Traffic Flow Prediction Using Convolutional Neural Network accelerated by Spark Distributed Cluster

Obtain information from historical data to forecast traffic flow in a city can be difficult because a precision forecasting demands large amount of data and accurate pattern analysis. Meanwhile, it is also meaningful because it provides a detailed and accurate point-to-point prediction for users. In this project, I use CNN (Convolutional Neural Network) to train the model based on the images captured by webcams in New York City. Then I deploy the training process on a Spark distributed Cluster so that the whole training process is accelerated. To efficiently combine CNN and Apache Spark, the prediction model is re-designed and optimized, and the distributed cluster is tuned. By using 5-fold validation, multiple test results are presented to provides a support for the analysis about the model optimization and distributed cluster tuning. The aim of this project is to find the most accurate prediction model for the traffic flow prediction with acceptable time cost

Plateforme informatique pour l'assistance à l'autonomie à domicile de personnes âgées

RÉSUMÉ : Ambient Assisted Living (AAL) en général et Activity Recognition (AR) en particulier sont des domaines de recherche actifs qui visent à aider les personnes dans leurs activités de la vie quotidienne (AVQ). Au cours des dernières années, nous avons constaté un intérêt accru pour leur applicabilité aux personnes âgées vivant en milieu rural qui perdent lentement leur autonomie en raison du vieillissement et aux maladies chroniques. Une avenue de recherche importante consiste à agréger et à rechercher des corrélations entre les données physiologiques qui servent à surveiller la santé des personnes âgées, leurs AVQ, leurs mouvements et toute autre donnée pouvant être recueillis sur leur environnement immédiat. Dans ce travail, nous abordons la possibilité de développer une plateforme non intrusive et abordable en raison de l'absence d'une telle plateforme. Elle est basée sur des capteurs de santé, de mouvement, d'activité et de localisation. En outre, nous discutons des principaux concepts derrière la création d'une architecture en couches, flexible et hautement modulaire qui se concentre sur la façon dont l'intégration de données de capteurs combinés peut être réalisée. À l'aide d'un prototype d'application de téléphonie mobile, nos travaux ont montré que nous pouvons intégrer de nombreuses technologies non invasives qui ne sont pas nécessairement les plus récentes, mais les plus abordables, évolutives et prêtes à être déployées dans des environnements réels. Un autre domaine de recherche découlant de ces avancées est de savoir comment la technologie et l'analyse pourraient bénéficier à la prévention et au traitement des maladies chroniques chez le nombre croissant de personnes âgées ayant des problèmes de santé. De nombreuses architectures sont proposées dans la littérature, mais elles manquent de modularité et de flexibilité pour différents types de capteurs. À cette fin, nous proposons une architecture à quatre couches et hautement modulaire pour l'analyse de la santé des personnes âgées. Finalement, nous évaluons l'approche en implémentant une partie de l'architecture sur des nœuds de brouillard et le cloud. De plus, nous déployons ces capteurs abordables, de qualité, et accessibles au grand public dans un appartement afin d'avancer vers l'utilisation du système proposé. Des données recueillies sont utilisées comme un test préliminaire pour évaluer les capacités de la plate-forme. En utilisant les données collectées lors de l'étape de validation, nous effectuons des prévisions d'une semaine dans le futur pour des séries univariées en utilisant des méthodes classiques populaires et les méthodes d'apprentissage en profondeur les plus récentes. Une comparaison de précision est présentée. -- Mot(s) clé(s) en français : IoT, suivi à distance des personnes âgées, santé intelligente et connectée, analyse, assistance à la vie ambiante, capteurs, intelligence artificielle. -- ABSTRACT : Ambient Assisted Living (AAL) in general and Activity Recognition (AR) in particular are active fields of research that aim at assisting people in their Activities of Daily Living (ADL). In recent years, we have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. One research venue is to aggregate and seek for correlations between the physiological data that serves to monitor the health of the elderly, their ADLs, their movements and any other data that may be collected about their immediate environment. In this work, we are tackling the possibility of developing a non-intrusive and affordable platform due to the lack of such a platform. It is based on embedded health, movement, activity and location sensors. Furthermore, we discuss the main concepts behind the creation of a layered, flexible and highly modular architecture that focuses on how the integration of newly combined sensor data can be achieved. Using a mobile phone application prototype, our work has shown that we can integrate many non-invasive technologies that are not necessarily the newest, but the most affordable, scalable and ready to be deployed in real life settings. