Towards the ensemble: IPCBR model in investigating financial bubbles

Asset value predictability remains a major research concern in financial market especially when considering the effect of unprecedented market fluctuations on the behaviour of market participants. This paper presents preliminary results toward the building a reliable forward problem on ensemble approach IPCBR model, that leverages the capabilities of Case based Reasoning(CBR) and Inverse Problem Techniques (IPTs) to describe and model abnormal stock market fluctuations (often associated with asset bubbles) using datasets from historical stock market prices. The framework uses a rich set of past observations and geometric pattern description and then applies a CBR to formulate the forward problem, Inverse Problem formulation is then applied to identify a set of parameters that can statistically be associated with the occurrence of the observed patterns. This research work presents a formative strategy aimed to determine the causes of behaviour, rather than predict future time series points which brings a novel perspective to the problem of asset bubbles predictability, and a deviation from the existing research trend. The results depict the stock dynamics and statistical fluctuating evidence associated with the envisaged bubble problem

Skin Tumors Diagnosis Utilizing Case Based Reasoning and The Expert System

Skin cancer is considered as the most type of cancer that happens in humans. Three basic types of cancer occur which are basal cell carcinoma (BCC), Squamous cell carcinoma (SCC). Skin cancer leads to death if it is not diagnosed in an early stage. Fortunately, early diagnosis of skin cancer raises the survival rate of victims. Computer-aided has a great role to detect skin cancer which leads to saving human life. Based on that, this study proposes a computer-aided diagnosis (CAD) system that detects skin cancer using digital images, techniques of image processing, by using the case-based reasoning and expert system. The main goal for designing this system is to create a cheap, easy-to-use, and relatively accurate system for detecting skin cancer in an early stage to save human life, raises the survival rate, and decreases the cost of the dermoscopy test

Exploring Best Practices to Utilize Business Intelligence Systems

Organizational leaders who can manage business intelligence system (BIS) resources may achieve sustainable success in economic, political, and corporate environments. The review of professional literature indicated that effective resource management in a BIS environment requires the establishment of best practice. The purpose of this qualitative, single-case study was to explore best practices among 9 BIS practitioners for effective resource management. Participation criteria included the active engagement in BIS professional disciplines and the willingness to share their perspectives. The conceptual framework for this study was the cognitive experiential self-theory (CEST). Five leaders and 4 data analysts at an eastern U.S. county government agency were interviewed. Using computer based qualitative data analysis software to assist with the coding process, interview transcripts and the published directives of government agency leaders were reviewed to identify themes and achieve triangulation. Five themes emerged: the need for comprehensive policies and procedures for creating operating standards, updated data acquisition training, human capital dynamics management for improved efficiency, protocols for transforming raw information into knowledge, and safeguards for preventing bias in data analysis. Findings derived from this study could contribute to global social change as BIS leaders use best practices to improve resource and data management proficiencies for rapidly transforming information into knowledge for developing policies, services, and regulations that affect public safety, fiscal planning, and social risk management

Valoriser les connaissances issues des expériences vécues pour recommander des actions de protection des sources d'eau potable : application du raisonnement à base de cas

Depuis des décennies, les pays du monde entier s'affairent à préserver leurs précieuses ressources en eau potable. Ils cherchent à anticiper les risques et à réduire les impacts anthropiques qui pourraient altérer les sources d'approvisionnement. Au Canada, la protection des sources d'eau potable (PSEP) est mise en œuvre au sein de l'approche à barrières multiples, dont elle est l'une des barrières fondamentales. Cette approche permet une gestion multidimensionnelle de l'eau à l'aide d'outils et des pratiques visant à assurer la qualité de l'eau de la source au robinet. Bien que différents cadres existent pour prendre en compte l'eau dans l'aménagement du territoire, la mise en œuvre de la protection des sources peine à se concrétiser. Pourtant, les acteurs de l'eau et de l'aménagement du territoire ont une grande expérience dans la mise en œuvre d'actions. Alors, comment partager ces expériences afin de les soutenir dans l'identification et la mise en œuvre de futures actions de protection des sources ? Le but de cette thèse est de développer un prototype de système d'aide à la décision à base de connaissances (KB-DSS). Celui-ci a pour objectif de faciliter l'identification d'actions ciblées de PSEP selon les problèmes rencontrés. Pour ce faire, ce prototype a été développé sur la base des connaissances issues des expériences vécues depuis deux décennies au Québec, mettant à contribution des expériences réelles de mise en œuvre d'actions liées à la protection de l'eau. Il est conçu pour être utilisable par tout acteur ayant un intérêt à agir pour protéger les sources d'eau potable à l'échelle locale et régionale, via un transfert de connaissances dans le processus d'élaboration et de mise en œuvre d'actions. En étant un support dans la définition des actions futures, le prototype développé entend encourager les parties prenantes à apprendre les unes des autres. L'originalité de la thèse repose sur l'adoption combinée de l'approche en science du design/de la conception (DSR), qui a servi de lignes directrices pour adopter une démarche collaborative et transparente. Celle-ci a permis une application réussie du raisonnement à base de cas (CBR) au complexe problème de la PSEP dans un cadre de gestion de l'eau et du territoire. De cette démarche sont nés différents outils méthodologiques, procédures et connaissances permettant de mieux comprendre les problèmes liés à la PSEP, mais également d'illustrer la conception intégrale d'un prototype d'aide à la décision à base de connaissances utilisant le CBR. Tout d'abord, le cadre conceptuel (chapitre 1) explore et tente de comprendre les liens qui existent entre la nature des problèmes à résoudre pour protéger l'eau, l'environnement décisionnel et la prise de décision. Pour ce faire, le cadre adopte une approche systémique et holistique superposant différentes théories et concepts tels que la gouvernance de l'eau, la gestion de l'eau, la prise de décision, la rationalité et la connaissance. Cette compréhension des défis sous-jacents à la mise en œuvre de la PSEP permettait de mieux comprendre la complexité du problème à résoudre et posait les bases à l'élaboration du prototype de système CBR proposé. Dans l'optique de mieux comprendre comment les défis soulevés dans le cadre conceptuel se concrétisent en pratique, le second chapitre présente une enquête en ligne documentant la mise en œuvre de la PSEP au Québec. Celle-ci visait à brosser un portrait-diagnostic permettant de mieux comprendre le processus décisionnel, d'identifier qui sont les intervenants et quelles sont les connaissances produites et mobilisées pour la prise de décision sur la PSEP. Les analyses qualitatives et quantitatives des réponses des 208 intervenants retenus ont permis de constater que la mise en œuvre de la PSEP impliquait une grande diversité d'intervenants, de tâches et de connaissances créées et se caractérisait par un fort dynamisme inter-organisationnel. Cependant, on constatait que son processus décisionnel perdait en inclusivité au fil des étapes de mise en œuvre, que les connaissances étaient parfois redondantes et qu'il existait de nombreux enjeux de transfert de connaissances (accès, quantité ou qualité des connaissances) entre les intervenants. Lors de l'enquête en ligne présentée au second chapitre, il a été demandé à certains acteurs (organismes de bassins versants, villes, municipalités régionales de comté) d'illustrer les problèmes liés à la PSEP rencontrés sur le terrain. En parallèle, 102 intervenants se sont auto-recrutés pour participer au processus de design du système d'aide à la décision. Le troisième chapitre présente la démarche d'acquisition et de structuration des connaissances du dit KB-DSS par une approche CBR. Le chapitre décrit une seconde enquête en ligne ayant permis de définir ce qu'est un cas pour la PSEP, soit une expérience vécue qui consiste en une multitude de problèmes et de solutions mises en œuvre. Puis, il décrit la modélisation d'une taxonomie des connaissances ayant permis d'aboutir à des descriptions structurées des cas. La conception des cas repose sur le savoir-faire et les besoins en connaissances exprimés par les acteurs de l'eau. La base de cas constitue l'épine dorsale du prototype de KB-DSS destiné à guider les décideurs dans l'élaboration de solutions fondées sur des expériences passées. Le quatrième chapitre présente le prototype de KB-DSS/CBR pour la protection des sources d'eau potable. Il retrace comment le CBR a été modélisé, structuré, implanté, testé et validé en collaboration avec les 102 acteurs de la gestion et de la gouvernance de l'eau au Québec. Il décrit l'intégralité du processus manuel d'ingénierie de cas pour concevoir des attributs qualitatifs sur la base de la taxonomie des connaissances. Il présente l'édition des cas, le processus et les métriques permettant de retrouver des cas, l'implantation et un exemple d'utilisation ainsi que la validation du prototype, réalisée par une procédure participative rigoureuse et transparente avec un petit groupe d'acteurs de l'eau du Québec. Ainsi, il fournit des preuves empiriques du potentiel positif d'une approche CBR pour la PSEP sur le territoire, et retrace une démarche qui peut être généralisée à d'autres contextes géographiques et socio-économiques similaires.Countries worldwide have been working for decades to preserve their precious drinking water resources. They seek to anticipate risks or reduce anthropogenic impacts that could alter the water quality and availability. In Canada, drinking water source protection (DSWP), or source water protection (SWP), is implemented as part of the multi-barrier approach and is one of the fundamental barriers. This approach allows for multidimensional water management using tools and practices to ensure water quality from source to tap. Although various frameworks exist to consider water in spatial planning, the implementation of DWSP is struggling to materialize. However, water and spatial planning actors have significant experience implementing actions. So, how can these experiences be shared to support them in identifying and implementing future DWSP actions? The goal of this thesis is to develop a prototype of a knowledge-based decision support system (KB-DSS). The objective of this prototype is to facilitate the identification of targeted actions for water protection according to the problems encountered. To do so, this prototype was developed based on knowledge gained from past experiences conducted over the last two decades in Quebec, using real experiences in implementing actions related to water protection. It is designed to be used by any actor with an interest in contributing for the protection of drinking water sources at the local and regional levels, through the transfer of knowledge in the process of developing and implementing actions. By being a support in the definition of future actions, the developed prototype intends to encourage the actors to learn from each other. The originality of the thesis lies in the combined adoption of the design science approach (DSR), which served as a guideline to adopt a collaborative and transparent approach. This allowed for a successful application of case-based reasoning (CBR) to the complex problem of DWSP in a water and territory management framework. From this approach, various methodological tools, procedures and knowledge were developed to better understand the DWSP problems, but also to illustrate the complete design of a prototype knowledge-based decision support system using CBR. First, the conceptual framework (chapter 1) explores and attempts to understand the links between the nature of the problems to be solved to protect water, the decision-making environment, and the decision-making process. These issues were explored by adopting a system analysis that allowed for layering concepts such as water governance, water management, decision-making, rationality, and knowledge. This holistic understanding of the underlying challenges of DWSP implementation provided a better understanding of the complexity of the problem at hand and laid the foundation for developing the proposed CBR system. To better understand how the challenges raised in the conceptual framework materialize in practice, the second chapter presents an online survey documenting the implementation of DWSP in Quebec. This survey aimed to provide a diagnostic portrait to understand the decision-making process better and identify the actors and the knowledge produced and mobilized for DWSP decision-making. Qualitative and quantitative analyses of the responses from the 208 selected actors revealed that the implementation of DWSP involved a wide variety of actors, tasks and knowledge created and was characterized by great inter-organizational dynamism. However, it was found that the decision-making process becomes less inclusive as actions are implemented. Also, the knowledge was sometimes redundant, and there were many problems with the knowledge transfer (access, quantity, or knowledge quality) between actors. During the online survey presented in the second chapter, selected actors (watershed organizations, municipalities, counties, etc.) were asked to illustrate DWSP-related problems encountered in the field. In parallel, 102 actors were self-recruited to participate in the design process of the KB-DSS. The third chapter presents the acquisition and structuring of DWSP problem-related knowledge. The chapter describes a second online survey that helped define a DWSP case, i.e., a lived experience consisting of a multitude of problems and solutions implemented at various scales by various actors. It then describes the modelling of a knowledge taxonomy that led to structured case descriptions. The design of the cases is based on the expertise and knowledge needs expressed by the water actors. The case base is the backbone of the KB-DSS prototype to guide decision-makers in developing solutions based on past experiences. The fourth chapter presents the prototype KB-DSS/CBR system for DWSP. It traces how CBR was modelled, structured, implemented, tested and validated in collaboration with 102 water management and governance actors in Quebec. It describes the entire manual case engineering process for the design of qualitative attributes from the knowledge taxonomy. It presents the case base, the case edition, and the case retrieval (process and metrics). This chapter also illustrates the implementation using a real-world experience use case, as well as the validation of the prototype, carried out through a transparent, participatory procedure with a small group of water actors in Quebec. Thus, it provides empirical evidence of the high potential of a CBR approach for DWSP in the spatial planning context and describes an approach that can be generalized to other similar geographical and socio-economic contexts

