Dans ce travail, on s’intéresse aux Systèmes d’Aide à la Décision Mobiles qui sont basés sur le processus d’Extraction des Connaissances à partir des Données (SADM/ECD). Nous contribuons non seulement à l'évaluation de ces systèmes, mais aussi à l'évaluation dans le processus d’ECD lui-même. L'approche proposée définit un module de support d'évaluation pour chaque module composant le processus d’ECD en se basant sur des modèles de qualité. Ces modules évaluent non seulement la qualité d'utilisation de chaque module logiciel composant le processus d’ECD, mais aussi d'autres critères qui reflètent les objectifs de chaque module de l’ECD. Notre objectif est d'aider les évaluateurs à détecter des défauts le plus tôt possible pour améliorer la qualité de tous les modules qui constituent un SADM/ECD. Nous avons aussi pris en compte le changement de contexte d'utilisation en raison de la mobilité. De plus, nous avons proposé un système d’aide à l’évaluation, nommé CEVASM : Système d’aide à l’évaluation basée sur le contexte pour les SADM, qui contrôle et mesure tous les facteurs de qualité proposés. Finalement, l'approche que nous proposons est appliquée pour l'évaluation des modules d'un SADM/ECD pour la lutte contre les infections nosocomiales à l'hôpital Habib Bourguiba de Sfax, Tunisie. Lors de l'évaluation, nous nous sommes basés sur le processus d'évaluation ISO/IEC 25040. L'objectif est de pouvoir valider, a priori, l'outil d'évaluation réalisé (CEVASM) et par conséquent, l'approche proposée. / In this work, we are interested in Mobile Decision support systems (MDSS), which are based on the Knowledge Discovery from Data process (MDSS/KDD). Our work is dealing with the evaluation of these systems, but also to the evaluation in the KDD process itself. The proposed approach appends an evaluation support module for each software module composing the KDD process based on quality models. The proposed evaluation support modules allow to evaluate not only the quality in use of each module composing the KDD process, but also other criteria that reflect the objectives of each KDD module. Our main goal is to help evaluators to detect defects as early as possible in order to enhance the quality of all the modules that constitute a MDSS/KDD. We have also presented a context-based method that takes into account the change of context of use due to mobility. In addition, we have proposed an evaluation support system that monitors and measures all the proposed criteria. Furthermore, we present the implementation of the proposed approach. These developments concern mainly the proposed evaluation tool: CEVASM: Context-based EVAluation support System for MDSS. Finally, the proposed approach is applied for the evaluation of the modules of a MDSS/KDD for the fight against nosocomial infections, in Habib Bourguiba hospital in Sfax, Tunisia. For every module in KDD, we are interested with the phase of evaluation. We follow the evaluation process based on the ISO/IEC 25040 standard. The objective is to be able to validate, a priori, the realized evaluation tool (CEVASM) and consequently, the proposed approach.

La quantité et la disponibilité sans cesse croissantes de données urbaines dérivées de sources variées posent de nombreux problèmes, notamment la consolidation, la visualisation et les perspectives d’exploitation maximales des données susmentionnées. Un problème prééminent qui affecte l’urbanisme est le choix du lieu approprié pour accueillir une activité particulière (service social ou commercial commun) ou l’utilisation correcte d’un bâtiment existant ou d’un espace vide. Dans cette thèse, nous proposons une approche pour aborder les défis précédents rencontrés avec les techniques d’apprentissage automatique, le classifieur de forêts aléatoires comme méthode dominante dans un système qui combine et fusionne divers types de données provenant de sources différentes, et les code à l’aide d’un nouveau modèle sémantique. qui peut capturer et utiliser à la fois des informations géométriques de bas niveau et des informations sémantiques de niveau supérieur et les transmet ensuite au classifieur de forêts aléatoires. Les données sont également transmises à d'autres classificateurs et les résultats sont évalués pour confirmer la prévalence de la méthode proposée. Les données extraites proviennent d’une multitude de sources, par exemple: fournisseurs de données ouvertes et organisations publiques s’occupant de planification urbaine. Lors de leur récupération et de leur inspection à différents niveaux (importation, conversion, géospatiale, par exemple), ils sont convertis de manière appropriée pour respecter les règles du modèle sémantique et les spécifications techniques des sous-systèmes correspondants. Des calculs géométriques et géographiques sont effectués et des informations sémantiques sont extraites. Enfin, les informations des étapes précédentes, ainsi que les résultats des techniques d’apprentissage automatique et des méthodes multicritères, sont intégrés au système et visualisés dans un environnement Web frontal capable d’exécuter et de visualiser des requêtes spatiales, permettant ainsi la gestion de trois processus. objets géoréférencés dimensionnels, leur récupération, transformation et visualisation, en tant que système d'aide à la décision. / The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this thesis we propose an approach to address the preceding challenges availed with machine learning techniques with the random forests classifier as its dominant method in a system that combines, blends and merges various types of data from different sources, encode them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier. The data are also forwarded to alternative classifiers and the results are appraised to confirm the prevalence of the proposed method. The data retrieved stem from a multitude of sources, e.g. open data providers and public organizations dealing with urban planning. Upon their retrieval and inspection at various levels (e.g. import, conversion, geospatial) they are appropriately converted to comply with the rules of the semantic model and the technical specifications of the corresponding subsystems. Geometrical and geographical calculations are performed and semantic information is extracted. Finally, the information from individual earlier stages along with the results from the machine learning techniques and the multicriteria methods are integrated into the system and visualized in a front-end web based environment able to execute and visualize spatial queries, allow the management of three-dimensional georeferenced objects, their retrieval, transformation and visualization, as a decision support system.

