The purpose of this study was to explore the subject of timescale estimating for rule-based systems. A model for estimating the timescale necessary to build rule-based systems was built and then tested in a controlled environment.

Knowledge in organizations today is considered as one of the most important assets the organization possesses. A considerable part of this knowledge is the knowledge possessed by the individuals employed by the organization. In order for intelligent systems to perform some of the tasks their human counter parts perform in an organization the intelligent systems need to acquire the knowledge their human counter parts possess for the specific task. To develop an intelligent system that can perform a specific task in an organization, the knowledge needed to perform the task will have to be extracted from the individuals in the organization via knowledge acquisition. This knowledge will then be presented so that the intelligent system can understand it and perform the task. In order to develop an intelligent system an ontology representing the domain under consideration as well as the rules that constitute the reasoning behind the intelligent system needs to be developed. In this dissertation a development environment for developing intelligent systems called the Collaborative Ontology Builder for Reasoning and Analysis (COBRA) was developed. COBRA provides a development environment for developing the ontology and rules for an intelligent system. COBRA was used in this study to develop a Cellular telecommunications Network Consistency Checking Intelligent System (CNCCIS), which was implemented in a cellular telecommunications network. / Prof. E.M. Ehlers

A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering / A multi-layered expert system shell that specifically addresses real-time issues is designed and implemented. The architecture of this expert system shell supports the concepts of parallelism, concurrent computation and competitive reasoning in that it allows several alternatives to be explored simultaneously. An inference engine driven by a hybrid of forward and backward chanining methods is used to achieve real-time response, and certainty factors are used for uncertainty management. Real-time responsiveness is improved by allowing the coexistence of procedural and declarative knowledge within the same system. A test bed that was set up in order to investigate the performance of the implemented shell is described. It was found in the performance analysis that the proposed system meets the real-time requirements as specified in this research. / Andrew Chakane 2018

Cloud Computing is a new computing model consists of a large pool of hardware and software resources on remote datacenters that are accessed through the Internet. Cloud Computing faces significant obstacles to its acceptance, such as security, virtualization, and lack of standardization. For Cloud standards, there is a long debate about their role, and more demands for Cloud standards are put on the table. The Cloud standardization landscape is so ambiguous. To model and analyze security standards for Cloud Computing and web services, we have surveyed Cloud standards focusing more on the standards for security, and we classified them by groups of interests. Cloud Computing leverages a number of technologies such as: Web 2.0, virtualization, and Service Oriented Architecture (SOA). SOA uses web services to facilitate the creation of SOA systems by adopting different technologies despite their differences in formats and protocols. Several committees such as W3C and OASIS are developing standards for web services; their standards are rather complex and verbose. We have expressed web services security standards as patterns to make it easy for designers and users to understand their key points. We have written two patterns for two web services standards; WS-Secure Conversation, and WS-Federation. This completed an earlier work we have done on web services standards. We showed relationships between web services security standards and used them to solve major Cloud security issues, such as, authorization and access control, trust, and identity management. Close to web services, we investigated Business Process Execution Language (BPEL), and we addressed security considerations in BPEL and how to enforce them. To see how Cloud vendors look at web services standards, we took Amazon Web Services (AWS) as a case-study. By reviewing AWS documentations, web services security standards are barely mentioned. We highlighted some areas where web services security standards could solve some AWS limitations, and improve AWS security process. Finally, we studied the security guidance of two major Cloud-developing organizations, CSA and NIST. Both missed the quality of attributes offered by web services security standards. We expanded their work and added benefits of adopting web services security standards in securing the Cloud. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2013.

