Data envelopment analysis (DEA) is a relative technical efficiency measurement tool, which uses operations research techniques to automatically calculate the weights assigned to the inputs and outputs of the production units being assessed. The actual input/output data values are then multiplied with the calculated weights to determine the efficiency scores. Recent variants of the DEA model impose upper and lower bounds on the weights to eliminate certain drawbacks associated with unrestricted weights. These variants are called weight restriction DEA models. Most weight restriction DEA models suffer from a drawback that the weight bound values are uncertain because they are determined based on either incomplete information or the subjective opinion of the decision-makers. Since the efficiency scores calculated by the DEA model are sensitive to the values of the bounds, the uncertainty of the bounds gets passed onto the efficiency scores. The uncertainty in the efficiency scores becomes unacceptable when we consider the fact that the DEA results are used for making important decisions like allocating funds and taking action against inefficient units. In order to minimize the effect of the uncertainty in bound values on the decision-making process, we propose to explicitly incorporate the uncertainty in the modeling process using the concepts of fuzzy set theory. Modeling the imprecision involves replacing the bound values by fuzzy numbers because fuzzy numbers can capture the intuitive conception of approximate numbers very well. Amongst the numerous types of weight restriction DEA models developed in the research, two are more commonly used in real-life applications compared to the others. Therefore, in this research, we focus on these two types of models for modeling the uncertainty in bound values. These are the absolute weight restriction DEA models and the Assurance Region (AR) DEA models. After developing the fuzzy models, we provide implementation roadmaps for illustrating the development and solution methodology of those models. We apply the fuzzy weight restriction models to the same data sets as those used by the corresponding crisp weight restriction models in the literature and compare the results using the two-sample paired t-test for means. We also use the fuzzy AR model developed in the research to measure the performance of a newspaper preprint insertion line. / Master of Science

In an era where use and dependence of information systems is significantly high, the threat of incidents related to information security that could jeopardize the information held by organizations is more and more serious. Alarming facts within the literature point to inadequacies in information security practices, particularly the evaluation of information security controls in organizations. Research efforts have resulted in various methodologies developed to deal with the information security controls assessment problem. A closer look at these traditional methodologies highlights various weaknesses that can prevent an effective information security controls assessment in organizations. This dissertation develops a methodology that addresses such weaknesses when evaluating information security controls in organizations. The methodology, created using the Fuzzy Logic Toolbox of MATLAB based on fuzzy theory and fuzzy logic, uses fuzzy set theory which allows for a more accurate assessment of imprecise criteria than traditional methodologies. It is argued and evidenced that evaluating information security controls using fuzzy set theory addresses existing weaknesses found in the literature for traditional evaluation methodologies and, thus, leads to a more thorough and precise assessment. This, in turn, results in a more effective selection of information security controls and enhanced information security in organizations. The main contribution of this research to the information security literature is the development of a fuzzy set theory-based assessment methodology that provides for a thorough evaluation of ISC in organizations. The methodology just created addresses the weaknesses or limitations identified in existing information security control assessment methodologies, resulting in an enhanced information security in organizations. The methodology can also be implemented in a spreadsheet or software tool, and promote usage in practical scenarios where highly complex methodologies for ISC selection are impractical. Moreover, the methodology fuses multiple evaluation criteria to provide a holistic view of the overall quality of information security controls, and it is easily extended to include additional evaluation criteria factor not considered within this dissertation. This is one of the most meaningful contributions from this dissertation. Finally, the methodology provides a mechanism to evaluate the quality of information security controls in various domains. Overall, the methodology presented in this dissertation proved to be a feasible technique for evaluating information security controls in organizations.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / David H. Ben-Arieh / Quest for continuous improvement among the organizations and issue of missing data for data analysis are never ending. This thesis brings these two topics under one roof, i.e., to evaluate the productivity of organizations with sparse data. This study focuses on Data Envelopment Analysis (DEA) to determine the efficiency of 41 member clinics of Kansas Association of Medically Underserved (KAMU) with missing data. The primary focus of this thesis is to develop new reliable methods to determine the missing values and to execute DEA. DEA is a linear programming methodology to evaluate relative technical efficiency of homogenous Decision Making Units, using multiple inputs and outputs. Effectiveness of DEA depends on the quality and quantity of data being used. DEA outcomes are susceptible to missing data, thus, creating a need to supplement sparse data in a reliable manner. Determining missing values more precisely improves the robustness of DEA methodology. Three methods to determine the missing values are proposed in this thesis based on three different platforms. First method named as Average Ratio Method (ARM) uses average value, of all the ratios between two variables. Second method is based on a modified Fuzzy C-Means Clustering algorithm, which can handle missing data. The issues associated with this clustering algorithm are resolved to improve its effectiveness. Third method is based on interval approach. Missing values are replaced by interval ranges estimated by experts. Crisp efficiency scores are identified in similar lines to how DEA determines efficiency scores using the best set of weights. There exists no unique way to evaluate the effectiveness of these methods. Effectiveness of these methods is tested by choosing a complete dataset and assuming varying levels of data as missing. Best set of recovered missing values, based on the above methods, serves as a source to execute DEA. Results show that the DEA efficiency scores generated with recovered values are close within close proximity to the actual efficiency scores that would be generated with the complete data. As a summary, this thesis provides an effective and practical approach for replacing missing values needed for DEA.

This dissertation is a study of artificial intelligence for diagnosing the condition of high voltage bushings. The techniques include neural networks, genetic algorithms, fuzzy set theory, particle swarm optimisation, multi-classifier systems, factor analysis, principal component analysis, multidimensional scaling, data-fusion techniques, automatic relevance determination and autoencoders. The classification is done using Dissolved Gas Analysis (DGA) data based on field experience together with criteria from IEEEc57.104 and IEC60599. A review of current literature showed that common methods for the diagnosis of bushings are: partial discharge, DGA, tan- (dielectric dissipation factor), water content in oil, dielectric strength of oil, acidity level (neutralisation value), visual analysis of sludge in suspension, colour of the oil, furanic content, degree of polymerisation (DP), strength of the insulating paper, interfacial tension or oxygen content tests. All the methods have limitations in terms of time and accuracy in decision making. The fact that making decisions using each of these methods individually is highly subjective, also the huge size of the data base of historical data, as well as the loss of skills due to retirement of experienced technical staff, highlights the need for an automated diagnosis tool that integrates information from the many sensors and recalls the historical decisions and learns from new information. Three classifiers that are compared in this analysis are radial basis functions (RBF), multiple layer perceptrons (MLP) and support vector machines (SVM). In this work 60699 bushings were classified based on ten criteria. Classification was done based on a majority vote. The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The work also proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The relevance and redundancy detection methods were able to prune the redundant measured variables and accurately diagnose the condition of the bushing with fewer variables. Experimental results from bushings that were evaluated in the field verified the simulations. The results of this work can help to develop real-time monitoring and decision making tools that combine information from chemical, electrical and mechanical measurements taken from bushings.

Predição de Links é uma área de pesquisa importante no contexto de Análise de Redes Sociais tendo em vista que predizer sua evolução é um mecanismo útil para melhorar e propiciar a comunicação entre usuários. Nas redes de coautoria isso pode ser utilizado para recomendação de usuários com interesses de pesquisa comuns. Este trabalho propõe um processo sistemático baseado em métricas não-dicotômicas para avaliação de predição de links em redes de coautoria, sendo considerada a definição de métodos para as seguintes tarefas identificadas: seleção de dados, determinação de novos links e avaliação dos resultados. Para seleção de dados definiu-se um sensor fuzzy baseado em atributos dos nós. O uso de composições fuzzy foi considerado para determinação de novos links _ponderados_ entre dois autores, adotando-se não apenas atributos dos nós, mas também a combinação de atributos de outros links observados. O link ponderado é denominado _qualidade da relação_ e é obtido pelo uso de propriedades estruturais da rede. Para avaliação dos resultados foi proposta a curva ROC fuzzy, que permite explorar os pesos dos links não apenas para ordenação dos exemplos. / Link prediction is an important research line in the Social Network Analysis context, as predicting the evolution of such nets is a useful mechanism to improve and encourage communication among users. In co-authorship networks, it can be used for recommending users with common research interests. This work proposes a systematic process based on non-dichotomic metrics for evaluation of link prediction in co-authorship networks considering the definition of methods for the following tasks: data selection, new link determination and result evaluation. Fuzzy sensor based on node attributes is adopted for data selection. Fuzzy compositions are used to predict new link weights between two authors, adopting not only attributes nodes, but also the combination of attributes of other observed links. The link weight called _relation quality_ is obtained by using structural features of the social network. The fuzzy roc curve is used for results evaluation, allowing us to consider the weights of the links and not only the ordering of examples.

The percentage of shop fabrication, including pipe spool fabrication, has been increasing on industrial construction projects during the past years. Industrial fabrication has a great impact on construction projects due to the fact that the productivity is higher in a controlled environment than in the field, and therefore time and cost of construction projects are reduced by making use of industrial fabrication. Effective planning and scheduling of the industrial fabrication processes is important for the success of construction projects. This thesis focuses on developing a new framework for optimizing shop scheduling, particularly pipe spool fabrication shop scheduling. The proposed framework makes it possible to capture uncertainty of the pipe spool fabrication shop while accounting for linguistic vagueness of the decision makers preferences using simulation modeling and fuzzy set theory. The implementation of the proposed framework is discussed using a real case study of a pipe spool fabrication shop. In this thesis, first, a simulation based scheduling framework is presented based on the integration of relational database management system, product modeling, process modeling, and heuristic approaches. Next, a framework for optimization of the industrial shop scheduling with respect to multiple criteria is proposed. Fuzzy set theory is used to linguistically assess different levels of satisfaction for the selected criteria. Additionally, an executable scheduling toolkit is introduced as a decision support system for pipe spool fabrication shop. / Construction Engineering and Management

Real-world problems especially the ones that involve natural systems are complex and they are composed of many non-deterministic components. Uncertainties associated with these non-deterministic components may originate from randomness or from imprecision due to lack of information. Until recently, uncertainty, regardless of its nature or source has been treated by probability concepts. However, uncertainties associated with real-world systems are not limited to randomness. Imprecise, vague or incomplete information may better be represented by other mathematical tools, such as fuzzy set theory, possibility theory, belief functions, etc. New approaches which allow utilization of probability theory in combination with these new mathematical tools found applications in various engineering fields. Uncertainty modeling in human health risk assessment and groundwater resources management areas are investigated in this thesis. In the first part of this thesis two new approaches which utilize both probability theory and fuzzy set theory concepts to treat parameter uncertainties in carcinogenic risk assessment are proposed. As a result of these approaches fuzzy health risks are generated. For the fuzzy risk to be useful for practical purposes its acceptability with respect to compliance guideline has to be evaluated. A new fuzzy measure, the risk tolerance measure, is proposed for this purpose. The risk tolerance measure is a weighed average of the possibility and the necessity measures which are currently used for decision making purposes. In the second part of this thesis two decision making frameworks are proposed to determine the best groundwater resources management strategy in the Savannah region, Georgia. Groundwater resources management problems, especially ones in the coastal areas are complex and require treatment of various uncertain inputs. The first decision making framework proposed in this study is composed of a coupled simulation-optimization model followed by a fuzzy multi-objective decision making approach while the second framework includes a groundwater flow model in which the parameters of the flow equation are characterized by fuzzy numbers and a decision making approach which utilizes the risk tolerance measure proposed in the first part of this thesis.

The research focuses on the application of site selection for cage aquaculture in Taiwan by developing the site evaluation DSS (Decision Support System). The modeling aspect of the system belongs to the domain of multi-criteria decision theories, which AHP (Analytic Hierarchy Process) and Fuzzy Set theory were used. Two case studies based on real world and hypothetical data were conducted to verify the integrity of the system. According to the literature review and the interview with several domain experts, various impact factors were identified first. The corresponding weights of each factor were then decided by analyzing the questionnaires designed based on the concept of AHP. The following work was to evaluate those impact factors based on the experience of domain experts using some appropriate approaches. To represent the domain knowledge, it is appropriate to use rule based inference system. Besides, fuzzy set theory was chosen to describe the antecedent and consequence of the rule base due to the considerations of uncertainty from human experts and ocean field data. Several related mythologies derived from the fuzzy set theory were used, such as the operation of fuzzy composition, determination of suitable membership function, fuzzy relationship matrix, fuzzy inference, defuzzification, and fuzzy pattern classification. All impact factors were categorized into three different types of membership functions that were designed specifically for the site selection of cage aquaculture. The consequence in the rule base, which is the site suitability, was also represented as the unique membership function. To calculate the fuzzy relationship matrix, the current research found that the operation of ¡§algebraic product and bounder sum¡¨ would produce better results than the commonly used ¡§max-min¡¨ operation. Each impact factor would have the associated fuzzy relationship matrix derived from the rule base. The site suitability in term of a fuzzy set can then be inferred by the fuzzy composition of current situation of the factor and the relationship matrix. By multiplying the AHP weight and the fuzzy suitability, the final site suitability index, taking all the impact factors into consideration, can therefore be derived. The real data in Feng-Gang, located in the southern Taiwan, were collected and evaluated using the site selection DSS. The results show Feng-Gang is suitable for the development of cage aquaculture, which is validated by the current prosperous business locally in cage aquaculture. As for the evaluation of multiple sites, 18 hypothetical sites near shore around Taiwan were chosen to calculate the corresponding suitability indexes, which were then be partitioned into several groups using the fuzzy pattern classification. Based on the results, the sites that were classified in the same group have similar cultivation conditions, which also proves the applicability of the site evaluation DSS.

This dissertation provides experimental evidence for the validity of an intonational phonology. The widely used Autosegmental-Metrical theory con- tends that the phonological structure of intonation can be expressed with two tonal targets (L/H tones and derivatives) and retrieved from its phonetic im- plementations. However, it has not been specifically demonstrated so far in a systematic way. This dissertation argues that this view on intonational phonol- ogy considers the phonetic forms of intonation as instances of phonologically structured intonational units forming functionally discrete categories (tones and derivatives). The model of Pattern Recognition for Intonation (PRInt) applies the concepts of categorization (vagueness, prototype, degrees of typicality) to in- tonation in order to abstract the phonological structure of intonational cate- gories from the ranking, by degree of typicality, of their variations in phonetic implementation. First, instances belonging to an intonation category are collected. Sec- ond, a pattern recognition module, relying on the 4-layer structure protocol, extracts a feature vector from the phonetic data of each instance: a sequence of structurally organized tones (L/H tones and derivatives). Third, a fuzzy classifier, using two functions (frequency and similar- ity), organizes the data from the feature vectors of all instances by degree of typicality (grade of membership of values in multisets) and generates the phonological structure of the intonation category, the prototypical pattern, ex- tracted from all instances, and that subsumes them all. It also re-creates the phonetic implementations of the phonological structure but with their features ranked by degree of typicality. This allows the model to distinguish phono- logically distinct structures from phonetic variations of the same phonological structure. The model successfully extracted the phonological intonation structure associated to three modalities of closed questions in French: neutral, doubt- ful, and surprised. It found that neutral and doubtful closed questions are phonologically distinct while surprise is a phonetic allocontour of the neutral modality, in line with prior characterizations of these patterns. It demon- strated that a bi-tonal phonological structure of intonation can be retrieved from phonetic variations. A versatile modeling tool, PRInt will be developed to use its acquired knowledge to evaluate the categorical status of novel instances and to extract multiple phonological units from mixed corpora. / text

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