Esta dissertação apresenta um sistema de indução de classificadores fuzzy. Ao invés de utilizar a abordagem tradicional de sistemas fuzzy baseados em regras, foi utilizado o modelo de Árvore de Padrões Fuzzy(APF), que é um modelo hierárquico, com uma estrutura baseada em árvores que possuem como nós internos operadores lógicos fuzzy e as folhas são compostas pela associação de termos fuzzy com os atributos de entrada. O classificador foi obtido sintetizando uma árvore para cada classe, esta árvore será uma descrição lógica da classe o que permite analisar e interpretar como é feita a classificação. O método de aprendizado originalmente concebido para a APF foi substituído pela Programação Genética Cartesiana com o intuito de explorar melhor o espaço de busca. O classificador APF foi comparado com as Máquinas de Vetores de Suporte, K-Vizinhos mais próximos, florestas aleatórias e outros métodos Fuzzy-Genéticos em diversas bases de dados do UCI Machine Learning Repository e observou-se que o classificador APF apresenta resultados competitivos. Ele também foi comparado com o método de aprendizado original e obteve resultados comparáveis com árvores mais compactas e com um menor número de avaliações. / This work presents a system for induction of fuzzy classifiers. Instead of the traditional fuzzy based rules, it was used a model called Fuzzy Pattern Trees (FPT), which is a hierarchical tree-based model, having as internal nodes, fuzzy logical operators and the leaves are composed of a combination of fuzzy terms with the input attributes. The classifier was obtained by creating a tree for each class, this tree will be a logic class description which allows the interpretation of the results. The learning method originally designed for FPT was replaced by Cartesian Genetic Programming in order to provide a better exploration of the search space. The FPT classifier was compared against Support Vector Machines, K Nearest Neighbour, Random Forests and others Fuzzy-Genetics methods on several datasets from the UCI Machine Learning Repository and it presented competitive results. It was also compared with Fuzzy Pattern trees generated by the former learning method and presented comparable results with smaller trees and a lower number of functions evaluations.

A Escala Facial de Dor - Revisada (EFD-R) é uma das escalas mais recomendadas na mensuração da intensidade da dor aguda em crianças. A versão original desta escala foi testada em crianças canadenses. O objetivo deste trabalho foi avaliar a validade, interpretação e preferência da versão brasileira da Escala Facial de Dor - Revisada (EFD-R-B), em duas amostras de crianças brasileiras: uma envolvendo dor aguda procedural e outra dor aguda pós-cirúrgica. Na primeira amostra participaram 77 crianças com idades entre 6 e 12 anos, do sexo feminino e masculino, que foram submetidas à coleta de sangue (dor procedural). As crianças estimaram a intensidade da sua dor, antes e após a punção venosa, na EFD-R-B. Na estimação após a punção venosa, a Escala Colorida Analógica (ECA) foi administrada junto com a EFD-R-B e, além disso, as crianças indicaram as faces que expressavam uma dor leve, moderada e severa, a escala que preferiam e o porquê. Na segunda amostra, participaram 53 crianças com idades entre 6 e 12 anos, do sexo feminino e masculino, que tinham sido submetidas a pequenas cirurgias (dor pós-cirúrgica). Nesta amostra, as crianças estimaram, na EFD-R-B e na ECA, a intensidade da dor que estavam sentindo no momento da entrevista. Também indicaram as faces que expressavam uma dor leve, moderada e severa, o limiar de tratamento da dor, a escala que preferiam e o porquê. Na comparação entre as pontuações obtidas na EFD-R-B e na ECA (validade convergente), nas duas amostras, os valores dos coeficientes Kendall\'s tau foram altos e significativos: =0,75 para o grupo de dor procedural e =0,79 para o grupo de dor pós-cirúrgica (p=0,00 nas