O trabalho apresenta o protótipo de um conjunto de sistemas especialistas para o auxílio na operação, monitoração e a avaliação do desempenho da manutenção de uma subestação de energia. Os sistemas especialistas propostos têm como objetivos básicos: o tratamento de alarmes da subestação, para depuração e triagem dos mesmos durante perturbações facilitando a análise e ações do operador; a localização de defeitos através da iteração entre alarmes gerados (fatos), regras que relacionam alarmes a causas (base de conhecimento) e a estrutura que relaciona este conjunto de regras (máquina de inferência); a reconfiguração da subestação para transferência de cargas entre circuitos através de um conjunto de manobras gerados por algoritmos de busca e de regras ligadas aos limites das grandezas elétricas dos equipamentos da rede; e ainda a análise do desempenho da manutenção baseada no conceito de índice de mérito operativo aplicado a subestações. O protótipo prevê o desenvolvimento utilizando a linguagem PROLOG, que é voltada para o tratamento declarativo de informações. Foram utilizados dados de uma subestação de uma empresa concessionária de energia para que se pudesse avaliar melhor o desenvolvimento das bases de conhecimento e da própria arquitetura do conjunto e de sua comunicação. Os testes efetuados mostraram resultados promissores e com grau de acerto elevado, o que indica que o sistema desenvolvido, é um embrião confiável para maiores sofisticações. O trabalho termina por apresentar estas novas possibilidades de aperfeiçoamento do sistema. / This work presents a set of expert systems for operation , supervision and maintenance of a electrical energy substation. The function performed by the expert systems presented here are: alarm processing, fault diagnosis, reconfiguration and the analysis of the maintenance performance. The system was developed in PROLOG language. Substation characteristics, data and information has been provided by a São Paulo utility company. The knowledge basis for the expert systems was developed with basis in such information. Tests were carried out to verify this system performance including the architecture and communication. The results are very promising and with a high level of confidence, which means that this system can be used as a seed for future improvement. The work ends by suggesting such improvements and new applications.

O processo de aquisição do conhecimento é uma das principais etapas de desenvolvimento de um sistema especialista e é considerado como um dos estágios mais difíceis. Essa dificuldade se dá em virtude da inexistência de uma metodologia eficiente, confiável e padrão para extração e organização do conhecimento das várias fontes. O método apresentado neste trabalho é uma alternativa que pode ser empregada para adquirir o conhecimento para desenvolver sistemas especialistas para diagnóstico de falhas em diferentes áreas da indústria. Este trabalho apresenta um método que integra as técnicas de confiabilidade e risco, tais como, Análise de Modos e Efeitos de Falha (FMEA), Análise de Árvore de falhas (FTA) e Estudo de Perigo e Operabilidade (HAZOP) para aquisição do conhecimento para o diagnóstico de falhas. O método também permite estimar a periocidade da manutenção preventiva aplicando os conceitos de manutenção imperfeita e teoria de decisão multicritério. O método utilizada técnicas empregadas em análise de confiabilidade e risco para determinar a relação entre efeito da falha em um sistema e as suas causas raiz com o objetivo de estabelecer um procedimento estruturado para aquisição do conhecimento associado à relação causa-efeito em um sistema. O método foi validado com a comparação do histórico de falhas de um sistema hidráulico de uma usina hidrelétrica e, considerando-se que os eventos definidos como causa raiz registrados no histórico de falhas foram encontrados como resultados da análise pelo sistema especialista, tem-se a validação. O método para determinar a periocidade da manutenção preventiva foi validado com os resultados de artigos e com os planos de manutenção da usina. / The process of knowledge acquisition is a major step in developing an expert system and is considered as one of the most difficult stages. This difficulty is due to the lack of an efficient, reliable and standard methodology for extraction and organization of knowledge from various sources. The method presented in this thesis is an alternative that can be used to acquire the knowledge to develop expert systems for fault diagnosis in different areas of industry. This thesis presents a method that integrates risk and reliability analysis techniques such as Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA) and Hazard and Operability Study (HAZOP) for the acquisition of knowledge to fault diagnosis. The method also allows estimating the optimal intervention times of preventive maintenance by applying the imperfect maintenance and multicriteria concepts. The method uses techniques that are employed in reliability and risk analysis to determine the relationship between fault effect in the system and its root causes in order to establish a structured acquisition of knowledge associated with the causeeffect relationship in a system procedure. The method was validated by comparing the failure database related to a hydropower plant hydraulic system and, considering that the events defined as root causes recorded in the failure database were found by expert system, the method was validated. The method for determining the optimal intervention time for preventive maintenance has been validated with the results of articles and maintenance plans of the plant.

