Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include linear model, neural networks model, 1-nearest neighbor and random forest model. For every kind of model, a variable selection method should be used. In our thesis we try to find the best model for this problem, and also select the most important input signals. After we compare these four models, we found that the best accuracy (96.9% correct classifications) was achieved with the random forest model.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different supervised methods and achieving 54% of accuracy. An approach related to tree-structure of classifiers obtained better results in comparison with a hierarchical approach. / Revisión por pares

<p dir="ltr">Artificial Intelligence (AI) is anticipated to exert a considerable impact on the global Gross Domestic Product (GDP), with projections estimating a contribution of 13 trillion dollars by the year 2030 (IEEE Board of Directors, 2019). In light of this influence on economic, societal, and intellectual realms, it is imperative for Policy Makers to acquaint themselves with the ongoing developments and consequential impacts of AI. The exigency of their preparedness lies in the potential for AI to evolve in unpredicted directions should proactive measures not be promptly instituted.</p><p dir="ltr">This paper endeavors to address a pivotal research question: " Do United States Policy Makers have a sufficient knowledgebase to understand Responsible AI in relation to Machine Learning to pass Artificial Intelligence legislation; and if they do not, how should a pedological instructional framework be created to give them the necessary knowledge?" The pursuit of answers to this question unfolded through the systematic review, gap analysis, and formulation of an instructional framework specifically tailored to elucidate the intricacies of Machine Learning. The findings of this study underscore the imperative for policymakers to undergo educational initiatives in the realm of artificial intelligence. Such educational interventions are deemed essential to empower policymakers with the requisite understanding for formulating effective regulatory frameworks that ensure the development of Responsible AI. The ethical dimensions inherent in this technological landscape warrant consideration, and policymakers must be equipped with the necessary cognitive tools to navigate these ethical quandaries adeptly.</p><p dir="ltr">In response to this exigency, the present study has undertaken the design and development of an instructional framework. This framework is conceived as a strategic intervention to address the evident cognitive gap existing among policymakers concerning the nuances of AI. By imparting an understanding of AI-related concepts, the framework aspires to cultivate a more informed and discerning governance ethos among policymakers, thus contributing to the responsible and ethical deployment of AI technologies.</p>