duas amostras). No grupo de dor procedural, a EFD-R-B refletiu as mudanças na intensidade da dor vivenciada pelas crianças antes e após a punção venosa (validade concorrente): Teste de Wilcoxon z=-6,65; p=0,00. Considerando uma escala de 0 a 10 para a EFD-R-B, a mediana e a amplitude interquartil (AIQ) para as faces indicadas como expressivas de intensidade leve, moderada e severa, foram 2 (2-2), 4 (4-6) e 10 (10-10) respectivamente, no grupo de dor procedural, e 2 (2-2), 6 (4-8) e 10 (10-10) respectivamente, no grupo de dor pós-cirúrgica. Na estimação do limiar de tratamento da dor (grupo de dor pós-cirúrgica), a mediana (AIQ) foi 6 (4-10). No grupo de dor procedural, a EFD-R-B foi a escala preferida por 57,1% das crianças e a ECA por 41,6%; no grupo de dor pós-cirúrgica, a EFD-R-B foi escolhida por 66% das crianças e a ECA por 34%. Estas proporções somente foram significativas no grupo de dor pós-cirúrgica (X²=5,453 p=0,02). Nossos resultados mostram que a EFD-R-B possui propriedades similares à escala original e boa aceitação entre as crianças entrevistadas. A determinação dos valores das diferentes intensidades de dor e do limiar de tratamento da dor, para cada participante, representa uma evidência importante sobre a interpretação da EFD-R. / The Faces Pain Scale Revised (FPS-R) is one of the most recommended tools in measuring the intensity of acute pain in children. The aim of this study was to assess validity, interpretability and preference of the Brazilian version of the FPS-R (FPS-R-B), in two different clinical samples. The first sample contained seventy-seven children, 6 to 12 years old and both sexes, undergoing venipuncture for blood sample (procedural pain). These children estimated their perceived pain intensity in FPS-R-B before and after venipuncture. Furthermore, after venipuncture, children were asked: a) to evaluate the intensity of their needle pain using the Coloured Analogue Scale (CAS), b) to indicate on the Faces scale the intensities representing the mild, moderate and severe pain, and c) to choose the scale they preferred and indicate the reasons for the preference. The second sample included fifty-three children, 6 to 12 years old and both sexes, undergoing minor surgery (postoperative pain). Following surgery, children were asked: a) to provide a rating of their current pain intensity using the FPS-R-B and the CAS, b) to indicate on the Faces scale the intensities representing the mild, moderate and severe pain, c) to estimate, on the FPS-R-B, the intensity of pain that their felt to warrant pharmacologic intervention (pain treatment threshold), and d) to choose the scale they preferred and indicate the reasons for the preference. The degree of concordance between FPS-R-B and CAS ratings (convergent validity), for both samples, was high and statistically significant Kendall\'s tau value was 0.75 for the first sample, and 0.79 for the second sample, (p<0.05) . FPS-R-B reflected the changes in pain intensity before and after venipuncture (concurrent validity): Wilcoxon Test z=- 6.24; p< 0.05. On the 0-10 scale for the FPS-R-B, the median and interquartile range (IQR) of the intensities that represented mild, moderate and severe pain were 2 (2-2), 4 (4-6) e 10 (10-10) respectively, for the first sample, and 2 (2-2), 6 (4-8) e 10 (10-10) respectively, for the second sample. The median and IQR for pain treatment threshold were 6 (4-10). Fifty-seven percent of children in the first sample and 64.8% in the second sample preferred the FPS-R-B. These proportions were statistically significant for the second sample (X²=5,453 p<0,05). Our data show that the FPS-R-B has similar statistical properties to the original. New evidences were presented regarding interpretability of the FPS-R by determining each children\'s treatment threshold and estimate of mild, moderate and severe pain. In this study, the FPS-R-B was preferred by the majority of children.

Enterprising technologies and policies that focus on energy reduction in buildings are paramount to achieving global carbon emissions targets. Energy retrofits, building stock modelling, heating, ventilation, and air conditioning (HVAC) upgrades and demand side management all present high leverage opportunities in this regard. Advances in computing, data science and machine learning can be leveraged to enhance these methods and thus to expedite energy reduction in buildings but challenges such as lack of data, limited model generalizability and reliability and un-reproducible studies have resulted in restricted industry adoption. In this thesis, rigorous and reproducible studies are designed to evaluate the benefits and limitations of state-of-the-art machine learning and statistical techniques for high-impact applications, with an emphasis on addressing the challenges listed above. The scope of this work includes calibration of physics-based building models and supervised deep learning, both of which are used to estimate building properties from real and synthetic data. • Original grey-box methods are developed to characterize physical thermal properties (RC and RK)from real-world measurement data. • The novel application of supervised deep learning for thermal property estimation and HVAC systems identification is shown to achieve state-of-the-art performance (root mean squared error of 0.089 and 87% validation accuracy, respectively). • A rigorous empirical review is conducted to assess which types of gray and black box models are most suitable for practical application. The scope of the review is wider than previous studies, and the conclusions suggest a re-framing of research priorities for future work. • Modern interpretability techniques are used to provide unique insight into the learning behaviour of the black box methods. Overall, this body of work provides a critical appraisal of new and existing data-driven approaches for thermal property estimation in buildings. It provides valuable and novel insight into barriers to widespread adoption of these techniques and suggests pathways forward. Performance benchmarks, open-source model code and a parametrically generated, synthetic dataset are provided to support further research and to encourage industry adoption of the approaches. This lays the necessary groundwork for the accelerated adoption of data-driven models for thermal property identification in buildings. / Graduate

[pt] A série temporal é a representação mais comum para a evoluçãao no tempo de uma variável qualquer. Em um problema de previsão de séries temporais, procura-se ajustar um modelo para obter valores futuros da série, supondo que as informações necessárias para tal se encontram no próprio histórico da série. Como os fenômenos representados pelas séries temporais nem sempre existem de maneira isolada, pode-se enriquecer o modelo com os valores históricos de outras séries temporais relacionadas. A estrutura formada por diversas séries de mesmo intervalo e dimensão ocorrendo paralelamente é denominada série temporal multivariada. Esta dissertação propõe uma metodologia de geração de um Sistema de Inferência Fuzzy (SIF) para previsão de séries temporais multivariadas a partir de dados históricos, com o objetivo de obter bom desempenho tanto em termos de acurácia de previsão como no quesito interpretabilidade da base de regras – com o intuito de extrair conhecimento sobre o relacionamento entre as séries. Para tal, são abordados diversos aspectos relativos ao funcionamento e à construção de um SIF, levando em conta a sua complexidade e claridade semântica. O modelo é avaliado por meio de sua aplicação em séries temporais multivariadas da base completa da competição M3, comparandose a sua acurácia com as dos métodos participantes. Além disso, através de dois estudos de caso com dados reais públicos, suas possibilidades de extração de conhecimento são exploradas por meio de dois estudos de caso construídos a partir de dados reais. Os resultados confirmam a capacidade do AutoMFIS de modelar de maneira satisfatória séries temporais multivariadas e de extrair conhecimento da base de dados. / [en] A time series is the most commonly used representation for the evolution of a given variable over time. In a time series forecasting problem, a model aims at predicting the series future values, assuming that all information needed to do so is contained in the series past behavior. Since the phenomena described by the time series does not always exist in isolation, it is possible to enhance the model with historical data from other related time series. The structure formed by several different time series occurring in parallel, each featuring the same interval and dimension, is called a multivariate time series. This dissertation proposes a methodology for the generation of a Fuzzy Inference System (FIS) for multivariate time series forecasting from historical data, aiming at good performance in both forecasting accuracy and rule base interpretability – in order to extract knowledge about the relationship between the modeled time series. Several aspects related to the operation and construction of such a FIS are investigated regarding complexity and semantic clarity. The model is evaluated by applying it to multivariate time series obtained from the complete M3 competition database and by comparing it to other methods in terms of accuracy. In addition knowledge extraction possibilities are explored through two case studies built from actual data. Results confirm that AutoMFIS is indeed capable of modeling time series behaviors in a satisfactory way and of extractig meaningful knowldege from the databases.

[pt] Por definição, a série temporal representa o comportamento de uma variável em função do tempo. Para o processo de previsão de séries, o modelo deve ser capaz de aprender a dinâmica temporal das variáveis para obter valores futuros. Contudo, prever séries temporais com exatidão é uma tarefa que vai além de escolher o modelo mais complexo, e portanto a etapa de análise é um processo fundamental para orientar o ajuste do modelo. Especificamente em problemas multivariados, o AutoMFIS é um modelo baseado na lógica fuzzy, desenvolvido para introduzir uma explicabilidade dos resultados através de regras semanticamente compreensíveis. Mesmo com características promissoras e positivas, este sistema possui limitações que tornam sua utilização impraticável em problemas com bases de dados com alta dimensionalidade. E com a presença cada vez maior de bases de dados mais volumosas, é necessário que a síntese automática de sistemas fuzzy seja adaptada para abranger essa nova classe de problemas de previsão. Por conta desta necessidade, a presente dissertação propõe a extensão do modelo AutoMFIS para a previsão de séries temporais com alta dimensionalidade, chamado de e-AutoMFIS. Apresentase uma nova metodologia, baseada em comitê de previsores, para o aprendizado distribuído de geração de regras fuzzy. Neste trabalho, são descritas as características importantes do modelo proposto, salientando as modificações realizadas para aprimorar tanto a previsão quanto a interpretabilidade do sistema. Além disso, também é avaliado o seu desempenho em problemas reais, comparando-se a acurácia dos resultados com as de outras técnicas descritas na literatura. Por fim, em cada problema selecionado também é considerado o aspecto da interpretabilidade, discutindo-se os critérios utilizados para a análise de explicabilidade. / [en] By definition, the time series represents the behavior of a variable as a time function. For the series forecasting process, the model must be able to learn the temporal dynamics of the variables in order to obtain consistent future values. However, an accurate time series prediction is a task that goes beyond choosing the most complex (or promising) model that is applicable to the type of problem, and therefore the analysis step is a fundamental procedure to guide the adaptation of a model. Specifically, in multivariate problems, AutoMFIS is a model based on fuzzy logic, developed not only to give accurate forecasts but also to introduce the explainability of results through semantically understandable rules. Even with such promising characteristics, this system has shown practical limitations in problems that involve datasets of high dimensionality. With the increasing demand formethods to deal with large datasets, it should be great that approaches for the automatic synthesis of fuzzy systems could be adapted to cover a new class of forecasting problems. This dissertation proposes an extension of the base model AutoMFIS modeling method for time series forecasting with high dimensionality data, named as e-AutoMFIS. Based on the Ensemble learning theory, this new methodology applies distributed learning to generate fuzzy rules. The main characteristics of the proposed model are described, highlighting the changes in order to improve both the accuracy and the interpretability of the system. The proposed model is also evaluated in different case studies, in which the results are compared in terms of accuracy against the results produced by other methods in the literature. In addition, in each selected problem, the aspect of interpretability is also assessed, which is essential for explainability evaluation.

The field of explainable artificial intelligence (XAI) has gained momentum in recent years following the increased use of AI systems across industries leading to bias, discrimination, and data security concerns. Several conceptual frameworks for how to reach AI systems that are fair, transparent, and understandable have been proposed, as well as a number of technical solutions improving some of these aspects in a research context. However, there is still a lack of studies examining the implementation of these concepts and techniques in practice. This research aims to bridge the gap between prominent theory within the area and practical implementation, exploring the implementation and evaluation of XAI models in the Swedish credit scoring industry, and proposes a three-step framework for the implementation of local explanations in practice. The research methods used consisted of a case study with the model development at UC AB as a subject and an experiment evaluating the consumers' levels of trust and system understanding as well as the usefulness, persuasive power, and usability of the explanation for three different explanation prototypes developed. The framework proposed was validated by the case study and highlighted a number of key challenges and trade-offs present when implementing XAI in practice. Moreover, the evaluation of the XAI prototypes showed that the majority of consumers prefers rulebased explanations, but that preferences for explanations is still dependent on the individual consumer. Recommended future research endeavors include studying a longterm XAI project in which the models can be evaluated by the open market and the combination of different XAI methods in reaching a more personalized explanation for the consumer. / Under senare år har antalet AI implementationer stadigt ökat i flera industrier. Dessa implementationer har visat flera utmaningar kring nuvarande AI system, specifikt gällande diskriminering, otydlighet och datasäkerhet vilket lett till ett intresse för förklarbar artificiell intelligens (XAI). XAI syftar till att utveckla AI system som är rättvisa, transparenta och begripliga. Flera konceptuella ramverk har introducerats för XAI som presenterar etiska såväl som politiska perspektiv och målbilder. Dessutom har tekniska metoder utvecklats som gjort framsteg mot förklarbarhet i forskningskontext. Däremot saknas det fortfarande studier som undersöker implementationer av dessa koncept och tekniker i praktiken. Denna studie syftar till att överbrygga klyftan mellan den senaste teorin inom området och praktiken genom en fallstudie av ett företag i den svenska kreditupplysningsindustrin. Detta genom att föreslå ett ramverk för implementation av lokala förklaringar i praktiken och genom att utveckla tre förklaringsprototyper. Rapporten utvärderar även prototyperna med konsumenter på följande dimensioner: tillit, systemförståelse, användbarhet och övertalningsstyrka. Det föreslagna ramverket validerades genom fallstudien och belyste ett antal utmaningar och avvägningar som förekommer när XAI system utvecklas för användning i praktiken. Utöver detta visar utvärderingen av prototyperna att majoriteten av konsumenter föredrar regelbaserade förklaringar men indikerar även att preferenser mellan konsumenter varierar. Rekommendationer för framtida forskning är dels en längre studie, vari en XAI modell introduceras på och utvärderas av den fria marknaden, dels forskning som kombinerar olika XAI metoder för att generera mer personliga förklaringar för konsumenter.

