Dissertations / Theses: 'Intelligence decision support system'

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Subramanian, Saravanan. "ANNTAX - an artificial intelligence based decision support system." Thesis, University of Surrey, 2005. http://epubs.surrey.ac.uk/2156/.

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Руденко, Максим Сергійович, Максим Сергеевич Руденко, and Maksym Serhiiovych Rudenko. "Intelligence decision support system for diagnostic oncological diseases." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/28793.

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Chin, Shou-fong. "Multi-agent as a decision support system." Thesis, Imperial College London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287526.

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Forghani, Morteza Seyed. "The likelihood of success in management intelligence systems : building a consultant advisory system." Thesis, Loughborough University, 1989. https://dspace.lboro.ac.uk/2134/6845.

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Management Intelligence Systems are a class- of Decision Support Systems aimed at providing intelligence about an ill-structured decision to a decision-maker. The research objective was to build a 'Consultant Advisory System', a computerised model of success, to assist internal consultants in, assessing the likelihood of success for a Management Intelligence System (MINTS). The system would also be capable of allowing the consultant to identify reasons which might lead to a low likelihood of success, so that corrective action can be taken. The approach taken is different from many other studies which have concentrated on the success of a computer-based information system after implementation, rather than assessing success throughout the whole process of initiating, developing and implementing such systems. The research has been based on a detailed survey of the literature on Management Information systems (MIS), and Decision Support Systems (DSS) and 39 field investigations involving detailed interviews with the key actors involved in a MINTS project. Two phases of MINTS development were identified: (A) ensuring a right environment and (B) maintaining relationships. About 280 factors were distilled as significant for the successful development of a MINTS and these have been incorporated in a computerised advisor. Validation of MINTS in general and the advisor in particular is discussed in detail.

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Sandhu, Raghbir Singh. "Intelligent spatial decision support systems." Thesis, University College London (University of London), 1998. http://discovery.ucl.ac.uk/1317911/.

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This thesis investigates the conceptual and methodological issues for the development of Intelligent Spatial Decision Support Systems (ISDSS). These are spatial decision support systems (SDSS) integrating intelligent systems techniques (Genetic Algorithms, Neural Networks, Expert Systems, Fuzzy Logic and Nonlinear methods) with traditional modelling and statistical methods for the analysis of spatial problems. The principal aim of this work is to verify the feasibility of heterogeneous systems for spatial decision support derived from a combination of traditional numerical techniques and intelligent techniques in order to provide superior performance and functionality to that achieved through the use of traditional methods alone. This thesis is composed of four distinct sections: (i) a taxonomy covering the employment of intelligent systems techniques in specific applications of geographical information systems and SDSS; (ii) the development of a prototype ISDSS; (iii) application of the prototype ISDSS to modelling the spatiotemporal dynamics of high technology industry in the South-East of England; and (iv) the development of ISDSS architectures utilising interapplication communication techniques. Existing approaches for implementing modelling tools within SDSS and GIS generally fall into one of two schemes - loose coupling or tight coupling - both of which involve a tradeoff between generality and speed of data interchange. In addition, these schemes offer little use of distributed processing resources. A prototype ISDSS was developed in collaboration with KPMG Peat Marwick's High Technology Practice as a general purpose spatiotemporal analysis tool with particular regard to modelling high technology industry. The GeoAnalyser system furnishes the user with animation and time plotting tools for observing spatiotemporal dynamics; such tools are typically not found in existing SDSS or GIS. Furthermore, GeoAnalyser employs the client/server model of distributed computing to link the front end client application with the back end modelling component contained within the server application. GeoAnalyser demonstrates a hybrid approach to spatial problem solving - the application utilises a nonlinear model for the temporal evolution of spatial variables and a genetic algorithm for calibrating the model in order to establish a good fit for the dataset under investigation. Several novel architectures are proposed for ISDSS based on existing distributed systems technologies. These architectures are assessed in terms of user interface, data and functional integration. Implementation issues are also discussed. The research contributions of this work are four-fold: (i) it lays the foundation for ISDSS as a distinct type of system for spatial decision support by examining the user interface, performance and methodological requirements of such systems; (ii) it explores a new approach for linking modelling techniques and SDSS; (iii) it investigates the possibility of modelling high technology industry; and (iv) it details novel architectures for ISDSS based on distributed systems.

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Weber, P. "Location intelligence : a decision support system for business site selection." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1302551/.