For an 8-bit grayscale image patch of size n x n, the number of distinguishable signals is 256(n2). Natural images (e.g.,photographs of a natural scene) comprise a very small subset of these possible signals. Traditional image and video processing relies on band-limited or low-pass signal models. In contrast, we will explore the observation that most signals of interest are sparse, i.e. in a particular basis most of the expansion coefficients will be zero. Recent developments in sparse modeling and L1 optimization have allowed for extraordinary applications such as the single pixel camera, as well as computer vision systems that can exceed human performance. Here we present a novel neural network architecture combining a sparse filter model and locally competitive algorithms (LCAs), and demonstrate the networks ability to classify human actions from video. Sparse filtering is an unsupervised feature learning algorithm designed to optimize the sparsity of the feature distribution directly without having the need to model the data distribution. LCAs are defined by a system of di↵erential equations where the initial conditions define an optimization problem and the dynamics converge to a sparse decomposition of the input vector. We applied this architecture to train a classifier on categories of motion in human action videos. Inputs to the network were small 3D patches taken from frame di↵erences in the videos. Dictionaries were derived for each action class and then activation levels for each dictionary were assessed during reconstruction of a novel test patch. We discuss how this sparse modeling approach provides a natural framework for multi-sensory and multimodal data processing including RGB video, RGBD video, hyper-spectral video, and stereo audio/video streams. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

by Chiu Kwok-yuan, Edward & Man Kin-wah, Andy. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 107-108.

Cheng Po Chu. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references. / ABSTRACT --- p.1 / Chapters / Chapter I. --- BACKGROUND --- p.2 / Chapter II. --- OVERVIEW OF HISTORY-TAKING EXPERT SYSTEMS --- p.3 / Chapter II.1 --- Structure of Diagnostic systems --- p.3 / Chapter II.2 --- Present Design --- p.4 / Chapter III. --- LOGICAL APPROACH TO HISTORY TAKING --- p.5 / Chapter III.1 --- Objectives of Taking a Medical History --- p.5 / Chapter III.2 --- Process of History Taking --- p.6 / Chapter III.3 --- The Art of Asking Questions --- p.8 / Chapter III.4 --- Implementation Problems --- p.9 / Chapter III.4.1 --- Question of Users --- p.9 / Chapter III.4.2 --- Question of the End Point --- p.10 / Chapter III.4.3 --- Analysis Problems --- p.10 / Chapter IV. --- DESIGN OF THE SYSTEM --- p.11 / Chapter IV.1 --- DATA REPRESENTATION --- p.11 / Chapter IV.1.1 --- Diagnosis --- p.11 / Chapter IV.1.2 --- Symptoms --- p.12 / Chapter IV.1.3 --- Patient History --- p.14 / Chapter IV.2 --- KNOWLEDGE --- p.15 / Chapter IV.3 --- INFERENCE ENGINE --- p.19 / Chapter IV.4 --- TEACHING MECHANISM --- p.24 / Chapter IV.4.1 --- Diagnostic Module --- p.24 / Chapter IV.4.2 --- Teaching Module: --- p.24 / Chapter V. --- STATISTICAL STUDY --- p.26 / Chapter VI. --- SAMPLE RUNNING OF THE PROGRAM: --- p.27 / Chapter VI.l. --- DIAGNOSTIC MODULE --- p.28 / Chapter VI.1.1 --- "Demographic Data, Chief Complaint and History of Present Illness" --- p.28 / Chapter VI.1.2 --- Related Symptoms --- p.30 / Chapter VI.1.3 --- Symptom Descriptors --- p.30 / Chapter VI.1.4 --- Deduction and Ask Cycle --- p.30 / Chapter VI.1.5. --- Summary --- p.31 / Chapter VI.1.6 --- Record in casebook --- p.32 / Chapter VI.2 --- THE TUTORING MODULE --- p.32 / Chapter VI.2.1 --- Demographic Data and Chief Complaint --- p.32 / Chapter VI.2.2 --- Advises and History of Present Illness --- p.33 / Chapter VI.2.2.1 --- Advises --- p.33 / Chapter VI.2.2.2 --- Summary --- p.34 / Chapter VI.2.2.3 --- History of Present Illness --- p.34 / Chapter VI.2.2.4 --- Advises again --- p.35 / Chapter VI.2.2.5 --- History of Present Illness again --- p.36 / Chapter VI.2.2.6 --- Advises again --- p.36 / Chapter VI.2.2.7 --- Summary again --- p.37 / Chapter VI.2.2.8 --- History of Present Illness again --- p.37 / Chapter VI.2.2.9 --- Offer of advice and Summary again --- p.38 / Chapter VI.2.3 --- Termination --- p.38 / Chapter VI.3 --- RETRIEVING CASES AND SUMMARIZING --- p.39 / Chapter VII. --- PERFORMANCE OF THE SYSTEM --- p.43 / Chapter VIII. --- FURTHER DEVELOPMENT --- p.44 / CONCLUSION --- p.46 / REFERENCES: --- p.46 / ACKNOWLEDGMENT: --- p.46