Simulation driven design is a method and process that has been developed over many years, and with today’s advanced software, the possibility to embed simulation into the design process has become a reality. The advantages of using simulation driven design in the product development process is well known and compared to a more traditional design process, the simulation driven design process can give the user the possibility to explore, optimize and design products with reduced lead time.  One of the methods that is applied in simulation driven design is the use of topology optimization (structural optimization). Topology optimization is something that GKN uses in the design process. Due to the complexity of the products GKN design and manufacture, the output from the topology optimization lacks good design interpretability and the design process requires a lot of time and effort.  The purpose of the thesis is to explore different simulation tools used for topology optimization and improve the methodology and process with higher design interpretability for a static topology optimization. This requires a good understanding of the component and the product development process. It is imperative that the topology result must have high design interpretability, and the visualization of the result must show the formation of clear rib structures.  The software’s used for performing topology optimization in this thesis are Inspire, SimLab, HyperMesh, and OptiStruct (HyperWorks suite). Static topology optimization is conducted, and manufacturing constraints for the casting process are considered. The methodology developed is robust for similar gearbox housing structures, and the process is set up to be efficient. The proposed method is verified by implementing it on a housing structure.  The resulting concept from the topology optimization is deemed to have higher design interpretability which improves knowledge transfer in the design process when compared to the current topology results. The weight of the product is reduced, and a more optimum design is reached with a lesser number of iterations. / Simuleringsdriven design är en metod och process som har utvecklats i många år, och med dagens avancerade programvaror har möjligheten att få in simulering direkt i designprocessen blivit verklighet. Fördelarna med att använda simuleringsdriven design i produktutvecklingsprocessen är välkända och jämfört med en mer traditionell designprocess kan den simuleringsdrivna designprocessen ge användaren möjlighet att utforska, optimera och designa produkter med reducerade ledtider som följd.  En av de metoder som tillämpas i simuleringsdriven design är användning av topologioptimering (strukturoptimering). Topologioptimering är något som GKN använder i designprocessen. På grund av komplexiteten hos produkterna GKN designar och tillverkar kräver designprocessen mycket ingenjörsarbete och tid. Produktionen har också problem med att tolka topologioptimeringsresultaten.. Syftet med avhandlingen är att utforska olika simuleringsverktyg som används för topologioptimering och förbättra metodiken och processen för att öka designtolkningen av en statisk topologioptimering. Detta kräver en god förståelse för komponenten och produktutvecklingsprocessen. För att förbättra osäkerheterna i resultaten från optimeringen, är det nödvändigt att dessa resultat är lätta att tolka, och visualiseringen av resultaten ska vara tydliga och visa hur lastvägarna går och därmed vart ribbor ska läggas.  Programvarorna som användes för att utföra topologioptimering i denna avhandling är Inspire, SimLab, HyperMesh och OptiStruct (HyperWorks suite). Statisk topologioptimering är utförd och tillverkningsbegränsningar för gjutningsprocesser har inkluderats.  Den metod som utvecklats är robust för liknande växellådshusstrukturer, och processen som föreslås är mera effektiv. Den föreslagna metoden har verifierats genom att den tillämpats för ett växellådshus.  Det resulterande topologikonceptet antas ha en bättre designtolkningsbarhet, vilket möjliggör en förbättrad kommunikation och kunskapsöverföring i konstruktionsprocessen, jämfört med den nuvarande processen. Produktens vikt minskas, och en mer optimal design nås med färre iterationer.

Despite the access to enormous amounts of data, there is a holdback in the usage of machine learning in the Cyber Security field due to the lack of interpretability of ”Black­box” models and due to heterogenerous data. This project presents a method that provide insights in the decision making process in Cyber Security classification. Hybrid Ensemble Methods (HEMs), use several weak learners trained on single data features and combines the output of these in a neural network. In this thesis HEM preforms phishing website classification with high accuracy, along with interpretability. The ensemble of predictions boosts the accuracy with 8%, giving a final prediction accuracy of 93 %, which indicates that HEM are able to reconstruct correlations between the features after the interpredability stage. HEM provides information about which weak learners trained on specific information that are valuable for the classification. No samples were disregarded despite missing features. Cross validation were made across 3 random seeds and the results showed to be steady with a variance of 0.22%. An important finding was that the methods performance did not significantly change when disregarding the worst of the weak learners, meaning that adding models trained on bad data won’t sabotage the prediction. The findings of these investigations indicates that Hybrid Ensamble methods are robust and flexible. This thesis represents an attempt to construct a smarter way of making predictions, where the usage of several forms of information can be combined, in an artificially intelligent way. / Trots tillgången till enorma mängder data finns det ett bakslag i användningen av maskininlärning inom cybersäkerhetsområdet på grund av bristen på tolkning av ”Blackbox”-modeller och på grund av heterogen data. Detta projekt presenterar en metod som ger insikt i beslutsprocessen i klassificering inom cyber säkerhet. Hybrid Ensemble Methods (HEMs), använder flera svaga maskininlärningsmodeller som är tränade på enstaka datafunktioner och kombinerar resultatet av dessa i ett neuralt nätverk. I denna rapport utför HEM klassificering av nätfiskewebbplatser med hög noggrannhet, men med vinsten av tolkningsbarhet. Sammansättandet av förutsägelser ökar noggrannheten med 8 %, vilket ger en slutgiltig prediktionsnoggrannhet på 93 %, vilket indikerar att HEM kan rekonstruera korrelationer mellan funktionerna efter tolkbarhetsstadiet. HEM ger information om vilka svaga maskininlärningsmodeller, som tränats på specifik information, som är värdefulla för klassificeringen. Inga datapunkter ignorerades trots saknade datapunkter. Korsvalidering gjordes över 3 slumpmässiga dragningar och resultaten visade sig vara stabila med en varians på 0.22 %. Ett viktigt resultat var att metodernas prestanda inte förändrades nämnvärt när man bortsåg från de sämsta av de svaga modellerna, vilket innebär att modeller tränade på dålig data inte kommer att sabotera förutsägelsen. Resultaten av dessa undersökningar indikerar att Hybrid Ensamble-metoder är robusta och flexibla. Detta projekt representerar ett försök att konstruera ett smartare sätt att göra klassifieringar, där användningen av flera former av information kan kombineras, på ett artificiellt intelligent sätt.

