34 research outputs found

    Robust and efficient approach to feature selection with machine learning

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    Most statistical analyses or modelling studies must deal with the discrepancy between the measured aspects of analysed phenomenona and their true nature. Hence, they are often preceded by a step of altering the data representation into somehow optimal for the following methods.This thesis deals with feature selection, a narrow yet important subset of representation altering methodologies.Feature selection is applied to an information system, i.e., data existing in a tabular form, as a group of objects characterised by values of some set of attributes (also called features or variables), and is defined as a process of finding a strict subset of them which fulfills some criterion.There are two essential classes of feature selection methods: minimal optimal, which aim to find the smallest subset of features that optimise accuracy of certain modelling methods, and all relevant, which aim to find the entire set of features potentially usable for modelling. The first class is mostly used in practice, as it adheres to a well known optimisation problem and has a direct connection to the final model performance. However, I argue that there exists a wide and significant class of applications in which only all relevant approaches may yield usable results, while minimal optimal methods are not only ineffective but even can lead to wrong conclusions.Moreover, all relevant class substantially overlaps with the set of actual research problems in which feature selection is an important result on its own, sometimes even more important than the finally resulting black-box model. In particular this applies to the p>>n problems, i.e., those for which the number of attributes is large and substantially exceeds the number of objects; for instance, such data is produced by high-throughput biological experiments which currently serve as the most powerful tool of molecular biology and a fundament of the arising individualised medicine.In the main part of the thesis I present Boruta, a heuristic, all relevant feature selection method. It is based on the concept of shadows, by-design random attributes incorporated into the information system as a reference for the relevance of original features in the context of whole structure of the analysed data. The variable importance on its own is assessed using the Random Forest method, a popular ensemble classifier.As the performance of the Boruta method turns out insatisfactory for some important applications, the following chapters of the thesis are devoted to Random Ferns, an ensemble classifier with the structure similar to Random Forest, but of a substantially higher computational efficiency. In the thesis, I propose a substantial generalisation of this method, capable of training on generic data and calculating feature importance scores.Finally, I assess both the Boruta method and its Random Ferns-based derivative on a series of p>>n problems of a biological origin. In particular, I focus on the stability of feature selection; I propose a novel methodology based on bootstrap and self-consistency. The results I obtain empirically confirm the validity of aforementioned effects characteristic to minimal optimal selection, as well as the efficiency of proposed heuristics for all relevant selection.The thesis is completed with a study of the applicability of Random Ferns in musical information retrieval, showing the usefulness of this method in other contexts and proposing its generalisation for multi-label classification problems.W większości zagadnień statystycznego