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As one of the leading ‘world cities’, London is home to a highly internationalised workforce and is particularly reliant on foreign direct investment (FDI) for its continued economic success. In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Given these pressures, the need for a coherent framework for data and methodologies to inform business location decisions is apparent. The research sets out to develop a decision support system to iteratively explore, compare and rank London’s business neighbourhoods. This is achieved through the development, integration and evaluation of spatial data and its manipulation to create an interactive framework to model business location decisions. The effectiveness of the resultant framework is subsequently assessed using a scenario based user evaluation. In this thesis, a geo-business classification for London is created, drawing upon the methods and practices common to geospatial neighbourhood classifications used for profiling consumers. The geo-business classification method encapsulates relevant location variables using Principal Components Analysis into a set of composite area characteristics. Next, the research investigates the implementation of an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP) allowing the aggregation of the geo-business classification and decision makers’ preferences into discrete decision alternatives. Lastly, the results of the integration of both data and model through the development of, and evaluation of a web-based prototype are presented. The development of this novel business location decision support framework enables not only improved location decision-making, but also the development of enhanced intelligence on the relative attractiveness of business neighbourhoods according to investor types.

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Zhang, Yan S. M. Program in Media Arts and Sciences (Massachusetts Institute of Technology). "CityMatrix : an urban decision support system augmented by artificial intelligence." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114059.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 75-77).
Cities are our future. Ninety percent of the world's population growth is expected to take place in cities. Not only are cities becoming bigger, they are also becoming more complex and changing even more rapidly. The decision-making process in urban design and urban planning is outdated. Currently, urban decision-making is mostly a top-down process, with community participation only in its late stages. Furthermore, many design decisions are subjective, rather than based on quantifiable performance and data. Urban simulation and artificial intelligence techniques have become more mature and accessible. However, until now these techniques have not been integrated into the urban decision-making process. Current tools for urban planning do not allow both expert and non-expert stakeholders to explore a range of complex scenarios rapidly with real-time feedback. To address these challenges, a dynamic, evidence-based decision support system called CityMatrix was prototyped. The goals of CityMatrix were 1) Designing an intuitive Tangible User Interface (TUI) to improve the accessibility of the decision-making process for non-experts. 2) Creating real-time feedback of multi-objective urban performances to help users evaluate their decisions, thus to enable rapid, collaborative decision-making. 3) Constructing a suggestion-making system that frees stakeholders from excessive, quantitative considerations and allows them to focus on the qualitative aspects of the city, thus helping them define and achieve their goals more efficiently. CityMatrix was augmented by Artificial Intelligence (AI) techniques including Machine Learning simulation predictions and optimization search algorithms. The hypothesis explored in this work was that the decision quality could be improved by the organic combination of both strength of human intelligence and machine intelligence. The system was pilot-tested and evaluated by comparing the problem-solving results of volunteers, with or without Al suggestions. Both quantitative and qualitative analytic results showed that CityMatrix is a promising tool that helps both professional and nonprofessional users understand the city better to make more collaborative and better-informed decisions. CityMatrix was an effort towards evidence-based, democratic decisionmaking. Its contributions lie in the application of Machine Learning as a versatile, quick, accurate, and low-cost approach to enable real-time feedback of complex urban simulations and the implementation of the optimization searching algorithms to provide open-ended decision-making suggestions.
by Yan Zhang.
S.M.

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Mao, Yanwei. "Decision Support System : A study of strategic decision makings in banks." Thesis, Jönköping University, JIBS, Business Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-12585.

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The main purpose of this research is to use Hermeneutic research approach to find out how Decision Support System (DSS) is used in banks and financial services. The research started from one stance, from which the further process could be extended to reach more complete picture of Decision Support System’s usage in strategic decision makings in banks. The research is also trying to find out the drawbacks and benefits of the DSS which have been used nowadays in banks. Furthermore, the future improvements of using DSS to make better decisions related with moral and different environments are also being discussed in the research findings.

During the primary data collection, resources from different channels have been used to support the research. The primary data sources include lectures and discussion in three banks’ visiting opportunities in Stockholm, Sweden, one interview with IT Vice president from Bank of America Merrill Lynch, New York, two interviews with a professor and a director respectively from Lund University and Financial Services Innovation Centre in University College Cork, Ireland.

Experiences from both academic and practical have been shared to strength the research’s validity and trustworthiness. Hermeneutic research approach addresses through the whole research process which needs to be open-minded and flexible.

Unawareness of DSS for people who are working in banks is one of the issues today. Different embedded models regarding various functions are not so clear to bank staff; thus there is a gap between human decisions and system decisions. There is a variation of requirements between central banks, retail banks, commercial banks, investment banks. Hence there should be a differentiation when implementing a system. Banking systems are widespread systems which are influenced by environment factors, political, economic, socio-cultural and technological variables.

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Xu, Xian Zhong. "Information systems for strategic intelligence support." Thesis, Open University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244368.

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West, Graeme Michael. "Computational intelligence methods for power system protection design and decision support." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400311.

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Almejalli, Khaled A., Keshav P. Dahal, and M. Alamgir Hossain. "Intelligent traffic control decision support system." Springer-Verlag, 2007. http://hdl.handle.net/10454/2554.

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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

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Galipalli, Ashwin Kumar, and Haritha Jyothi Madyala. "Process to Build an Efficient Decision Support System." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16405.

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Decision support systems will be an asset dealing with the complexity involved in many decision situations for companies, organizations and societies by integrating different aspects into a holistic pattern. That creates a close relationship to systems science since systems thinking promote holism as a profitable way to handle complexity. The ideal decision support system should not be used to make automatic decisions but to assist a human being in the decision process. That process is sometimes described as a model consisting of the phases, intelligence, design and choice. Intelligence is needed to understand the situation and find the information that is needed to continue the process. Design means designing different alternatives and in the last phase, choice, the alternatives are evaluated and the best alternative is chosen. A good decision support system should give the user assistance through the whole process. The main purpose of our research is identifying the process of building an efficient Decision Support System. The target groups are the people who are working with multinational companies that are specialized in constructing and delivering decision support systems to end users. The number of target companies involved in this study is only two and is limited Indian Multinational companies. The theoretical study helps in identifying the basic characteristics of a decision support system, exploring the types of decision support systems used in current organizations, resulting if there is any particular standard for constructing DSS today and signifying approach for constructing a user friendly decision support system by analyzing the existing literature related to DSS. At the same time, empirical study advances the research problem from a practical angle. The conclusion for this research is a comprehensive report in relation to the varieties of Decision Support Systems used in today’s organizations, qualities that a decision support system ought to possess and suggested process to be implemented for building an efficient decision support system.
Program: Magisterutbildning i informatik

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Andersson, Daniel, Hannes Fries, and Per Johansson. "Business Intelligence : The impact on decision support and decision making processes." Thesis, Jönköping University, JIBS, Business Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-1159.

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Historically, decision support systems have been used in organizations to facilitate better decisions. Business Intelligence has become important in recent years because the business environment is more complex and changes faster than ever before. Organizations have started to realize the value of existing information in operational, managerial, and strategic decision making. By using analytical methods and data warehousing, decision support can now be used in a flexible way and assist decision makers in decision making processes. Increasing investments in Business Intelligence indicate that it can bring value to organizations. Benefits such as the ability to access relevant and timely decision support when it is needed can be of tremendous value when the use of existing information has become more a question of survival or bankruptcy for an organization, than profit or loss. Thus, it would be interesting to see how decision support and decision making have changed in organizations after implementing a Business Intelligence system. The purpose of this thesis is to investigate if and how Business Intelligence has changed decision support and decision making processes.A deductive approach using a qualitative method has been used with semi-structured elite interviews. The thesis aims to investigate the manufacturing industry located in the Jönköping region in Sweden. The interviewed organizations are Husqvarna AB, Fläkt Woods AB, Myresjöhus AB, and Kinnarps AB. Our analysis shows positive effects of Business Intelligence in organizations with improvements of decision support due to timeliness, accessibility, quality, and better control of organizational information. As improvements in decision support has occurred, decision making has become better. Complicated problems are now easier to interpret by decision makers. Our research also concludes that intuition still has a major impact in decision making processes.

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Sjöberg, Viktor, and Elisabeth Hugner. "Insights about Business Intelligence and Decision-Making : A case study at Systembolaget." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414622.

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In today’s constantly evolving technological environment, businesses have more tools to support decision-making and these can be categorized as Decision Support Systems (DSS). One of the tools is Business Intelligence (BI), which is regarded as a high-priority investment in organizations nowadays. Even though there exists a vast amount of research in the DSS area, most of the influential work is conducted in time incomparable to today’s technological environment. In addition, most of the research focuses on profit-seeking organizations, as BI has been regarded as a tool to increase profits. However, non-profit organizations also use BI, but are not portrayed in the BI research area. The aim with this study is to explore how BI is used in relation to decision-making in a non-profit organization and to investigate the crucial factors in the usage of BI in relation to decision-making. A qualitative case study approach is applied where the Swedish non-profit organization Systembolaget AB is the case company. The main findings indicate that interaction between the two decision-making types is needed when using BI in a non-profit context. Moreover, having data literacy, data reliability, and data accessibility is found crucial in order to achieve BI success in relation to decision-making, especially when more and more decisions are made at the operational level. Finally, the results of this study amplify the need for an update in the DSS framework.

