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Video surveillance systems have been popular as a security tool for years, and the technological development helps monitoring accident-prone areas with the help of digital image processing.A thermal and a visual camera are being used in the surveillance project. The thermal camera is sensitive to the heat emitted by objects, and it is essential to employ the thermal camera as the visual camera is only useful in the presence of light. These cameras do not provide images of the same resolution. In order to extract the region of interest (ROI) of the visual camera, the images of these cameras need to have the same resolution; therefore the thermal images are processed in order to have the same size as the visual image.The ROI extraction is needed in order to reduce the data that needs to be transmitted. The region of interest is extracted from the visual image and the required processes are mostly done on the thermal image as it has lower resolution and therefore requires less computational processing. The image taken from the thermal camera is up scaled by using the nearest neighbor algorithm and it is zero-padded to make the resolutions of the two images equal, and then the region of interest is extracted by masking the result with the related converted version of visual image to YCbCr color space.

“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into three categories (e.g. global, local, and personalised) which have been widespread and used in the areas of data analysis and decision support in general, and in the areas of medicine and bioinformatics in particular. Most recently, the concept of personalised modelling has been widely applied to various disciplines such as personalised medicine, personalised drug design for known diseases (e.g. cancer, diabetes, brain disease, etc.) as well as for other modelling problems in ecology, business, finance, crime prevention, and so on. The philosophy behind the personalised modelling approach is that every person is different from others, thus he/she will benefit from having a personalised model and treatment. However, personalised modelling is not without issues, such as defining the correct number of neighbours or defining an appropriate number of features. As a result, the principal goal of this research is to study and address these issues and to create a novel framework and system for personalised modelling. The framework would allow users to select and optimise the most important features and nearest neighbours for a new input sample in relation to a certain problem based on a weighted variable distance measure in order to obtain more precise prognostic accuracy and personalised knowledge, when compared with global modelling and local modelling approaches.

With the ubiquity of mobile devices, there has been increased interest in determining how they can be used with location-based services. These types of services work best when the device has the ability to sense its location frequently, while still maintaining enough battery life to carry out its normal daily functions. Since the life of the battery on a mobile device is already so limited, ways of preserving that energy has become an important issue. The goal of this thesis is to demonstrate that Bluetooth can assist in providing energy efficient mobile device localization. This goal is achieved through a proposed Bluetooth Location Service Discovery framework which provides an API that can be incorporated into third party applications. The API allows BlackBerry devices to use surrounding Bluetooth devices in order to make a prediction about its current location. Predictions are completed with the assistance of the K-Nearest Neighbour data mining algorithm, and can be used as an alternative to invoking the GPS. The results obtained through experiments demonstrate that the results are comparable to those obtained with GPS.

“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into three categories (e.g. global, local, and personalised) which have been widespread and used in the areas of data analysis and decision support in general, and in the areas of medicine and bioinformatics in particular. Most recently, the concept of personalised modelling has been widely applied to various disciplines such as personalised medicine, personalised drug design for known diseases (e.g. cancer, diabetes, brain disease, etc.) as well as for other modelling problems in ecology, business, finance, crime prevention, and so on. The philosophy behind the personalised modelling approach is that every person is different from others, thus he/she will benefit from having a personalised model and treatment. However, personalised modelling is not without issues, such as defining the correct number of neighbours or defining an appropriate number of features. As a result, the principal goal of this research is to study and address these issues and to create a novel framework and system for personalised modelling. The framework would allow users to select and optimise the most important features and nearest neighbours for a new input sample in relation to a certain problem based on a weighted variable distance measure in order to obtain more precise prognostic accuracy and personalised knowledge, when compared with global modelling and local modelling approaches.

“Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into three categories (e.g. global, local, and personalised) which have been widespread and used in the areas of data analysis and decision support in general, and in the areas of medicine and bioinformatics in particular. Most recently, the concept of personalised modelling has been widely applied to various disciplines such as personalised medicine, personalised drug design for known diseases (e.g. cancer, diabetes, brain disease, etc.) as well as for other modelling problems in ecology, business, finance, crime prevention, and so on. The philosophy behind the personalised modelling approach is that every person is different from others, thus he/she will benefit from having a personalised model and treatment. However, personalised modelling is not without issues, such as defining the correct number of neighbours or defining an appropriate number of features. As a result, the principal goal of this research is to study and address these issues and to create a novel framework and system for personalised modelling. The framework would allow users to select and optimise the most important features and nearest neighbours for a new input sample in relation to a certain problem based on a weighted variable distance measure in order to obtain more precise prognostic accuracy and personalised knowledge, when compared with global modelling and local modelling approaches.

The k-nearest neighbour (k-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the k-NN classifier. In this thesis we investigate the use of alternative distances for the k-NN classifier. We start by introducing some background notions in statistical machine learning. We define the k-NN classifier and discuss Stone's theorem and the proof that k-NN is universally consistent on the normed space R^d. We then prove that k-NN is universally consistent if we take a sequence of random norms (that are independent of the sample and the query) from a family of norms that satisfies a particular boundedness condition. We extend this result by replacing norms with distances based on uniformly locally Lipschitz functions that satisfy certain conditions. We discuss the limitations of Stone's lemma and Stone's theorem, particularly with respect to quasinorms and adaptively choosing a distance for k-NN based on the labelled sample. We show the universal consistency of a two stage k-NN type classifier where we select the distance adaptively based on a split labelled sample and the query. We conclude by giving some examples of improvements of the accuracy of classifying various datasets using the above techniques.

The Swedish educational reforms of the 1990s introduced a choice- and voucher-based system, which allowed students to choose schools regardless of their proximity to them. As a consequence, new opportunities for geographical disparities in educational provisions as well as in home-to- school mobilities have emerged. The following thesis addresses this development by focusing on compulsory school (grade 9) students’ home-to-school mobility patterns. More specifically, a Bourdieusian lens is applied to understand mobility in terms of both physical and social space. In contrast to the Bourdieusian tradition, articulations between social and physical space are operationalized by constructing individually defined, scalable neighbourhoods. The software EquiPop is used to compute neighbourhood context neighbours in the municipality of Stockholm (n = 779 079) using the k-nearest neighbour algorithm (k = 1 600). A k-means cluster analysis is applied to construct income-based neighbourhood types. On this basis, this thesis asks about the localizations and positions of schools and students as well as about the mobility patterns and predictors of students residing in low-income, and thus economic capital deprived, neighbourhoods (n = 2 346). Utilizing register data, the study finds an unequal distribution of educational provisions in relation to different providers, i.e. municipal schools and independent schools, as well as different school types. Furthermore, the results indicate that students from low-income neighbourhoods are unequally mobilized dependent on migration background and the educational background of mothers. Moreover, independent schools have been found to be a attractive alternative for students from low-income neighbourhoods. / Research project "On the outskirt of the school market" by Håkan Forsberg

Yes / Automatic gender classification has become a topic of great interest to the visual computing research community in recent times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person’s smile. Thus, we show that the complex dynamics of a smile on someone’s face bear much relation to the person’s gender. To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here, we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person’s smile.

No / This chapter is concerned with the discussion of a computational framework to aid with gender classification in an automated fashion using the dynamics of a smile. The computational smile dynamics framework we discuss here uses the spatio-temporal changes on the face during a smile. Specifically, it uses a set of spatial and temporal features on the overall face. These include the changes in the area of the mouth, the geometric flow around facial features and a set of intrinsic features over the face. These features are explicitly derived from the dynamics of the smile. Based on it, a number of distinct dynamic smile parameters can be extracted which can then be fed to a machine learning algorithm for gender classification.