGlobalstandard architecture and case-based reasoning(CBR) method that get documents informationimmediately. This approach produce customizedrecommendation with fuzzy rules on using recordsor documents characteristics and the documentrelations regulated in TTQS (Taiwan TrainQualiSystem) standard so that it helps collaborativeoperation among organizations and can tracedocument management status

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

[EN] This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.This work was supported in part by the EU's H 2020 research and innovation programme under the Marie SklodowskaCurie Grant Agreement 641794 (project DREAM-GO) and Grant Agreement 703689 (project ADAPT), in part by the FEDER Funds through COMPETE program, and in part by the National Funds through FCT under the Project UID/EEA/00760/2013. (Corresponding author: Tiago Pinto.)info:eu-repo/semantics/publishedVersio

An intelligent decision support system for machine learning algorithms recommendation

Machine learning is a very central topic in Artificial Intelligence and even computer science in general. Nowadays, its use in Big Data problems is quite well known. However, while the big data, and machine learning problems in general, are quite varied and in needing of different kinds of solutions, there are as well many different methods in machine learning that can be used. In this work, we propose an application that might help deciding on which machine learning methods a user needs for a specified problem. The application is an Intelligent Decision Support System for Machine Learning Algorithm Recommendation for which we present the design, which is centered around the combined use of the Case-Based Reasoning and RuleBased Reasoning, for the recommending process, while also trying to make the system easy to use and manage. We present a prototype of such a system, and the implementation details of the two recommender algorithms. The preliminary testing of the prototype shows it to be a promising tool

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

BMR: Benchmarking Metrics Recommender for personnel issues in software development proyects

This paper presents an architecture which applies document similarity measures to the documentation produced during the phases of software development in order to generate recommendations of process and people metrics for similar projects. The application makes a judgment of similarity of the Service Provision Offer (SPO) document of a new proposed project to a collection of Project History Documents (PHD), stored in a repository of unstructured texts. The process is carried out in three stages: firstly, clustering of the Offer document with the set of PHDs which are most similar to it; this provides the initial indication of whether similar previous projects exist, and signifies similarity. Secondly, determination of which PHD in the set is most comparable with the Offer document, based on various parameters: project effort, project duration (time), project resources (members/size of team), costs, and sector(s) involved, indicating comparability of projects. The comparable parameters are extracted using the GATE Natural Language Processing architecture. Lastly, a recommendation of metrics for the new project is made, which is based on the transferability of the metrics of the most similar and comparable PHD extracted, here referred to as recommendation.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI- 020100-2008-564) and SONAR2 (TSI-020100-2008- 665) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140- C03-02).Publicad

Manufacturing processes in the textile industry. Expert Systems for fabrics production

 The textile industry is characterized by the economic activity whose objective is the production of fibres, yarns, fabrics, clothing and textile goods for home and decoration, as well as technical and industrial purposes. Within manufacturing, the Textile is one of the oldest and most complex sectors which includes a large number of sub-sectors covering the entire production cycle, from raw materials and intermediate products, to the production of final products. Textile industry activities present different subdivisions,each with its own traits. The length of the textile process and the variety of its technicalprocesses lead to the coexistence of different sub-sectors in regards to their business structure and integration. The textile industry is developing expert systems applicationsto increase production, improve quality and reduce costs. The analysis of textile designs or structures includes the use of mathematical models to simulate the behavior of the textile structures (yarns, fabrics and knitting). The Finite Element Method (FEM) has largely facilitated the prediction of the behavior of that textile structure under mechanical loads. For classification problems Artificial Neural Networks (ANNs) have proved to be a very effective tool as a quick and accurate solution. The Case-Based Reasoning (CBR) method proposed in this study complements the results of the finite element simulation, mathematical modeling and neural networks methods

Strategies for online personalised nutrition advice employed in the development of the eNutri web app

The internet has considerable potential to improve health-related food choice at low-cost. Online solutions in this field can be deployed quickly and at very low cost, especially if they are not dependent on bespoke devices or offline processes such as the provision and analysis of biological samples. One key challenge is the automated delivery of personalised dietary advice in a replicable, scalable and inexpensive way, using valid nutrition assessment methods and effective recommendations. We have developed a web-based personalised nutrition system (eNutri) which assesses dietary intake using a validated graphical FFQ and provides personalised food-based dietary advice automatically. Its effectiveness was evaluated during an online randomised controlled trial dietary intervention (EatWellUK study) in which personalised dietary advice was compared with general population recommendations (control) delivered online. The present paper presents a review of literature relevant to this work, and describes the strategies used during the development of the eNutri app. Its design and source code have been made publicly available under a permissive open source license, so that other researchers and organisations can benefit from this work. In a context where personalised diet advice has great potential for health promotion and disease prevention at-scale and yet is not currently being offered in the most popular mobile apps, the strategies and approaches described in the present paper can help to inform and advance the design and development of technologies for personalised nutrition

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers