Resumo: Imagens multiespectrais de alta resolução espacial podem se constituir em uma fonte de dados adequada para o mapeamento de processos associados ao desenvolvimento de culturas agrícolas, como a detecção de plantas daninhas. A aerofotogrametria convencional e imagens de satélite de alta resolução espacial são alternativas para aquisição dessas imagens. Entretanto, devido ao custo elevado da aquisição destas imagens, tem sido desenvolvido, pelo Departamento de Cartografia da Faculdade de Ciências e Tecnologia da UNESP de Presidente Prudente, um Sistema de Sensoriamento Remoto Aerotransportado (SRA), capaz de oferecer resolução espacial sub-métrica. Este trabalho considerou a hipótese de que a partir de imagens adquiridas pelo Sistema é possível discriminar graus de infestação de plantas daninhas em culturas de café. Para investigar esta hipótese, foi realizado o mapeamento de plantas daninhas utilizando dois diferentes métodos: classificação de imagens multiespectrais (classificação por redes neurais artificiais - RNA) e análise geoestatística (krigagem por indicação com dados indiretos). Os mapas temáticos foram submetidos à análise da qualidade temática. A krigagem por indicação apresentou resultados suavizados e confusos, ao contrário da classificação por RNA, a qual se constituiu num método eficiente para o objetivo proposto, confirmando a hipótese inicial da investigação. / Abstract: Multispectral images with high spatial resolution can be a suitable data source for the mapping of processes associated to the crop development, as detection of weed. The aerial photogrammetry and satellite image of high spatial resolution are alternatives for the aquisition of these images. However, due to the high cost of these images, a Sistema de Sensoriamento Remoto Aerotransportado - SRA, which is capable of to offer sub-metric spatial resolution has been developed by the Department of Cartography, FCT/Unesp (Presidente Prudente). This work taked into account the hypothesis that is possible to discriminate infestation degree of weed in coffee crop from high spatial resolution multispectral images. To investigate this hypothesis, it was accomplished the mapping using two different methods: multispectral images classification (artificial neural networks classification) and geoestatistics analysis (Indicator kriging with soft data). The thematics maps were submitted to the analysis of thematic quality. The indicator kriging showed smoothed and confused results instead of the artificial neural networks classification, whose results were efficient for the purpose, confirming the initial hypothesis of the investigation. / Orientador: Maria de Lourdes Bueno Trindade Galo / Coorientador: Nilton Nobuhiro Imai / Banca: Bernardo Friedrich Theoodor Rudorff / Banca: Vilma Mayumi Tachibana / Mestre

This thesis is studying basic concepts of Convolutional Neural Networks. Influence of structural elements on ability of the network to train is investigated. Result of this thesis is comparisons of designed model of Convolutional Neural Network with results from ILSVRC competition.

Artificial Intelligence in form of Machine Learning classifiers is increasingly applied for travel choice modeling issues and therefore constitutes a promising, competitive alternative towards conventional discrete choice models like the Logit approach. In comparison to traditional theory-based models, data-driven Machine Learning generally shows powerful predictive performance, but often lacks in model interpretability, i.e., the provision of comprehensible explanations of individual decision behavior. Consequently, the question about which approach is superior remains unanswered. Thus, this paper performs an in-depth comparison between benchmark Logit models and Artificial Neural Networks and Decision Trees representing two popular algorithms of Artificial Intelligence. The primary focus of the analysis is on the models’ prediction performance and its ability to provide reasonable economic behavioral information such as the value of travel time and demand elasticities. For this purpose, I use crossvalidation and extract behavioral indicators numerically from Machine Learning models by means of post-hoc sensitivity analysis. All models are specified and estimated on synthetic and empirical data. As the results show, Neural Networks provide plausible aggregate value of time and elasticity measures, even though their values are in different regions as those of the Logit models. The simple Classification Tree algorithm, however, appears unsuitable for the applied computation procedure of these indicators, although it provides reasonable interpretable decision rules for travel choice behavior. Consistent with the literature, both Machine Learning methods achieve strong overall predictive performance and therefore outperform the Logit models in this regard. Finally, there is no clear indication of which approach is superior. Rather, there seems to be a methodological tradeoff between Artificial Intelligence and discrete choice models depending on the underlying modeling objective.

This master's thesis deals with the possibilities how to guarantee the quality of service in the area of computer networks using a GNU/Linux operating system. This work compares and evaluates tools which are necessary to guarantee the quality of service. The goal of this work is to discuss the advantages and disadvantages of these tools and to design a system which handles the problem of quality of service. Designed system uses a heuristics, which allows the user to set up the quality of service system without studying specific properties of communication protocols on the network or application layer. This work also includes a theoretical introduction into the quality of service and computer networks.

The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyperparameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of implementation of the unary classifier.

This thesis deals with the possibility to evaluate cognitive and emotional traits and their relations to job positions and functions. The basis is to create a set of tests which could be used during a hiring procedure while deciding among candidates. It also suggests how to classify users based on a set of tests and thereby provide support in choosing the right job. The thesis gives a brief outlook of individually tested traits, describes a proposal of a web application and its implementation, describes implementation of neural network classifier and presents obtained results.

This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.