modelowania istnieje problem niedostosowania zebranych danych do natury badanego zjawiska; co za tym idzie, analiza danych jest zazwyczaj poprzedzona zmianą ich surowej formy w optymalną dla dalej stosowanych metod.W rozprawie zajmuję się selekcją cech, jedną z klas zabiegów zmiany formy danych. Dotyczy ona systemów informacyjnych, czyli danych dających się przedstawić w formie tabelarycznej jako zbiór obiektów opisanych przez wartości zbioru atrybutów (nazywanych też cechami), oraz jest zdefiniowana jako proces wydzielenia w jakimś sensie optymalnego podzbioru atrybutów.Wyróżnia się dwie zasadnicze grupy metod selekcji cech: poszukujących możliwie małego podzbioru cech zapewniającego możliwie dobrą dokładność jakiejś metody modelowania (minimal optimal) oraz poszukujących podzbioru wszystkich cech, które niosą istotną informację i przez to są potencjalnie użyteczne dla jakiejś metody modelowania (all relevant). Tradycyjnie stosuje się prawie wyłącznie metody minimal optimal, sprowadzają się one bowiem w prosty sposób do znanego problemu optymalizacji i mają bezpośredni związek z efektywnością finalnego modelu. W rozprawie argumentuję jednak, że istnieje szeroka i istotna klasa problemów, w których tylko metody all relevant pozwalają uzyskać użyteczne wyniki, a metody minimal optimal są nie tylko nieefektywne ale często prowadzą do mylnych wniosków. Co więcej, wspomniana klasa pokrywa się też w dużej mierze ze zbiorem faktycznych problemów w których selekcja cech jest sama w sobie użytecznym wynikiem, nierzadko ważniejszym nawet od uzyskanego modelu. W szczególności chodzi tu o zbiory klasy p>>n, to jest takie w których liczba atrybutów w~systemie informacyjnym jest duża i znacząco przekracza liczbę obiektów; dane takie powszechnie występują chociażby w wysokoprzepustowych badaniach biologicznych, będących obecnie najpotężniejszym narzędziem analitycznym biologii molekularnej jak i fundamentem rodzącej się zindywidualizowanej medycyny.W zasadniczej części rozprawy prezentuję metodę Boruta, heurystyczną metodę selekcji zmiennych. Jest ona oparta o koncepcję rozszerzania systemu informacyjnego o cienie, z definicji nieistotne atrybuty wytworzone z oryginalnych cech przez losową permutację wartości, które są wykorzystywane jako odniesienie dla oceny istotności oryginalnych atrybutów w kontekście pełnej struktury analizowanych danych. Do oceny ważności cech metoda wykorzystuje algorytm lasu losowego (Random Forest), popularny klasyfikator zespołowy.Ponieważ wydajność obliczeniowa metody Boruta może być niewystarczająca dla pewnych istotnych zastosowań, w dalszej części rozprawy zajmuję się algorytmem paproci losowych, klasyfikatorem zespołowym zbliżonym strukturą do algorytmu lasu losowego, lecz oferującym znacząco lepszą wydajność obliczeniową. Proponuję uogólnienie tej metody, zdolne do treningu na generycznych systemach informacyjnych oraz do obliczania miary ważności atrybutów.Zarówno metodę Boruta jak i jej modyfikację wykorzystującą paprocie losowe poddaję w rozprawie wyczerpującej analizie na szeregu zbiorów klasy p>>n pochodzenia biologicznego. W szczególności rozważam tu stabilność selekcji; w tym celu formułuję nową metodę oceny opartą o podejście resamplingowe i samozgodność wyników. Wyniki przeprowadzonych eksperymentów potwierdzają empirycznie zasadność wspomnianych wcześniej problemów związanych z selekcją minimal optimal, jak również zasadność przyjętych heurystyk dla selekcji all relevant.Rozprawę dopełnia studium stosowalności algorytmu paproci losowych w problemie rozpoznawania instrumentów muzycznych w nagraniach, ilustrujące przydatność tej metody w innych kontekstach i proponujące jej uogólnienie na klasyfikację wieloetykietową

    Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living

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    Following the recent advances in technology and the growing use of mobile devices such as smartphones, several solutions may be developed to improve the quality of life of users in the context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g., accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver, which allow the acquisition of physical and physiological parameters for the recognition of different Activities of Daily Living (ADL) and the environments in which they are performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare tasks, based on the types of skills that people usually learn in early childhood, including feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping, watching TV, working, listening to music, cooking, eating and others. On the context of AAL, some individuals (henceforth called user or users) need particular assistance, either because the user has some sort of impairment, or because the user is old, or simply because users need/want to monitor their lifestyle. The research and development of systems that provide a particular assistance to people is increasing in many areas of application. In particular, in the future, the recognition of ADL will be an important element for the development of a personal digital life coach, providing assistance to different types of users. To support the recognition of ADL, the surrounding environments should be also recognized to increase the reliability of these systems. The main focus of this Thesis is the research on methods for the fusion and classification of the data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL in almost real-time, taking into account the large diversity of the capabilities and characteristics of the mobile devices available in the market. In order to achieve this objective, this Thesis started with the review of the existing methods and technologies to define the architecture and modules of the method for the identification of ADL. With this review and based on the knowledge acquired about the sensors available in off-the-shelf mobile devices, a set of tasks that may be reliably identified was defined as a basis for the remaining research and development to be carried out in this Thesis. This review also identified the main stages for the development of a new method for the identification of the ADL using the sensors available in off-the-shelf mobile devices; these stages are data acquisition, data processing, data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One of the challenges is related to the different types of data acquired from the different sensors, but other challenges were found, including the presence of environmental noise, the positioning of the mobile device during the daily activities, the limited capabilities of the mobile devices and others. Based on the acquired data, the processing was performed, implementing data cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of the research their implementation does not have influence in the results of the identification of the ADL and environments, as the features are extracted from a set of data acquired during a defined time interval and there are no missing values during this stage. The joint selection of the set of usable sensors and the identifiable set of tasks will then allow the development of a framework that, considering multi-sensor data fusion technologies and context awareness, in coordination with other information available from the user context, such as his/her agenda and the time of the day, will allow to establish a profile of the tasks that the user performs in a regular activity day. The classification method and the algorithm for the fusion of the features for the recognition of ADL and its environments needs to be deployed in a machine with some computational power, while the mobile device that will use the created framework, can perform the identification of the ADL using a much less computational power. Based on the results reported in the literature, the method chosen for the recognition of the ADL is composed by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron (MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural Networks (DNN). Data acquisition can be performed with standard methods. After the acquisition, the data must be processed at the data processing stage, which includes data cleaning and feature extraction methods. The data cleaning method used for motion and magnetic sensors is the low pass filter, in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform (FFT) was applied to extract the different frequencies. When the data is clean, several features are then extracted based on the types of sensors used, including the mean, standard deviation, variance, maximum value, minimum value and median of raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance and median of the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the five greatest distances between the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel- Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw data acquired from the microphone data; and the distance travelled calculated with the data acquired from the GPS receiver. After the extraction of the features, these will be grouped in different datasets for the application of the ANN methods and to discover the method and dataset that reports better results. The classification stage was incrementally developed, starting with the identification of the most common ADL (i.e., walking, running, going upstairs, going downstairs and standing activities) with motion and magnetic sensors. Next, the environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen, living room, hall, street and library. After the environments are recognized, and based on the different sets of sensors commonly available in the mobile devices, the data acquired from the motion and magnetic sensors were combined with the recognized environment in order to differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled, extracted from the GPS receiver data, allowing also to recognize the driving activity. After the implementation of the three classification methods with different numbers of iterations, datasets and remaining configurations in a machine with high processing capabilities, the reported results proved that the best method for the recognition of the most common ADL and activities without motion is the DNN method, but the best method for the recognition of environments is the FNN method with Backpropagation. Depending on the number of sensors used, this implementation reports a mean accuracy between 85.89% and 89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of environments, and equals to 100% for the recognition of activities without motion, reporting an overall accuracy between 85.89% and 92.00%. The last stage of this research work was the implementation of the structured framework for the mobile devices, verifying that the FNN method requires a high processing power for the recognition of environments and the results reported with the mobile application are lower than the results reported with the machine with high processing capabilities used. Thus, the DNN method was also implemented for the recognition of the environments with the mobile devices. Finally, the results reported with the mobile devices show an accuracy between 86.39% and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition of environments, and equal to 100% for the recognition of activities without motion, reporting an overall accuracy between 58.02% and 89.15%. Compared with the literature, the results returned by the implemented framework show only a residual improvement. However, the results reported in this research work comprehend the identification of more ADL than the ones described in other studies. The improvement in the recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum number of ADL and environments previously recognized was 13, while the number of ADL and environments recognized with the framework resulting from this research is 16. In conclusion, the framework developed has a mean improvement of 2.93% in the accuracy of the recognition for a larger number of ADL and environments than previously reported. In the future, the achievements reported by this PhD research may be considered as a start point of the development of a personal digital life coach, but the number of ADL and environments recognized by the framework should be increased and the experiments should be performed with different types of devices (i.e., smartphones and smartwatches), and the data imputation and other machine learning methods should be explored in order to attempt to increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro, giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS), que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas. O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo, esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os principais conceitos para o desenvolvimento do novo método de identificação das AVD, utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de dados, imputação de dados, extração de características, fusão de dados e extração de resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis. As diferentes características das pessoas podem igualmente influenciar a criação dos métodos, escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos, realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a extração de características, para iniciar a criação do novo método para o reconhecimento das AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo de tempo definido. A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados adquiridos com múltiplos sensores em coordenação com outras informações relativas ao contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para testes adicionais. Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de processamento de dados, que inclui os métodos de correção de dados e de extração de características. O método de correção de dados utilizado para sensores de movimento e magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências. Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de características que reporta melhores resultados. O módulo de classificação de dados foi incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir. Após a implementação dos três métodos de classificação com diferentes números de iterações, conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os resultados relatados provaram que o melhor método para o reconhecimento das atividades comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51% para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão global entre 85,89% e 92,00%. A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando que o método FNN requer um alto poder de processamento para o reconhecimento de ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados reportados com a máquina com alta capacidade de processamento utilizada no desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral entre 58,02% e 89,15%. Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93% na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os métodos de imputação e outros métodos de classificação de dados devem ser explorados de modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e ambientes