Denna undersökning utvärderar tillvägagångssätt inom "Explainable Artificial Intelligence" (XAI), särskilt "Local Interpretable Model Agnostic Explanations" (LIME) och 'Shapley Additive Explanations' (SHAP), genom att implementera dem i maskininlärningsmodeller som används inom cybersäkerhetens brandväggssystem. Prioriteten är att förbättra förståelsen av flervals klassificerings uppgift inom brandvägg hantering. I takt med att dagens AI-system utvecklas, sprids och tar en större roll i kritiska beslutsprocesser, blir transparens och förståelighet alltmer avgörande. Denna studie demonstrerar genom detaljerad analys och metodisk experimentell utvärdering hur SHAP och LIME belyser effekten av olika egenskaper på modellens prognoser, vilket i sin tur ökar tilliten till beslut som drivs av AI. Resultaten visar, hur funktioner såsom "Elapsed Time (sec)”, ”Network Address Translation” (NAT) källa och "Destination ports" ansenlig påverkar modellens resultat, vilket demonstreras genom analys av SHAP-värden. Dessutom erbjuder LIME detaljerade insikter i den lokala beslutsprocessen, vilket förbättrar vår förståelse av modellens beteende på individuell nivå. Studiet betonar betydelsen av XAI för att minska klyftan mellan AI operativa mekanismer och användarens förståelse, vilket är avgörande för felsökning samt för att säkerställa rättvisa, ansvar och etisk integritet i AI-implementeringar. Detta gör studiens implikationer betydande, då den ger en grund för framtida forskning om transparens i AI-system inom olika sektorer. / This study evaluates the explainable artificial intelligence (XAI) methods, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), by applying them to machine learning models used in cybersecurity firewall systems and focusing on multi-class classification tasks within firewall management to improve their interpretability. As today's AI systems become more advanced, widespread, and involved in critical decision-making, transparency and interpretability have become essential. Through accurate analysis and systematic experimental evaluation, this study illustrates how SHAP and LIME clarify the impact of various features on model predictions, thereby leading to trust in AI-driven decisions. The results indicate that features such as Elapsed Time (sec), Network Address Translation (NAT) source, and Destination ports markedly affect model outcomes, as demonstrated by SHAP value analysis. Additionally, LIME offers detailed insights into the local decision making process, enhancing our understanding of model behavior at the individual level. The research underlines the importance of XAI in reducing the gap between AI operational mechanisms and user understanding, which is critical for debugging, and ensuring fairness, responsibility, and ethical integrity in AI implementations. This makes the implications of this study substantial, providing a basis for future research into the transparency of AI systems across different sectors.

Made available in DSpace on 2016-06-02T19:05:54Z (GMT). No. of bitstreams: 1 3998.pdf: 3486824 bytes, checksum: f1c040adfdc7d0672bc93a058f8a413d (MD5) Previous issue date: 2011-06-28 / Financiadora de Estudos e Projetos / The goal of this work is to study, expand and evaluate the use of multiobjective genetic algorithms and the iterative rule learning approach in fuzzy system generation, especially, in fuzzy rule-based systems, both in automatic fuzzy rule generation from datasets and in fuzzy sets optimization. This work investigates the use of multi-objective genetic algorithms with a focus on the trade-off between accuracy and interpretability, considered contradictory objectives in the representation of fuzzy systems. With this purpose, we propose and implement an evolutive multi-objective genetic model composed of three stages. In the first stage uniformly distributed fuzzy sets are created. In the second stage, the rule base is generated by using an iterative rule learning approach and a multiobjective genetic algorithm. Finally the fuzzy sets created in the first stage are optimized through a multi-objective genetic algorithm. The proposed model was evaluated with a number of benchmark datasets and the results were compared to three other methods found in the literature. The results obtained with the optimization of the fuzzy sets were compared to the result of another fuzzy set optimizer found in the literature. Statistical comparison methods usually applied in similar context show that the proposed method has an improved classification rate and interpretability in comparison with the other methods. / O objetivo deste trabalho é estudar, expandir e avaliar o uso dos algoritmos genéticos multiobjetivo e a abordagem iterativa na geração de sistemas fuzzy, mais especificamente para sistemas fuzzy baseados em regras, tanto na geração automática da base de regras fuzzy a partir de conjuntos de dados, como a otimização dos conjuntos fuzzy. Esse trabalho investiga o uso dos algoritmos genéticos multiobjetivo com enfoque na questão de balanceamento entre precisão e interpretabilidade, ambos considerados contraditórios entre si na representação de sistemas fuzzy. Com este intuito, é proposto e implementado um modelo evolutivo multiobjetivo genético composto por três etapas. Na primeira etapa são criados os conjuntos fuzzy uniformemente distribuídos. Na segunda etapa é tratada a geração da base de regras usando a abordagem iterativa e um algoritmo genético multiobjetivo. Por fim, na terceira etapa os conjuntos fuzzy criados na primeira etapa são otimizados mediante um algoritmo genético multiobjetivo. O modelo desenvolvido foi avaliado em diversos conjuntos de dados benchmark e os resultados obtidos foram comparados com outros três métodos, que geram regras de classificação, encontrados na literatura. Os resultados obtidos após a otimização dos conjuntos fuzzy foram comparados com resultados de outro otimizador de conjuntos fuzzy encontrado na literatura. Métodos estatísticos de comparação usualmente aplicados em contextos semelhantes mostram uma melhor taxa de classificação e interpretabilidade do método proposto com relação a outros métodos.