The maturing of Artificial intelligence provides great opportunities for healthcare, but also comes with new challenges. For Artificial intelligence to be adequate a comprehensive analysis of the data is necessary along with testing the data in multiple algorithms to determine which algorithm is appropriate to use. In this study collection of data has been gathered that consists of patients who have either been readmitted or not readmitted to hospital within 30-days after being admitted. The data has then been analyzed and compared in different algorithms to determine the most appropriate algorithm to use.

O primeiro livro de Jorge Amado traduzido para o idioma inglês foi publicado nos Estados Unidos em 1945, pela Alfred A. Knopf Publishers, por meio de patrocínio do Departamento de Estado americano, que mantinha um programa de intercâmbio cultural como parte da Política de Boa Vizinhança do presidente Roosevelt. A literatura traduzida era, então, vista como um caminho para compreender o outro. Criou-se, a partir daí, um padrão de comportamento que perdurou por décadas. Érico Veríssimo, Gilberto Freyre, Alfred e Blanche Knopf, Samuel Putnam e Harriet de Onís foram atores importantes nesse cenário. Apesar de seu contínuo posicionamento de esquerda, após desligar-se do Partido Comunista no final da década de 1950, Jorge Amado tornou-se um bestseller norteamericano, como resultado dessa vertente diplomática e do renovado projeto de tradução (e de amizade) de Alfred A. Knopf. Entretanto, outras redes de influência também atuavam sobre a recepção da obra do escritor, fazendo com que ela fosse assimilada de forma própria, metonímica, diferente da que ocorreu em países do leste europeu, por exemplo. Esta pesquisa investigou a relação entre os atores mencionados, tais redes de influência e a representação cultural do Brasil na literatura traduzida de Jorge Amado nos Estados Unidos. / The first book by Jorge Amado in English translation was published in the United States in 1945 by Alfred A. Knopf Publishers, under the auspices of the U.S. State Department, who sponsored a cultural interchange program as part of president Roosevelts the Good Neighbor Policy. Translated literature was seen, at the time, as a way of understanding the other. Érico Veríssimo, Gilberto Freyre, Alfred and Blanche Knopf, Samuel Putnam and Harriet de Onís were actors in this scenario. In spite of his support of the tenets of the political left, after leaving the Communist Party in the late 1950s Jorge Amado became an American bestseller, a result of such diplomatic movement as well as Alfred A. Knops translation project. Nevertheless, other influence networks also affected the authors reception in the United States, which turned out to be quite different from that in the Eastern Europe, for instance. This research investigates the relation between the aforementioned actors, such influence network and Brazils cultural representation in Jorge Amados translated literature in the United States.

Local feature detection and its applications in different problems are quite popular in vision research. In order to analyze a scene, its invariant features, which are distinguishable in many views of this scene, are used in pose estimation, object detection and augmented reality. However, required performance metrics might change according to the application type / in general, the main metrics are accepted as accuracy and computational complexity. The contributions in this thesis provide improving these metrics and can be divided into three parts, as local feature detection, local feature description and description matching in different views of the same scene. In this thesis an efficient feature detection algorithm with sufficient repeatability performance is proposed. This detection method is convenient for real-time applications. For local description, a novel local binary pattern outperforming state-of-the-art binary pattern is proposed. As a final task, a fuzzy decision tree method is presented for approximate nearest neighbor search. In all parts of the system, computational efficiency is considered and the algorithms are designed according to limited processing time. Finally, an overall system capable of matching different views of the same scene has been proposed and executed in a mobile platform. The results are quite promising such that the presented system can be used in real-time applications, such as augmented reality, object retrieval, object tracking and pose estimation.

This thesis tries to determine some of the driving force behind Swedish foreign direct in-vestments into the Baltic counties. The analysis is performed in three steps, first we analyze global FDI into transitional economies, and afterwards we look at Swedish FDI and com-pare it with German FDI. The determinants examined are index of economic freedom, R&D intensity, trade balance, wage level and proximity. The analyzed period is form 1995 to 2005. The analysis use data on the following transition countries Latvia, Lithuania, Esto-nia, Poland, Hungary, Czech Republic, Slovak Republic, Slovenia, Croatia, Romania and Bulgaria. The results show that the determinants vary across the countries. The motives of Swedish and German investors differ. Thus, for Swedish investors R&D, economical free-dom and trade balance are the influencing factors, but for Germany only trade balance and wage level are important. The conclusion is that different determinants triggers foreign di-rect investment in transitional economies in different ways.

The purpose of this thsis is to develop a vision perception based Intelligent Vehicle Driving Assistant System ( IVDAS ), which utilizes CCD camera to capture the movement of vehicle and road image on DSP-Based . According to daytime and night time, we analyzed the full information in the image to acquire the important and proper characteristics about lane mark and vehicle. There are two sub-systems in our system , including Lane Mark Detection and Vehicle Detection. The main goal is to identify if there are existing vehicles in the front of or near our vehicle. This system can provide information for the Intelligent Vehicle to make decision to avoid accident happening and assisted driver in driving safely.

Among the various remote sensing methods that can be used to map forest areas, the K Nearest Neighbor (KNN) supervised classification method is becoming increasingly popular for creating forest inventories in some countries. In this study, the utility of the KNN algorithm is evaluated for forest/non-forest/water stratification. Antalya is selected as the study area. The data used are composed of Landsat TM and Landsat ETM satellite images, acquired in 1987 and 2002, respectively, SRTM 90 meters digital elevation model (DEM) and land use data from the year 2003. The accuracies of different modifications of the KNN algorithm are evaluated using Leave One Out, which is a special case of K-fold cross-validation, and traditional accuracy assessment using error matrices. The best parameters are found to be Euclidean distance metric, inverse distance weighting, and k equal to 14, while using bands 4, 3 and 2. With these parameters, the cross-validation error is 0.009174, and the overall accuracy is around 86%. The results are compared with those from the Maximum Likelihood algorithm. KNN results are found to be accurate enough for practical applicability of this method for mapping forest areas.

Many interesting applications involve decision prediction based on a time-series sequence or a set of time-series sequences, which are referred to as time-series classification problems. Past classification analysis research predominately focused on constructing a classification model from training instances whose attributes are atomic and independent. Direct application of traditional classification analysis techniques to time-series classification problems requires the transformation of time-series data into non-time-series data attributes by applying some statistical operations (e.g., average, sum, etc). However, such statistical transformation often results in information loss. In this thesis, we proposed the Time-Series Classification (TSC) technique, based on the nearest neighbor classification approach. The result of empirical evaluation showed that the proposed time-series classification technique had better performance than the statistical-transformation-based approach.

<p>This thesis tries to determine some of the driving force behind Swedish foreign direct in-vestments into the Baltic counties. The analysis is performed in three steps, first we analyze global FDI into transitional economies, and afterwards we look at Swedish FDI and com-pare it with German FDI. The determinants examined are index of economic freedom, R&D intensity, trade balance, wage level and proximity. The analyzed period is form 1995 to 2005. The analysis use data on the following transition countries Latvia, Lithuania, Esto-nia, Poland, Hungary, Czech Republic, Slovak Republic, Slovenia, Croatia, Romania and Bulgaria. The results show that the determinants vary across the countries. The motives of Swedish and German investors differ. Thus, for Swedish investors R&D, economical free-dom and trade balance are the influencing factors, but for Germany only trade balance and wage level are important. The conclusion is that different determinants triggers foreign di-rect investment in transitional economies in different ways.</p>

Foreign exchange (FX) rate forecasting has been a challenging area of study in the past. Various linear and nonlinear methods have been used to forecast FX rates. As the currency data are nonlinear and highly correlated, forecasting through nonlinear dynamical systems is becoming more relevant. The nearest neighbor (NN) algorithm is one of the most commonly used nonlinear pattern recognition and forecasting methods that outperforms the available linear forecasting methods for the high frequency foreign exchange data. The basic idea behind the NN is to capture the local behavior of the data by selecting the instances having similar dynamic behavior. The most relevant k number of histories to the present dynamical structure are the only past values used to predict the future. Due to this reason, NN algorithm is also known as the k-nearest neighbor algorithm (k-NN). Here k represents the number of chosen neighbors. In the k-nearest neighbor forecasting procedure, similar instances are captured through a distance function. Since the forecasts completely depend on the chosen nearest neighbors, the distance plays a key role in the k-NN algorithm. By choosing an appropriate distance, we can improve the performance of the algorithm significantly. The most commonly used distance for k-NN forecasting in the past was the Euclidean distance. Due to possible correlation among vectors at different time frames, distances based on deterministic vectors, such as Euclidean, are not very appropriate when applying for foreign exchange data. Since Mahalanobis distance captures the correlations, we suggest using this distance in the selection of neighbors. In the present study, we used five different foreign currencies, which are among the most traded currencies, to compare the performances of the k-NN algorithm with traditional Euclidean and Absolute distances to performances with the proposed Mahalanobis distance. The performances were compared in two ways: (i) forecast accuracy and (ii) transforming their forecasts in to a more effective technical trading rule. The results were obtained with real FX trading data, and the results showed that the method introduced in this work outperforms the other popular methods. Furthermore, we conducted a thorough investigation of optimal parameter choice with different distance measures. We adopted the concept of distance based weighting to the NN and compared the performances with traditional unweighted NN algorithm based forecasting. Time series forecasting methods, such as Auto regressive integrated moving average process (ARIMA), are widely used in many ares of time series as a forecasting technique. We compared the performances of proposed Mahalanobis distance based k-NN forecasting procedure with the traditional general ARIM- based forecasting algorithm. In this case the forecasts were also transformed into a technical trading strategy to create buy and sell signals. The two methods were evaluated for their forecasting accuracy and trading performances. Multi-step ahead forecasting is an important aspect of time series forecasting. Even though many researchers claim that the k-Nearest Neighbor forecasting procedure outperforms the linear forecasting methods for financial time series data, and the available work in the literature supports this claim with one step ahead forecasting. One of our goals in this work was to improve FX trading with multi-step ahead forecasting. A popular multi-step ahead forecasting strategy was adopted in our work to obtain more than one day ahead forecasts. We performed a comparative study on the performance of single step ahead trading strategy and multi-step ahead trading strategy by using five foreign currency data with Mahalanobis distance based k-nearest neighbor algorithm.

This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation. / text