    Single-channel source separation using non-negative matrix factorization

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    RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

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    Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    A Human-Machine Framework for the Classification of Phonocardiograms

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    In this thesis, we present and evaluate a framework for combining machine learning algo- rithms, crowd workers, and experts in the classification of heart sound recordings. The development of a hybrid human-machine framework for heart sound recordings is moti- vated by the past success in utilizing human computation to solve problems in medicine as well as the use of human-machine frameworks in other domains. We describe the methods that decide when and how to escalate the analysis of heart sound recordings to different resources and incorporate their decision into a final classification. We present and discuss the results of the framework which was tested with a number of different machine classi- fiers and a group of crowd workers from Amazon’s Mechanical Turk. We also provide an evaluation of how crowd workers perform in various different heart sound analysis tasks, and how they compare with machine classifiers. In addition, we investigate how machine and human analysis are effected by different types of heart sounds and provide a strategy for involving experts when these methods are uncertain. We conclude that the use of a hybrid framework is a viable method for heart sound classification

    Pattern Recognition

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    Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

    Pertanika Journal of Science & Technology

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    Overlapped speech and music segmentation using singular spectrum analysis and random forests

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    Recent years have seen ever-increasing volumes of digital media archives and an enormous amount of user-contributed content. As demand for indexing and searching these resources has increased, and new technologies such as multimedia content management systems, en-hanced digital broadcasting, and semantic web have emerged, audio information mining and automated metadata generation have received much attention. Manual indexing and metadata tagging are time-consuming and subject to the biases of individual workers. An automated architecture able to extract information from audio signals, generate content-related text descriptors or metadata, and enable further information mining and searching would be a tangible and valuable solution. In the field of audio classification, audio signals may be broadly divided into speech or music. Most studies, however, neglect the fact that real audio soundtracks may have either speech or music, or a combination of the two, and this is considered the major hurdle to achieving high performance in automatic audio classification, since overlapping can contaminate relevant characteristics and features, causing incorrect classification or information loss. This research undertakes an extensive review of the state of the art by outlining the well-established audio features and machine learning techniques that have been applied in a broad range of audio segmentation and recognition areas. Audio classification systems and the suggested solutions for the mixed soundtracks problem are presented. The suggested solutions can be listed as follows: developing augmented and modified features for recognising audio classes even in the presence of overlaps between them; robust segmentation of a given overlapped soundtrack stream depends on an innovative method of audio decomposition using Singular Spectrum Analysis (SSA) that has been studied extensively and has received increasing attention in the past two decades as a time series decomposition method with many applications; adoption and development of driven classification methods; and finally a technique for continuous time series tasks. In this study, SSA has been investigated and found to be an efficient way to discriminate speech/music in mixed soundtracks by two different methods, each of which has been developed and validated in this research. The first method serves to mitigate the overlapping ratio between speech and music in the mixed soundtracks by generating two new soundtracks with a lower level of overlapping. Next, feature space is calculated for the output audio streams, and these are classified using random forests into either speech or music. One of the distinct characteristics of this method is the separation of the speech/music key features that lead to improve the classification performance. Nevertheless, that did encounter a few obstructions, including excessively long processing time, increased storage requirements (each frame symbolised by two outputs), and this all leads to greater computational load than previously. Meanwhile, the second method em-ploys the SSA technique to decompose a given audio signal into a series of Principal Components (PCs), where each PC corresponds to a particular pattern of oscillation. Then, the transformed well-established feature is measured for each PC in order to classify it into either speech or music based on the baseline classification system using a RF machine learning technique. The classification performance of real-world soundtracks is effectively improved, which is demonstrated by comparing speech/music recognition using conventional classification methods and the proposed SSA method. The second proposed and de-veloped method can detect pure speech, pure music, and mix with a much lower complexity level
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