Le développement des systèmes d’information numériques et plus particulièrement les systèmes interactifs d’aide à la décision (SIAD) orientés données (Application Analytics) rencontre des échecs divers.La plupart des études montrent que ces échecs de projets de développement de SIAD relève de la phase d’analyse des besoins et des exigences. Les exigences qu'un système doit satisfaire sont insuffisamment définies à partir de besoins réels des utilisateurs finaux.D’un point de vue théorique, l’analyse de l’état de l’art, mais également du contexte industriel particulier, conduit donc à porter une attention particulière à cette phase et à élaborer une approche collaborative d’analyse des besoins et des exigences dirigée par les problèmes.Un système d’aide à la décision est avant tout un système d’aide à la résolution de problèmes et le développement de ce type d’artefact ne peut donc se faire sans avoir convenablement identifié en amont les problèmes de décision auxquels font face les utilisateurs décideurs, afin d’en déduire les exigences et le type de SIAD.Cette approche, par un renversement de la primauté implicite de la solution technique par rapport à la typologie des problèmes de décision, a été explicitée et mise en œuvre pour le développement d’une Application Analytics qui a permis d’atteindre l’objectif attendu : un système efficace et qui satisfasse d’un triple point de vue technique, fonctionnel et ergonomique, ses différents utilisateurs finaux. / The design of digital information systems, especially interactive Data-Driven Decision Support System (DSS) (Analytics Application) often misses its target.Most of studies have proven that the sources of most DSS design failures are rooted in the analysis step of the users’ needs and requirements a system has to meet and comply with. From a theoretical point of view, the analysis of the state of art combined with the analysis of specific industrial contexts, leads to focus on this critical step, and consequently to develop a collaborative problem-driven requirements engineering approach.A DSS, first and foremost, is a problem solving support system. It implies that developing such an artefact cannot be performed without an adequate upstream identification of end-users’ decision problems, prior to defining the decision makers’ requirements and the appropriate type of DSS.Characterized by the reversal of the implicit primacy of technical solution versus the typology of decision problems, this approach has been elaborated and implemented to design an Analytics Application. As a result, it allowed to reach the expected objective: An effective system that meets the different end-users’ expectations from a technical, functional and ergonomic standpoint.

IT-system är idag en del av i stort sett varje organisation. Tidigare forskning har visat attinförandet och användningen av nya IT-system påverkar organisationens struktur, aktiviteteroch aktörer och att mer forskning behövs för att förstå hur IT-systemen förändrarorganisatoriska processer. Äldreomsorgen är ett exempel på en kontext där mer forskningbehövs enligt tidigare forskning. Det blir allt vanligare att Sveriges kommuner implementerararbetssättet “Individens Behov I Centrum” (IBIC) i äldreomsorgen, vilket förändrar hurbedömning av individers behov utförs. Som stöd i arbetet med behovsbedömning i enlighetmed IBIC har flera av landets kommuner börjat använda behovsbedömningssystemet Kuben.Studien syftar till att bidra med ny kunskap om hur IT-system förändrar organisatoriskaprocesser. Med en abduktiv ansats och kvalitativa intervjuer som metod belyser vi i dennastudie hur Kuben förändrat behovsbedömningsprocessen utifrån en handläggares perspektiv.Med utgångspunkt i vår litteraturstudies tre förändringsområden visade resultatet exempelvisatt Kubens avsaknad av integrering med kommunernas verksamhetssystem leder till en ökadrisk för felaktiga behovsbedömningar. Samtidigt visade resultatet även att användning avKuben bland annat leder till en högre kvalitet i behovsbedömningen. Om förändringen ikvaliteten beror på användning av Kuben eller IBIC är dock svårt att avgöra eftersom Kubenoch IBIC i de flesta fall har införts samtidigt. Studiens slutsatser har bidragit tillrekommendationer för organisationer som avser att implementera behovsbedömningssystemsamt riktlinjer för framtida forskning. / IT systems are today part of virtually every organization. Previous research has shown thatthe introduction and use of new IT systems affects the structure, activities and actors of theorganization and that more research is needed to understand how IT systems changeorganizational processes. According to previous research the elderly care is an example of acontext where more research is needed. It is becoming increasingly common for Sweden'smunicipalities to implement the "Individens behov i centrum" approach (IBIC) in the elderlycare, which changes how assessment of individuals' needs is carried out. In support of thework on needs assessment in accordance with IBIC, several of the country's municipalitieshave started to use the needs assessment system Kuben. The aim of this study is to contributewith new knowledge about how IT systems change organizational processes. Using anabductive approach and qualitative interviews as a method, we illustrate how Kuben haschanged the needs assessment process from the perspective of a practitioner. On the basis ofthe three areas of change in our literature study, the results showed for example that Kubenslack of integration with the municipalities' ERP systems leads to an increased risk of incorrectneeds assessments. At the same time, the results also showed for example that the use ofKuben leads to higher quality in the needs assessment. However, whether the change inquality is due to the use of Kuben or IBIC is difficult to determine since in most cases Kubenand IBIC have been introduced simultaneously. The study's conclusions have contributed torecommendations for organizations that intend to implement needs assessment systems aswell as guidelines for future research.

Background: Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. Objective: With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. Methods: Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. Results: We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. Conclusions: AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

No description available.

The purpose of this research is to design, develop, evaluate, and demonstrate an open source gateway and mobile application for the SmartFarm open source decision support system to improve agricultural stewardship, environmental conservation, and provide farmers with a system that they own. There are very limited options for an open source gateway for collecting data on the farm. The options available are: expensive, require professional maintenance, are not portable between systems, improvements are made only by the manufacturer, limited in customization options, difficult to operate, and data is owned by the company rather than the farmer. The gateway is designed to send data to the cloud from remote SmartFarm Data Acquisition (DAQ) nodes, collect measurement data from remote SmartFarm DAQ nodes, provide a means of wirelessly programming remote SmartFarm DAQ nodes, and a tool that provides data analysis and insight from remote SmartFarm DAQ nodes. It is evaluated to work with 900MHz radios, SmartFarm DAQ nodes, and costs $35. Its setup takes 4 steps and ~20 minutes installation time, does not require maintenance, can utilize Wi-Fi, Bluetooth, and other wireless protocols, and software can port to other systems. The gateway measured data rate of 93.4Mbit/s internet upload speed, passing a range of 252 to 1592 bytes of data from a remote node to the cloud, consumes 2.8 Watts, with a software efficiency of 25% CPU usage, a measurement efficiency of 1 message every 15 seconds, can provide data analysis with the cloud service tool, and it can wirelessly program remote DAQ nodes. The goal of the mobile app is educating farmers, academia, and community members, of farming sustainably today, and for the future. The app is used as a tool to aid people in farming sustainably, teaching agricultural stewardship, and teaching environmental conservation. The app is evaluated with adaptation of 85.1%, frequency of use at 0.12 respondents/minute, and 22 respondents said they find the SmartFarm DSS as beneficial. By developing, evaluating, and demonstrating the gateway and mobile app, the SmartFarm decision support system is a viable option for improving agricultural stewardship and retaining farmers’ ownership of their data.

The increasing demand for healthcare services, coupled with the challenges of managing budgets and navigating complex regulations, has underscored the need for sustainable and efficient healthcare delivery. In response to this pressing issue, this thesis aims to optimize hospital efficiency using Artificial Intelligence (AI) techniques. The focus extends beyond improving surgical intraoperative time to encompass preoperative and postoperative periods as well. The research presents a novel Prescriptive Analytics System (PAS) designed to enhance the Surgical Success Rate (SSR) in surgeries and specifically in high volume arthroplasty. The SSR is a critical metric that reflects the successful completion of 4-surgeries during an 8-hour timeframe. By leveraging AI, the developed PAS has the potential to significantly improve the SSR from its current rate of 39% at The Ottawa Hospital to a remarkable 100%. The research is structured around five peer-reviewed journal papers, each addressing a specific aspect of the optimization of surgical efficiency. The first paper employs descriptive analytics to examine the factors influencing delays and overtime pay during surgeries. By identifying and analyzing these factors, insights are gained into the underlying causes of surgery inefficiencies. The second paper proposes three frameworks aimed at improving Operating Room (OR) throughput. These frameworks provide structured guidelines and strategies to enhance the overall efficiency of surgeries, encompassing preoperative, intraoperative, and postoperative stages. By streamlining the workflow and minimizing bottlenecks, the proposed frameworks have the potential to significantly optimize surgical operations. The third paper outlines a set of actions required to transform a selected predictive system into a prescriptive one. By integrating AI algorithms with decision support mechanisms, the system can offer actionable recommendations to surgeons during surgeries. This transformative step holds tremendous potential in enhancing surgical outcomes while reducing time. The fourth paper introduces a benchmarking and monitoring system for the selected framework that predicts SSR. Leveraging historical data, this system utilizes supervised machine learning algorithms to forecast the likelihood of successful outcomes based on various surgical team and procedural parameters. By providing real-time monitoring and predictive insights, surgeons can proactively address potential risks and improve decision-making during surgeries. Lastly, an application paper demonstrates the practical implementation of the prescriptive analytics system. The case study highlights how the system optimizes the allocation of resources and enables the scheduling of additional surgeries on days with a high predicted SSR. By leveraging the system's capabilities, hospitals can maximize their surgical capacity and improve overall patient care.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system. / Applied Science University (ASU) of Jordan