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Sun, Runpu. "Using Social Media Intelligence to Support Business Knowledge Discovery and Decision Making." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145394.

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The new social media sites - blogs, micro-blogs, and social networking sites, among others - are gaining considerable momentum to facilitate collaboration and social interactions in general. These sites provide a tremendous asset for understanding social phenomena by providing a wide availability of novel data sources. Recent estimates suggest that social media sites are responsible for as much as one third of new Web content, in the forms of social networks, comments, trackbacks, advertisements, tags, etc. One critical and immediate challenge facing the MIS researchers then becomes - how to effectively utilize this huge wealth of social media data, to facilitate business knowledge discovery and decision making.Among these available data sources, social networks constitute the backbone of almost all social media sites. These network structures provide a rich description of the social scenes and contexts, which is helpful for us to address the above challenge. In this dissertation, I have primarily employed the probabilistic network models, to study various social network related problems arose from the use of social media services. In Chapter 2 and Chapter 3, I studied how information overload can affect the efficiency of information diffusion in online social networks (Delicious.com and Digg.com). Novel diffusion model were proposed to model the observed information overload. The models and their extensions are thoroughly evaluated by solving the Influence Maximization problem related to information diffusion and viral marketing applications. In Chapter 4, I studied the information overload in a micro-blogging application (Twitter.com) using a design science methodology. A content recommendation framework was proposed to help micro-blogging users to efficiently identify quality emergency news feeds. Chapter 5 presents a novel burst detection algorithm concerning identifying and analyzing correlated burst patterns by considering multiple inputs (data streams) that co-evolve over time. The algorithm was later used for discovering burst keywords/tag pairs from online social communities, which are strong indicators of emerging or changing user interests.Chapter 6 concludes this dissertation by highlighting major research contributions and future directions.

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Cooper, Tessa L. "Case Adaptation for an Intelligent Decision Support System for Diabetes Management." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1289585163.

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Al-Mohamdi, Granim Al Hamaidi. "An intelligent decision support system for project management." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287199.

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Berggren, Andreas, Martin Gunnarsson, and Johannes Wallin. "Artificial intelligence as a decision support system in property development and facility management." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-25535.

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The construction industry has been hesitant for a long time to apply new technologies. In property development, the industry relies heavily on employees bringing experience from one project to another. These employees learn to manage risks in connection with the acquisition of land, but when these people retire, the knowledge disappears. An AI-based decision-support system that takes the risks and the market into account when acquiring land can learn from each project and bring this knowledge into future projects. In facility management, artificial intelligence could increase the efficiency of the allocation of staff in the ongoing operations. The purpose of the study is to analyse how companies in the real estate industry can improve their decision-making with the help of AI in property development and property management. In this study, two case studies of two different players in the real estate industry have been performed. One player, Bygg-Fast, represents property development and the other player, VGR, represents facility management. The study is based on interviews, discussions, and collected data. By mapping and then quantifying the risks and market indicators that are input data in the process, a basis can be created. The data can be used for a model that lays the foundation for an AI-based decision support system that will help the property developer to make calculated decisions in the land acquisition process. By mapping what a flow through a property looks like, measuring points can be set out to analyse how long the activities take in the specific business. These measured values provide a collection of data that makes it easier to plan the activities conducted in the property. A more efficient flow can be achieved by visualizing the entire process so staff can be allocated to the right part of the flow. By being flexible and being able to re-plan the business quickly if planning is disrupted, a high level of efficiency can be achieved. This could be done by an AI-based decision support system that simulates alternative day plans.
Byggbranschen har länge varit tveksamt till att applicera nya tekniker. Inom fastighetsutveckling bygger branschen mycket på att anställda tar med sig erfarenheter från ett projekt till ett annat. Dessa anställda lär sig hantera risker i samband med förvärv av mark men när dessa personer slutar eller går i pension försvinner kunskapen. Ett AI baserat beslutssystem som tar risk och marknad i beaktning vid förvärv av mark kan lära sig av varje projekt och ta med dessa kunskaper till framtida projekt. Inom fastighetsförvaltning skulle artificiell intelligens kunna effektivisera allokerandet av personal i den pågående verksamheten. Syftet med studien är att analysera hur företag i fastighetsbranschen kan förbättra sitt beslutstagande med hjälp av AI i utveckling av fastigheter samt fastighetsförvaltning. I denna studien har två fallstudier av två olika aktörer i fastighetsbranschen utförts. Ena aktören, Bygg-Fast, representerar fastighetsutveckling och den andra aktören, VGR, representerar fastighetsförvaltning. Studien bygger på intervjuer, diskussioner och insamlade data. Genom att kartlägga och sedan kvantifiera de risker samt marknadsindikatorer som är indata i processen kan ett underlag skapas. Underlaget kan användas för en modell som lägger grunden för ett AI baserat beslutsstödsystem som ska hjälpa fastighetsutvecklaren med att ta kalkylerade beslut i mark förvärvsprocessen. Genom att kartlägga hur ett flöde genom en fastighet ser ut kan mätpunkter sättas ut för att analysera hur lång tid aktiviteterna tar i den specifika verksamheten. Dessa mätvärden ger en samlad data som gör det lättare att planera verksamheten som bedrivs i fastigheten. Ett effektivare flöde kan uppnås genom att visualisera hela processen så personal kan allokeras till rätt del av flödet. Genom att vara flexibel och kunna planera om verksamheten snabbt ifall planering störs kan en hög effektivitet nås. Detta skulle kunna göras av ett AI baserat beslutsstödsystem som simulerar alternativa dagsplaneringar.

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Casas, Irene. "Towards a learning atis : intelligence-assisted travel decision support system using neural networks /." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486402288260508.

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Basra, Rajveer Singh. "A framework for knowledge discovery within business intelligence for decision support." Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/4457.

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Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes.

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Zhang, Pu. "Development of an intelligent decision support system of transportation planning for high rise construction." Thesis, University of London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299939.

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Jíra, Martin. "Business intelligence: Požadavky na výběr softwaru a jeho přínosy." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-198247.

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The thesis focuses on requirements, which must be established when business intelligence is chosen, from user point of view. Business intelligence je decision support system and it is very important, that system must be ease of use and provide expected information for analyst and managers. During the process of choosing the software, they are the one, who provide requirements and expectation of the system. The thesis focuses on mapping those requirements and their importance using survey among analyst and managers, who uses business intelligence or want to use it.

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Goonatilake, Suran. "An intelligent hybrid system for financial decision support." Thesis, University College London (University of London), 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307300.

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Москаленко, Альона Сергіївна, Алена Сергеевна Москаленко, and Alona Serhiivna Moskalenko. "Intelligent decision support system for renal radionuclide imaging." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46806.

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Radionuclide imaging of kidneys has a special place in nuclear medicine. It allows to register functional changes, far earlier than the structural and anatomical changes. Therefore, it is indispensable at early diagnosis. The reliability of data interpretation of renal scintigraphy studies depends on the level of doctor-diagnostician’s professional qualification and on the presence of their practical experience.

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Anand, Sarabjot Singh. "Value-adding intelligence in clinical prognostic systems." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311514.

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Keneni, Blen M. Keneni. "Evolving Rule Based Explainable Artificial Intelligence for Decision Support System of Unmanned Aerial Vehicles." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525094091882295.

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Domrachev, V., O. Lyubich, and R. Kostetsky. "The modern systems of business intelligence as a component of decision making support in Ukrainian banking system." Thesis, Українська академія банківської справи Національного банку України, 2013. http://essuir.sumdu.edu.ua/handle/123456789/59472.

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The current stage of the banking services development that runs in increasing competition environment and the crisis in the global monetary and financial markets is characterized by three main trends: the growing role of innovative technologies, increasing of the scope and diversity of retail banking services, increasing of the share of innovative technologies, formation of new market segments of services for the population.

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Matz, Thomas W. "A decision support system for synchronizing manufacturing in a multifacility production system." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182444606.

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Zarkadis, George. "An intelligent decision support system for acid-base diagnosis." Thesis, City University London, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235504.

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ZHAO, YING, and JINZI GAO. "Decision Support System- Research on the application of DSS in China's Banks." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20445.

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Information system is widely used in financial area all around the world today, and businessintelligence systems has draw more and more attention from both academia and businesscircles. Based on this situation, we carried out our research. The main purpose of our researchis to find out how Decision Support System (DSS) is used in China's banks. As there are morethan five hundred banks in China, we choose the four biggest commercial banks(which hascover more than 85% of financial activities in China's banking area) as examples to study. Wesent Emails and made telephone calls to different roles in these four banks, from chiefinformation officer, managers of business to normal staff. Before carried out interviews, wedid literature study to set a scientific background for our interviews. After the collection andanalysis of data from both interview and literature study, the result is presented in threechapters. The theoretical study part introduces the theory background of DSS and how it isused in banks, the framework of the DSS and the basic model of the DSS, also newtechniques in DSS. The Empirical results part introduces the results got from interviews. InAnalysis part the results from the former chapters will be combined and analyzed, in this partwe presents the application situation of DSS in China's banks, the affection of DSS on banksemployees and improvement and drawback DSS brings to China's banks. Also newtechnology of decision support system and its application. And the last part we would drawconclusions for this thesis and summarize results from the interviews and theories andevaluate the whole research process. And the introduction of our research and the methodsused to achieve the research goal will be introduced in the first two chapters.

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Walker, Donald. "Similarity determination and case retrieval in an intelligent decision support system for diabetes managment." Ohio : Ohio University, 2007. http://www.ohiolink.edu/etd/view.cgi?ohiou1194562654.

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Li, Dong. "Development of a decision support system for partnership evaluation and the strategic management of supply chains." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287164.

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Bunting, John W. "An expert system version of a textbook of general practice medicine, to help with tutoring and decision support." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369992.

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Spurgeon, K. "An intelligent decision making support system for dissolved gas analysis." Thesis, University of Liverpool, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427045.

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Srisukkham, Worawut. "An intelligent decision support system for acute lymphoblastic leukaemia detection." Thesis, Northumbria University, 2017. http://nrl.northumbria.ac.uk/36140/.

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The morphological analysis of blood smear slides by haematologists or haematopathologists is one of the diagnostic procedures available to evaluate the presence of acute leukaemia. This operation is a complex and costly process, and often lacks standardized accuracy owing to a variety of factors, including insufficient expertise and operator fatigue. This research proposes an intelligent decision support system for automatic detection of acute lymphoblastic leukaemia (ALL) using microscopic blood smear images to overcome the above barrier. The work has four main key stages. (1) Firstly, a modified marker-controlled watershed algorithm integrated with the morphological operations is proposed for the segmentation of the membrane of the lymphocyte and lymphoblast cell images. The aim of this stage is to isolate a lymphocyte/lymphoblast cell membrane from touching and overlapping of red blood cells, platelets and artefacts of the microscopic peripheral blood smear sub-images. (2) Secondly, a novel clustering algorithm with stimulating discriminant measure (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of the nucleus and cytoplasm of lymphocytic cell membranes. The SDM measures are used in conjunction with Genetic Algorithm for the clustering of nucleus, cytoplasm, and background regions. (3) Thirdly, a total of eighty features consisting of shape, texture, and colour information from the nucleus and cytoplasm of the identified lymphocyte/lymphoblast images are extracted. (4) Finally, the proposed feature optimisation algorithm, namely a variant of Bare-Bones Particle Swarm Optimisation (BBPSO), is presented to identify the most significant discriminative characteristics of the nucleus and cytoplasm segmented by the SDM-based clustering algorithm. The proposed BBPSO variant algorithm incorporates Cuckoo Search, Dragonfly Algorithm, BBPSO, and local and global random walk operations of uniform combination, and Lévy flights to diversify the search and mitigate the premature convergence problem of the conventional BBPSO. In addition, it also employs subswarm concepts, self-adaptive parameters, and convergence degree monitoring mechanisms to enable fast convergence. The optimal feature subsets identified by the proposed algorithm are subsequently used for ALL detection and classification. The proposed system achieves the highest classification accuracy of 96.04% and significantly outperforms related meta-heuristic search methods and related research for ALL detection.

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Sawaragi, Tetsuo. "Intelligent Decision Support System Based on Hierarchical Causal Knowledge Structures." Kyoto University, 1987. http://hdl.handle.net/2433/74703.

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Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko, and V. V. Moskalenko. "Intelligent decision support system for control of growing single crystals." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13410.

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Yusuf, Syed Adnan. "An evolutionary AI-based decision support system for urban regeneration planning." Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/114896.

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The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.

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Al-Ani, I. I. "Construction of knowledge based decision support systems : An investigation in undergraduate course selection." Thesis, University of Kent, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376774.

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Hamilton-Wright, Andrew. "Transparent Decision Support Using Statistical Evidence." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/778.

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An automatically trained, statistically based, fuzzy inference system that functions as a classifier is produced. The hybrid system is designed specifically to be used as a decision support system. This hybrid system has several features which are of direct and immediate utility in the field of decision support, including a mechanism for the discovery of domain knowledge in the form of explanatory rules through the examination of training data; the evaluation of such rules using a simple probabilistic weighting mechanism; the incorporation of input uncertainty using the vagueness abstraction of fuzzy systems; and the provision of a strong confidence measure to predict the probability of system failure.

Analysis of the hybrid fuzzy system and its constituent parts allows commentary on the weighting scheme and performance of the "Pattern Discovery" system on which it is based.

Comparisons against other well known classifiers provide a benchmark of the performance of the hybrid system as well as insight into the relative strengths and weaknesses of the compared systems when functioning within continuous and mixed data domains.

Classifier reliability and confidence in each labelling are examined, using a selection of both synthetic data sets as well as some standard real-world examples.

An implementation of the work-flow of the system when used in a decision support context is presented, and the means by which the user interacts with the system is evaluated.

The final system performs, when measured as a classifier, comparably well or better than other classifiers. This provides a robust basis for making suggestions in the context of decision support.

The adaptation of the underlying statistical reasoning made by casting it into a fuzzy inference context provides a level of transparency which is difficult to match in decision support. The resulting linguistic support and decision exploration abilities make the system useful in a variety of decision support contexts.

Included in the analysis are case studies of heart and thyroid disease data, both drawn from the University of California, Irvine Machine Learning repository.

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Pedersen, Kim Ohme. "Explanation Methods in Clinical Decision Support : A Hybrid System Approach." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11833.

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The use of computer-based decision support systems within the field of health science has over the last decades been extensively researched and tested, both in controlled environments and in clinical practice. Despite the obvious benefits of utilizing such systems in the day-to-day activities, many of the designed systems fail to make the impact one could hope to achieve. We have designed and implemented a prototype of a decision support system which use both Case-Based Reasoning and probabilistic inference through a Bayesian Network as a basis for the solution. To achieve user acceptance an explanation module has been implemented which gives the user full access to the data which has been used in the reasoning process, both from the Case-Based Reasoning and the Bayesian Network. The system has shown promising results within the domain of wine recommendation, with a very high accuracy despite uncertain accuracy of the knowledge within the system. Furthermore the explanations presented to an expert conformed to the causal way of reasoning used by said expert, and was accepted as a very useful tool to get pointed in the right direction for evaluation of the solution.

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Roued-Cunliffe, Henriette. "A decision support system for the reading of ancient documents." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:9d547661-4dea-4c54-832b-b2f862ec7b25.

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The research presented in this thesis is based in the Humanities discipline of Ancient History and begins by attempting to understand the interpretation process involved in reading ancient documents and how this process can be aided by computer systems such as Decision Support Systems (DSS). The thesis balances between the use of IT tools to aid Humanities research and the understanding that Humanities research must involve human beings. It does not attempt to develop a system that can automate the reading of ancient documents. Instead it seeks to demonstrate and develop tools that can support this process in the five areas: remembering complex reasoning, searching huge datasets, international collaboration, publishing editions, and image enhancement. This research contains a large practical element involving the development of a DSS prototype. The prototype is used to illustrate how a DSS, by remembering complex reasoning, can aid the process of interpretation that is reading ancient documents. It is based on the idea that the interpretation process goes through a network of interpretation. The network of interpretation illustrates a recursive process where scholars move between reading levels such as ‘these strokes look like the letter c’ or ‘these five letters must be the word primo’. Furthermore, the thesis demonstrates how technology such as Web Services and XML can be used to make a DSS even more powerful through the development of the APPELLO word search Web Service. Finally, the conclusion includes a suggestion for a future development of a working DSS that incorporates the idea of a layer-based system and focuses strongly on user interaction.

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Walker, Donald. "Similarity Determination and Case Retrieval in an Intelligent Decision Support System for Diabetes Management." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1194562654.

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Mehdi, Hadi Abidalsajad-Kamel, and Yin-tsu Lin. "Evaluation of Information Quality in Business Intelligence as a key success factor for using Decision Support System." Thesis, Uppsala universitet, Informationssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-163186.

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Eriksson, Mats, and Stefan Sahlin. "Ledningsdatabas för beslutsstöd : en studie på Electrolux i Mariestad." Thesis, University West, Department of Informatics and Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-614.

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Andersson, Daniel, Jenny Franzén, and Hannes Fries. "Business Intelligence : Analysis of vendors’ and suppliers’ arguments for BI." Thesis, Jönköping University, JIBS, Business Administration, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-1114.

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Introduction

Organizations are exposed to a rapidly changing business environment with never ending challenges. Investments in information technology (IT) have been one common approach to support organizations. Business Intelligence (BI), an off-spring from IT, is a system that assists many organizations in taking more accurate and timely decisions, improving process monitoring and providing better support for decision making. Recently organizations have started to realize the value of investing in BI, by discovering its analytical methods and capabilities to create business value.

Problem

Investments in BI have increased substantially over the past years and one reason for this might be due to vendors praise about BI’s ability to deliver business value. Significantly increased business value, better decision making, and high returns on investments are only a few benefits that have been claimed for. When considering the fact that it is very difficult to measure any direct benefits from IT investments in general, and BI as a consequence, an interest for analyzing the arguments used for selling BI emerged.