A new strategy for case-based reasoning retrieval using classification based on association

Cased Based Reasoning (CBR) is an important area of research in the field of Artificial Intelli-gence. It aims to solve new problems by adapting solutions, that were used to solve previous similar ones. Among the four typical phases - retrieval, reuse, revise and retain, retrieval is a key phase in CBR approach, as the retrieval of wrong cases can lead to wrong decisions. To ac-complish the retrieval process, a CBR system exploits Similarity-Based Retrieval (SBR). How-ever, SBR tends to depend strongly on similarity knowledge, ignoring other forms of knowledge, that can further improve retrieval performance.The aim of this study is to integrate class association rules (CARs) as a special case of associa-tion rules (ARs), to discover a set (of rules) that can form an accurate classifier in a database. It is an efficient method when used to build a classifier, where the target is pre-determined. The proposition for this research is to answer the question of whether CARs can be integrated into a CBR system. A new strategy is proposed that suggests and uses mining class association rules from previous cases, which could strengthen similarity based retrieval (SBR). The propo-sition question can be answered by adapting the pattern of CARs, to be compared with the end of the Retrieval phase. Previous experiments and their results to date, show a link between CARs and CBR cases. This link has been developed to achieve the aim and objectives.A novel strategy, Case-Based Reasoning using Association Rules (CBRAR) is proposed to improve the performance of the SBR and to disambiguate wrongly retrieved cases in CBR. CBRAR uses CARs to generate an optimum frequent pattern tree (FP-tree) which holds a val-ue of each node. The possible advantage offered is that more efficient results can be gained, when SBR returns uncertain answers. In addition, CBRAR has been evaluated using two sources of CBR frameworks - Jcolibri and Free CBR. With the experimental evaluation on real datasets indicating that the proposed CBRAR is a better approach when compared to CBR systems, offering higher accuracy and lower error rate

A case-based reasoning system for radiotherapy treatment planning for brain cancer

In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past. This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge

A case-based reasoning system for radiotherapy treatment planning for brain cancer

In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past. This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge