The scarcity of freshwater resources in the Sultanate of Oman, makes it essential that both surface and groundwater resources are carefully managed. Introducing new water demand management tools is important, especially for the coastal agricultural areas (e. g. Al Batinah coastal region) which are affected by sea water intrusion. Based on a social survey performed during this work, the existing situation generates conflicts between different stakeholders (SHs) which have different interests regarding water availability, sustainable aquifer management, and profitable agricultural production. The current aim is to evaluate the implementation potential of several management interventions and their combinations by analysing opinions and responses of the relevant stakeholders in the region. Influencing the behavior and drivers affecting farmers’ decision-making manner, can be a valuable tool to improve water demand management. The work also introduces the use of a participatory process within the frame of an integrated water resources management (IWRM) to support decision makers in taking better informed decisions. Data were collected by questionnaires from different groups of stakeholders. These data were analysed statistically for each group separately as well as relations amongst groups by using the SPSS (Statistical Package for Social Science) software package. Differences were examined between opinions of farmers and decision makers (DM’s) regarding potential interventions. Farmers’ frequency curves showed differences in opinions in some interventions, while differences in opinions were not so high within the group of DM’s. Therefore, Cross Tabulation and Discriminant Analysis (DA) were performed to identify the drivers influencing farmers’ opinions regarding the intervention measures. As an advanced step, a Bayesian Networks (BNs) approach is used for mapping stakeholders’ behaviors and to show the strength of a relationship between dependent and predictor variables. By using BNs it is possible to analyse future scenarios for implementation and acceptance of interventions.

Este trabalho fornece uma avaliação empírica do desempenho de Redes Bayesianas quando se impõe restrições à largura de árvore de sua estrutura. O desempenho da rede é visto especificamente pela sua capacidade de generalização e também pela precisão da inferência em problemas de tomada de decisão. Resultados preliminares sugerem que adicionar essa restrição na largura de árvore diminui a capacidade de generalização do modelo além de tornar a tarefa de aprendizado mais difícil. / This work provides an empirical evaluation of the performance of Bayesian Networks when treewidth is bounded. The performance of the network is viewed as its generalizability and also as the accuracy of inference in decision making problems. Preliminary results suggest that adding constraints to treewidth decreases the model performance on unseen data and makes the corresponding optimization problem more difficult.

Este trabalho fornece uma avaliação empírica do desempenho de Redes Bayesianas quando se impõe restrições à largura de árvore de sua estrutura. O desempenho da rede é visto especificamente pela sua capacidade de generalização e também pela precisão da inferência em problemas de tomada de decisão. Resultados preliminares sugerem que adicionar essa restrição na largura de árvore diminui a capacidade de generalização do modelo além de tornar a tarefa de aprendizado mais difícil. / This work provides an empirical evaluation of the performance of Bayesian Networks when treewidth is bounded. The performance of the network is viewed as its generalizability and also as the accuracy of inference in decision making problems. Preliminary results suggest that adding constraints to treewidth decreases the model performance on unseen data and makes the corresponding optimization problem more difficult.

Thesis (MSc (Applied mathematics))--University of Stellenbosch, 2011. / Includes bibliography. / Please refer to full text to view abstract.

An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. The recent substantial increase in the availability of such data has stimulated the interest in inferring the networks and pathways from the data themselves. The main interests of this thesis are the application, evaluation and the improvement of machine learning methods applied to the reverse engineering of biochemical pathways and networks. The thesis starts with the application of an established method to newly available gene expression data related to the interferon pathway of the human immune system in order to identify active subpathways under di erent experimental conditions. The thesis continues with the comparative evaluation of various machine learning methods (Relevance networks, Graphical Gaussian Models, Bayesian networks) using observational and interventional data from cytometry experiments as well as simulated data from a gold-standard network. The thesis also extends and improves existing methods to include biological prior knowledge under the Bayesian approach in order to increase the accuracy of the predicted networks and it quanti es to what extent the reconstruction accuracy can be improved in this way.

Vehicle safety and road safety are two important issues. They are related to each other and road accidents are mostly caused by driver distraction. Issues related to driver distraction like eating, drinking, talking to a passenger, using IVIS (In-Vehicle Information System) and thinking something unrelated to driving are some of the main reasons for road accidents. Driver distraction can be categorized into 3 different types: visual distraction, manual distraction and cognitive distraction. Visual distraction is when driver's eyes are off the road and manual distraction is when the driver takes one or both hands off the steering wheel and places the hand/s on something that is not related to the driving safety. Cognitive distraction whereas happens when a driver's mind is not on the road. It has been found that cognitive distraction is the most dangerous among the three because the thinking process can induce a driver to view and/or handle something unrelated to the safety information while driving a vehicle. This study proposes a physiological measurement to detect driver cognitive distraction. Features like lips, eyebrows, mouth movement, eye movement, gaze rotation, head rotation and blinking frequency are used for the purpose. Three different sets of experiments were conducted. The first experiment was conducted in a lab with faceLAB cameras and served as a pilot study to determine the correlation between mouth movement and eye movement during cognitive distraction. The second experiment was conducted in a real traffic environment using faceAPI cameras to detect movement on lips and eyebrows. The third experiment was also conducted in a real traffic environment. However, both faceLAB and faceAPI toolkits were combined to capture more features. A reliable and stable classification algorithm called Dynamic Bayesian Network (DBN) was used as the main algorithm for analysis. A few more others algorithms like Support Vector Machine (SVM), Logistic Regression (LR), AdaBoost and Static Bayesian Network (SBN) were also used for comparison. Results showed that DBN is the best algorithm for driver cognitive distraction detection. Finally a comparison was also made to evaluate results from this study and those by other researchers. Experimental results showed that lips and eyebrows used in this study are strongly correlated and have a significant role in improving cognitive distraction detection.

Στην παρούσα διπλωματική εργασία πραγματευόμαστε το θέμα της χρήσης των Bayesian Δικτύων -και γενικότερα των Πιθανοτικών Γραφικών Μοντέλων - στη Μηχανική Μάθηση. Στα πρώτα κεφάλαια της εργασίας αυτής παρουσιάζουμε συνοπτικά τη θεωρητική θεμελίωση αυτών των δομημένων πιθανοτικών μοντέλων, η οποία απαρτίζεται από τις βασικές φάσεις της αναπαράστασης, επαγωγής συμπερασμάτων, λήψης αποφάσεων και εκμάθησης από τα διαθέσιμα δεδομένα. Στα επόμενα κεφάλαια, εξετάζουμε ένα ευρύ φάσμα εφαρμογών των πιθανοτικών γραφικών μοντέλων και παρουσιάζουμε τα αποτελέσματα των εξομοιώσεων που υλοποιήσαμε. Συγκεκριμένα, αρχικά με χρήση γράφων ορίζονται τα Bayesian δίκτυα, Markov δίκτυα και Factor Graphs. Έπειτα, παρουσιάζονται οι αλγόριθμοι επαγωγής συμπερασμάτων που επιτρέπουν τον απευθείας υπολογισμό πιθανοτικών κατανομών από τους γράφους. Διευκολύνεται η λήψη αποφάσεων υπό αβεβαιότητα με τα δέντρα αποφάσεων και τα Influence διαγράμματα. Ακολούθως, μελετάται η εκμάθηση της δομής και των παραμέτρων των πιθανοτικών γραφικών μοντέλων σε παρουσία πλήρους ή μερικού συνόλου δεδομένων. Τέλος, παρουσιάζονται εκτενώς σενάρια τα οποία καταδεικνύουν την εκφραστική δύναμη, την ευελιξία και τη χρηστικότητα των Πιθανοτικών Γραφικών Μοντέλων σε εφαρμογές του πραγματικού κόσμου. / The main subject of this diploma thesis is how probabilistic graphical models can be used in a wide range of real-world scenarios. In the first chapters, we have presented in a concise way the theoretical foundations of graphical models, which consists of the deeply related phases of representation, inference, decision theory and learning from data. In the next chapters, we have worked on many applications, from Optical Character Recognition to Recoginizing Actions and we have presented the results from the simulations.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: In this thesis we present GrMPy, a library of classes and functions implemented in Python, designed for implementing graphical models. GrMPy supports both undirected and directed models, exact and approximate probabilistic inference, and parameter estimation from complete and incomplete data. In this thesis we outline the necessary theory required to understand the tools implemented within GrMPy as well as provide pseudo-code algorithms that illustrate how GrMPy is implemented. / AFRIKAANSE OPSOMMING: In hierdie verhandeling bied ons GrMPy aan,'n biblioteek van klasse en funksies wat Python geim- plimenteer word en ontwerp is vir die implimentering van grafiese modelle. GrMPy ondersteun beide gerigte en ongerigte modelle, presies eenbenaderde moontlike gevolgtrekkings en parameterskat- tings van volledige en onvolledige inligting. In hierdie verhandeling beskryf ons die nodige teorie wat benodig word om die hulpmiddels wat binne GrMPy geimplimenteer word te verstaan sowel as die pseudo-kodealgoritmes wat illustreer hoe GrMPy geimplimenteer is.

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken. The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time. The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner. The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

Mobile Ad hoc Networks (MANETs) are becoming increasingly important because of their unique characteristics of connectivity. Several delay sensitive applications are starting to appear in these kinds of networks. Therefore, an issue in concern is to guarantee Quality of Service (QoS) in such constantly changing communication environment. The classical QoS aware solutions that have been used till now in the wired and infrastructure wireless networks are unable to achieve the necessary performance in the MANETs. The specialized protocols designed for multihop ad hoc networks offer basic connectivity with limited delay awareness and the mobility factor in the MANETs makes them even more unsuitable for use. Several protocols and solutions have been emerging in almost every layer in the protocol stack. The majority of the research efforts agree on the fact that in such dynamic environment in order to optimize the performance of the protocols, there is the need for additional information about the status of the network to be available. Hence, many cross layer design approaches appeared in the scene. Cross layer design has major advantages and the necessity to utilize such a design is definite. However, cross layer design conceals risks like architecture instability and design inflexibility. The aggressive use of cross layer design results in excessive increase of the cost of deployment and complicates both maintenance and upgrade of the network. The use of autonomous protocols like bio-inspired mechanisms and algorithms that are resilient on cross layer information unavailability, are able to reduce the dependence on cross layer design. In addition, properties like the prediction of the dynamic conditions and the adaptation to them are quite important characteristics. The design of a routing decision algorithm based on Bayesian Inference for the prediction of the path quality is proposed here. The accurate prediction capabilities and the efficient use of the plethora of cross layer information are presented. Furthermore, an adaptive mechanism based on the Genetic Algorithm (GA) is used to control the flow of the data in the transport layer. The aforementioned flow control mechanism inherits GA’s optimization capabilities without the need of knowing any details about the network conditions, thus, reducing the cross layer information dependence. Finally, is illustrated how Bayesian Inference can be used to suggest configuration parameter values to the other protocols in different layers in order to improve their performance.