Purpose

The purpose of this thesis is to identify what arguments vendors and suppliers use when selling BI solutions, and explore their value by analyzing them through the use of existing theories from literature.

Method

A qualitative approach has been adopted, where unstructured interviews with BI vendors and suppliers were conducted. An inductive approach has been applied to gather arguments and then shifted to a deductive, in order to finalize the study and analyze arguments with appropriate theory. The research has been performed from without the Swedish market with well-known organizations.

Conclusions

A single version of the truth, control, and time savings are credible arguments for investing in BI. Furthermore, cost savings and improved analytical capabilities are fairly credible, whereas increased efficiency has least credibility when analyzed against theories. In general, we believe that the ability to gain from these positive effects from BI, organizations have to take an active role in realizing these.

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Viademonte, da Rosa Sérgio I. (Sérgio Ivan) 1964. "A hybrid model for intelligent decision support : combining data mining and artificial neural networks." Monash University, School of Information Management and Systems, 2004. http://arrow.monash.edu.au/hdl/1959.1/5159.

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Lamalfa, Salvatore. "Business intelligence mobile per aziende di vendita al dettaglio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3718/.

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Belal, Suliman Yousef. "The development of an intelligent patient monitoring system in the neonatal intensive care unit (NICU)." Thesis, Keele University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251516.

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Nadeem, Malik-Sajjad-Ahmed. "Classification with a reject-option to improve transcriptomic data based decision support systems." Paris 13, 2011. http://www.theses.fr/2011PA132012.

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L’obésité et le cancer sont des maladies complexes multifactorielles. Les données de puces à ADN représentent une source importante de connaissances pour étudier ces maladies. Le choix d’une thérapie pour lutter contre l’obésité pourrait avoir un impact élevé sur sa prévalence, mais l’utilisation de modèles prédictifs dans ce cadre reste un domaine de recherche relativement inexploré. De telles méthodes sont utilisées pour l’aide au diagnostic avec un certain succès dans le domaine du cancer, mais elles nécessitent encore des améliorations. La majorité des études de puces à ADN sont basées sur des schémas de classification dans lesquels tous les échantillons sont classés, quelque soit le degré de confiance associé à leur classification. Dans le domaine thérapeutique, il est plus sûr de s’abstenir de prendre une décision si le degré de confiance n’est pas assez élevé, plutôt que de proposer une mauvaise thérapie. Des études basées sur des approches d’apprentissage computationnel suggèrent l’utilisation d’une option de rejet dans les systèmes de décision. Le rejet d’ambiguïté et le rejet de distance sont parmi les principales méthodes de rejet, mais les performances prédictives des méthodes de classification n’atteignent pas toujours le niveau souhaité avec ces approches. Le manuscrit de cette thèse explore tout d’abord ces deux approches dans le cadre de données de puces pour l’obésité et le cancer, puis suggère leur combinaison pour adresser le problème des faibles performances de classification. De plus, des méthodes graphiques permettant de visualiser et de comparer les performances prédictives des méthodes de classification avec option de rejet sont présentées : Accuracy-Rejection Curves (ARCs) et Cost-Rejection Curves (CRCs). Nous avons analysé empiriquement les trois approches de rejet avec l’aide des ARCs et des CRCs pour des données simulées, ainsi que pour les données d’obésité et de cancer. Les trois expériences ont montré une amélioration des résultats
Data extracted from DNA microarrays is considered an important source of knowledge about obesity and cancer like complex diseases. Few obe studies exist to predict the outcomes of different therapies. The choice of a therapy for obesity could have high impact in the prevalence of this disease and the use of predictive models for this is relatively an unexplored field of research. In cancer studies, such methods are used with some success but they still need improvements. Generally, in microarray classification, ail the samples are classified, regardless of the degree of confidence associated with the classification of a particular sample. It is wise to refrain from making a decision about a therapy if the degree of confidence on a diagnosis is flot high, rather than suggesting a wrong therapy. Few studies based on machine learning approaches suggested the use of reject-option in decision support systems. Sometimes, with principal reject-option approaches (the ambiguity-reject and the distance-reject) the predictive performances of classification methods do flot become up-to a desired level. In this thesis we first explore these reject-option approaches and then address the problem of low performing classification methods by suggesting the combination of both approaches. Moreover, graphical methods i. E. Accuracy Rejection Curies (ARCs) and Cost-Rejection Curves (CRCs), for visualizing and comparing the predictive performances of classification methods with a reject-option, are presented. Empirical results based on three reject option approaches and with the help of ARCs and CRCs for synthetic data, obesity data and cancer data have shown the improved results

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Dissertations / Theses on the topic 'Intelligence decision support system'

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