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.

The ubiquity of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to their widespread adoption across diverse domains like education, self-driving cars, healthcare, and more. AI is making its way into the industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation, driven by challenges such as water demand forecasting, wastewater treatment, asset maintenance and management, and water quality assessment. Water utilities are at different stages in their journey of digital transformation, and its decision-makers, who are non-expert stakeholders in AI applications, must understand the technology to make informed decisions. The non-expert stakeholders should know that while AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration, and ethical concerns that should be considered before implementing it for real-world applications. Civil engineering is a licensed profession where critical decision-making is involved. Failure of critical decisions by civil engineers may put their license at risk, and therefore trust in any decision-support technology is crucial for its acceptance in real-world applications. This research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) to facilitate the successful application of AI in the water sector. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities are at an early stage of implementing AI as they face concerns regarding the blackbox nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation. / Master of Science / The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) in various industries like education, self-driving cars, healthcare, and more has spurred interest in its potential application in the water sector. As the water sector undergoes a digital transformation to address challenges such as water demand forecasting, wastewater treatment, asset management, and water quality assessment, water utilities need to understand the benefits and challenges of AI technology. Automating water sector operations through AI involves high risk as it has a huge ecological, economic, and sociological impact on society. Water utilities are non-expert end users of AI and they should be aware of its challenges such as data management, model development, domain knowledge integration, and ethical concerns when implementing AI for real-world applications. To address these challenges, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) to help water utilities successfully apply AI technology in their system. We conduct pilot interviews and surveys with small, medium, and large water utilities across the United States to capture their current AI practices and challenges. The research results led us to find that water utilities are still at an early stage of adopting AI in their system and are faced with issues such as blackbox nature of the technology, its trustworthiness for real-world application, and sustainability at the utilities. We believe that aiWATERS will serve as a relevant guide for water utilities and will help them overcome current AI-based challenges.

The study investigated the feasibility of the Ottawa decision support tool to assist HIV positive mothers' infant feeding choice. The aim was to explore and describe the feasibility of the Ottawa Decision Support Tool (ODST) in counselling HIV infected pregnant women on decision-making regarding the choice of safe infant feeding. The finding of this study will assist and support HIV positive mothers to be independent decision makers in choosing an infant feeding option for their babies. A descriptive qualitative research approach guided the researcher to explore and describe the feasibility of the ODST to assist HIV positive mothers' infant feeding choice. This study is based on the Ottawa decision support framework (ODSF). Three focus group that comprised midwives as participants were conducted. The first focus group was conducted in January 2013 and the two subsequent ones in August 2013. Data was analysed using a framework approach. The following themes emerged from data-analysis: • Appropriateness • Receptiveness of intervention • Effectiveness Conclusions were drawn based on the attained objectives of the study. The overall conclusion was that the ODST is feasible to assist HIV positive mothers' infant feeding choice. Limitations of the study were identified and recommendations were made for nursing practice, nursing education and further research. / MCur, North-West University, Potchefstroom Campus, 2015

The study investigated the feasibility of the Ottawa decision support tool to assist HIV positive mothers' infant feeding choice. The aim was to explore and describe the feasibility of the Ottawa Decision Support Tool (ODST) in counselling HIV infected pregnant women on decision-making regarding the choice of safe infant feeding. The finding of this study will assist and support HIV positive mothers to be independent decision makers in choosing an infant feeding option for their babies. A descriptive qualitative research approach guided the researcher to explore and describe the feasibility of the ODST to assist HIV positive mothers' infant feeding choice. This study is based on the Ottawa decision support framework (ODSF). Three focus group that comprised midwives as participants were conducted. The first focus group was conducted in January 2013 and the two subsequent ones in August 2013. Data was analysed using a framework approach. The following themes emerged from data-analysis: • Appropriateness • Receptiveness of intervention • Effectiveness Conclusions were drawn based on the attained objectives of the study. The overall conclusion was that the ODST is feasible to assist HIV positive mothers' infant feeding choice. Limitations of the study were identified and recommendations were made for nursing practice, nursing education and further research. / MCur, North-West University, Potchefstroom Campus, 2015

