Determining species of individual trees is important for forest management. In this thesis we investigate if it is possible to discriminate between Norway spruce, Scots pine and deciduous trees from airborne laser scanning data by using unique probability density functions estimated for each specie. We estimate the probability density functions in three different ways: by fitting a beta distribution, histogram density estimation and kernel density estimation. All these methods classifies single laser returns (and not segments of laser returns). The resulting classification is compared with a reference method based on features extracted from airborne laser scanning data.We measure how well a method performs by using the overall accuracy, that is the proportion of correctly predicted trees. The highest overall accuracy obtained by the methods we developed in this thesis is obtained by using histogram-density estimation where an overall accuracy of 83.4 percent is achieved. This result can be compared with the best result from the reference method that produced an overall accuracy of 84.1 percent. The fact that we achieve a high level of correctly classified trees indicates that it is possible to use these types of methods for identification of tree species. / Att kunna artbestämma enskilda träd är viktigt inom skogsbruket. I denna uppsats undersöker vi om det är möjligt att skilja mellan gran, tall och lövträd med data från en flygburen laserskanner genom att skatta en unik täthetsfunktion för varje trädslag. Täthetsfunktionerna skattas på tre olika sätt: genom att anpassa en beta-fördelning, skatta täthetsfunktionen med histogram samt skatta täthetsfunktionen med en kernel täthetsskattning. Alla dessa metoder klassificerar varje enskild laserretur (och inte segment av laserreturer). Resultaten från vår klassificering jämförs sedan med en referensmetod som bygger på särdrag från laserskanner data. Vi mäter hur väl metoderna presterar genom att jämföra den totala precisionen, vilket är andelen korrektklassificerade träd. Den högsta totala precisionen för de framtagna metoderna i denna uppsats erhölls med metoden som bygger på täthetsskattning med histogram. Precisionen för denna metod var 83,4 procent rättklassicerade träd. Detta kan jämföras med en rättklassificering på 84,1 procent vilket är det bästa resultatet för referensmetoderna. Att vi erhåller en så pass hög grad av rättklassificerade träd tyder på att de metoder som vi använder oss av är användbara för trädslagsklassificering.

Doctor of Philosophy / Department of Statistics / Suzanne Dubnicka / DNA barcodes are short strands of nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) of the mitochondrial DNA (mtDNA). A single barcode may have the form C C G G C A T A G T A G G C A C T G . . . and typically ranges in length from 255 to around 700 nucleotide bases. Unlike nuclear DNA (nDNA), mtDNA remains largely unchanged as it is passed from mother to offspring. It has been proposed that these barcodes may be used as a method of differentiating between biological species (Hebert, Ratnasingham, and deWaard 2003). While this proposal is sharply debated among some taxonomists (Will and Rubinoff 2004), it has gained momentum and attention from biologists. One issue at the heart of the controversy is the use of genetic distance measures as a tool for species differentiation. Current methods of species classification utilize these distance measures that are heavily dependent on both evolutionary model assumptions as well as a clearly defined "gap" between intra- and interspecies variation (Meyer and Paulay 2005). We point out the limitations of such distance measures and propose a character-based method of species classification which utilizes an application of Bayes' rule to overcome these deficiencies. The proposed method is shown to provide accurate species-level classification. The proposed methods also provide answers to important questions not addressable with current methods.

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing peoples physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall.<p> In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application.<p> This wearable system works in two modes: indoor and outdoor, depending on users demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment.<p> For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life.

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing peoples physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall.<p> In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application.<p> This wearable system works in two modes: indoor and outdoor, depending on users demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment.<p> For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-09-12T14:09:09Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MSc_Antonio Carlos de Castro da Silva_digital_12_04_16.pdf: 2938596 bytes, checksum: 9d5e96b489990fe36c4e1ad5a23148dd (MD5) / Made available in DSpace on 2016-09-12T14:09:09Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MSc_Antonio Carlos de Castro da Silva_digital_12_04_16.pdf: 2938596 bytes, checksum: 9d5e96b489990fe36c4e1ad5a23148dd (MD5) Previous issue date: 2016-01-15 / A detecção prematura de defeitos nos componentes de linhas de montagem de fabricação é determinante para a obtenção de produtos finais de boa qualidade. Partindo desse pressuposto, o presente trabalho apresenta uma plataforma desenvolvida para detecção automática dos defeitos de fabricação em painéis TFT-LCD (Thin Film Transistor-Liquid Cristal Displays) através da realização de inspeção de imagem. A plataforma desenvolvida é baseada em câmeras, sendo o painel inspecionado posicionado em uma câmara fechada para não sofrer interferência da luminosidade do ambiente. As etapas da inspeção consistem em aquisição das imagens pelas câmeras, definição da região de interesse (detecção do quadro), extração das características, análise das imagens, classificação dos defeitos e tomada de decisão de aprovação ou rejeição do painel. A extração das características das imagens é realizada tomando tanto o padrão RGB como imagens em escala de cinza. Para cada componente RGB a intensidade de pixels é analisada e a variância é calculada, se um painel apresentar variação de 5% em relação aos valores de referência, o painel é rejeitado. A classificação é realizada por meio do algorítimo de Naive Bayes. Os resultados obtidos mostram um índice de 94,23% de acurácia na detecção dos defeitos. Está sendo estudada a incorporação da plataforma aqui descrita à linha de produção em massa da Samsung em Manaus. / The early detection of defects in the parts used in manufacturing assembly lines is crucial for assuring the good quality of the final product. Thus, this paper presents a platform developed for automatically detecting manufacturing defects in TFT-LCD (Thin Film Transistor-Liquid Cristal Displays) panels by image inspection. The developed platform is based on câmeras. The panel under inspection is positioned in a closed chamber to avoid interference from light sources from the environment. The inspection steps encompass image acquisition by the cameras, setting the region of interest (frame detection), feature extraction, image analysis, classification of defects, and decision making. The extraction of the features of the acquired images is performed using both the standard RGB and grayscale images. For each component the intensity of RGB pixels is analyzed and the variance is calculated. A panel is rejected if the value variation of the measure obtained is 5% of the reference values. The classification is performed using the Naive Bayes algorithm. The results obtained show an accuracy rate of 94.23% in defect detection. Samsung (Manaus) is considering the possibility of incorporating the platform described here to its mass production line.

<p dir="ltr">As data continues to grow rapidly in volume and complexity, there is an increasing need to extract meaningful insights from it. These datasets often contain sensitive individual information, making privacy protection crucial. Differential privacy has become the de facto standard for protecting individuals' privacy. Many datasets also have known constraints and structures. Can these known constraints or structures be leveraged to design mechanisms with better utility?</p><p dir="ltr">The focus of this thesis is to demonstrate that by leveraging the inherent structures and constraints within datasets, it may be possible to design differential privacy mechanisms that offer better utility (i.e., more accurate results) while maintaining the required level of privacy. This involves exploring advanced techniques and modifications to the basic mechanisms that take advantage of dataset-specific properties, such as sparsity, distributional assumptions, or other contextual information. This approach aims to minimize the noise added, thereby improving the utility of differentially private outputs.</p><p dir="ltr">In many scenarios, datasets contain constraints. In this thesis, we show that generating differentially private synthetic data while preserving constraints increases utility across several metrics, including marginal queries, classification task accuracy, and clustering. Smooth sensitivity is a data-dependent sensitivity metric that allows for more precise noise addition based on the actual data distribution, rather than worst-case scenarios. It addresses the limitations of local sensitivity by ensuring robust privacy guarantees, even in the presence of outliers or small changes in the data.</p><p dir="ltr"><br></p><p dir="ltr">We have developed a differentially private Naive Bayes model using smooth sensitivity. By using data-dependent sensitivity measures like smooth sensitivity and incorporating known data constraints, we can reduce the amount of noise added, resulting in a more accurate model.</p>

Τα συστήματα διεπαφών ανθρώπινου εγκεφάλου-υπολογιστή (BCIs: Brain-Computer Interfaces) απαιτούν την πραγματικού χρόνου, αποτελεσματική επεξεργασία των μετρήσεων των ηλεκτροεγκεφαλογραφικών (ΗΕΓ) σημάτων του χρήστη τους, προκειμένου να μεταφράσουν τις νοητικές διεργασίες/προθέσεις του σε σήματα ελέγχου εξωτερικών διατάξεων ή συστημάτων. Στο πλαίσιο της εργασίας αυτής μελετήθηκε το θεωρητικό υπόβαθρο του προβλήματος και αναλύθηκαν συνοπτικά οι κυριότερες τεχνικές που χρησιμοποιούνται σήμερα. Επιπρόσθετα, παρουσιάστηκε μία μέθοδος ταξινόμησης των νοητικών προθέσεων της αριστερής και δεξιάς κίνησης των χεριών ενός χρήστη η οποία εφαρμόστηκε σε πραγματικά ιατρικά δεδομένα. Η εξαγωγή των χαρακτηριστικών που διαφοροποιούνται μεταξύ των δύο καταστάσεων βασίστηκε σε πληροφορίες του πεδίου χρόνου-συχνότητας, οι οποίες αντλούνται με το φιλτράρισμα των ακατέργαστων ΗΕΓ δεδομένων και με τη βοήθεια των αιτιατών κυματιδίων Morlet, ενώ για την επακόλουθη ταξινόμηση των χαρακτηριστικών αναπτύχθηκαν και συγκρίθηκαν δύο αξιόπιστες μέθοδοι. Η πρώτη αφορά στη δημιουργία πιθανοθεωρητικών προτύπων κανονικής κατανομής για κάθε κατηγορία πρόθεσης κίνησης, με την τελική απόφαση ταξινόμησης να λαμβάνεται με εφαρμογή του απλού ταξινομητή του Bayes, ενώ η δεύτερη δημιουργεί ένα πρότυπο ταξινόμησης με βάση το θεωρητικό πλαίσιο των Μηχανών Διανυσμάτων Υποστήριξης (SVM). Στόχος του προβλήματος της δυαδικής ταξινόμησης είναι να αποφασίζεται σε ποια από τις δύο κατηγορίες ανήκει μία δεδομένη νοητική πρόθεση όσο το δυνατόν ταχύτερα και αξιόπιστα, έτσι ώστε ο σχεδιαζόμενος αλγόριθμος να εξυπηρετήσει ένα πλαίσιο ανατροφοδότησης της τελικής απόφασης στο χρήστη σε συνθήκες πραγματικού χρόνου. / Brain-Computer Interfaces (BCIs) demand the efficient processing of EEG data in order to translate one's thought or wish into a control signal that can be applied as input to external devices. Here we present a method to classify left from right hand movements, by extracting features from the data with Morlet wavelets and classifying with two different models, SVMs and Naive Bayes Classifier.

This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.

Background: In every organization, employees are an essential resource. For several reasons, employees are neglected by the organizations, which leads to employee turnover. Employee turnover causes considerable losses to the organization. Using machine learning algorithms and with the data in hand, a prediction of an employee’s future in an organization is made. Objectives: The aim of this thesis is to conduct a comparison study utilizing supervised machine learning algorithms such as Logistic Regression, Naive Bayes Classifier, Random Forest Classifier, and XGBoost to predict an employee’s future in a company. Using evaluation metrics models are assessed in order to discover the best efficient model for the data in hand. Methods: The quantitative research approach is used in this thesis, and data is analyzed using statistical analysis. The labeled data set comes from Kaggle and includes information on employees at a company. The data set is used to train algorithms. The created models will be evaluated on the test set using evaluation measures including Accuracy, Precision, Recall, F1 Score, and ROC curve to determine which model performs the best at predicting employee turnover. Results: Among the studied features in the data set, there is no feature that has a significant impact on turnover. Upon analyzing the results, the XGBoost classifier has better mean accuracy with 85.3%, followed by the Random Forest classifier with 83% accuracy than the other two algorithms. XGBoost classifier has better precision with 0.88, followed by Random Forest Classifier with 0.82. Both the Random Forest classifier and XGBoost classifier showed a 0.69 Recall score. XGBoost classifier had the highest F1 Score with 0.77, followed by the Random Forest classifier with 0.75. In the ROC curve, the XGBoost classifier had a higher area under the curve(AUC) with 0.88. Conclusions: Among the studied four machine learning algorithms, Logistic Regression, Naive Bayes Classifier, Random Forest Classifier, and XGBoost, the XGBoost classifier is the most optimal with a good performance score respective to the tested performance metrics. No feature is found majorly affect employee turnover.