The objectives of this research are threefold: (i) to develop a conceptual framework and a prototype in order to extend the application capability of a category of multiple objective decision support systems (MODSS) techniques; (ii) to explore the combined functionalities of knowledge-based expert systems (ES) and MODSS through embedding an intelligent front-end, and (iii) to develop a new system and process of dealing with multiple objective decision making (MODM) models in a group decision support system (GDSS) framework. Ultimately, a system that integrates MODSS, ES and GDSS is generated, which is then evaluated in a laboratory experimental setup. This integrated system contains a sufficient number of MODM methods to solve MODM problems, provides an ES-based guide to select and use the most suitable MODM method, and has the capability to aggregate individual decision makers' preferences to produce a compromise solution of an MODM problem in different forms and styles of group meetings. The system is supported by a set of group decision making (GDM) methods which combine the preferences of the individual group members and thus increases the confidence of each group member in the compromise solution.The research is conducted using a multiple-methodologies approach using the system development methodology as the backbone. The conceptual framework of the integrated system is elaborated to integrate multiple system elements into one facility at the application system level based on functional and resource integration. A prototype implements this conceptual framework as an intelligence-based and graphical user interface (GUI)-based MODSS that works in an individual/group environment. Both the conceptual framework and the prototype are called Intelligent Multiple Objectives Group Decision Support Systems (IMOGDSS).Initial evaluation of the IMOGDSS is encouraging, which ++ / is conducted in the form of testing a number of hypotheses in an experimental setup. This research thus makes contributions in both theoretical and application domains. Five major contributions are listed below:It develops a unique conceptual framework of integrating MODSS, ES and GDSS effectively to deal with MODM problem in individual/group decision making under a knowledge-based intelligent architecture.It provides a new application of ES, that is, utilising knowledge-based ES to select the most efficient MODM method for each particular decision maker (or decision group) in a particular decision problem.The complete method management function of the MODM methodology base guides the decision makers to use the most suitable method to solve their decision making problems, allows them to use multiple methods to resolve complex problems, that could not otherwise be solved with a single MODM, and also allows the group members to get solutions from different methods.This study produces an opportunity to select and apply the 'best' aggregation model to aggregate the individual solutions of an MODM problem through integrating various GDM methods in a methodology base.This study implements a two-stage configuration of group decision support software that provides a GUI-based hierarchical procedure for solving MODM problems with intelligent guidance in a decision group. The two-stage group decision making procedure is able to help the decision makers to analyse, understand and interact cooperatively in the group decision making process to reach a compromise solution.

Complex decision-making is a prominent aspect of requirements engineering (RE) and the need for improved decision support for RE decision-makers has been identified by a number of authors in the research literature. The fundamental viewpoint that permeates this thesis is that RE decision-making can be substantially improved by RE decision support systems (REDSS) based on the actual needs of RE decision-makers as well as the actual generic human decision-making activities that take place in the RE decision processes. Thus, a first step toward better decision support in requirements engineering is to understand complex decision situations of decision-makers. In order to gain a holistic view of the decision situation from a decision-maker’s perspective, a decision situation framework has been created. The framework evolved through an analysis of decision support systems literature and decision-making theories. The decision situation of RE decision-makers has been studied at a systems engineering company and is depicted in this thesis. These situations are described in terms of, for example, RE decision matters, RE decision-making activities, and RE decision processes. Factors that affect RE decision-makers are also identified. Each factor consists of problems and difficulties. Based on the empirical findings, a number of desirable characteristics of a visionary REDSS are suggested. Examples of characteristics are to reduce the cognitive load, to support creativity and idea generation, and to support decision communication. One or more guiding principles are proposed for each characteristic and available techniques are described. The purpose of the principles and techniques is to direct further efforts concerning how to find a solution that can fulfil the characteristic. Our contributions are intended to serve as a road map that can direct the efforts of researchers addressing RE decision-making and RE decision support problems. Our intention is to widen the scope and provide new lines of thought about how decision-making in RE can be supported and improved.

Thesis (Ph. D.)--University of Missouri--Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 104-114).

Thesis (doctoral)--Rijksuniversiteit Groningen, 1992. / Includes bibliographical references (p. 223-230).

Thesis (M.H.A.)--University of Michigan, 1975.

Thesis (M.H.A.)--University of Michigan, 1975.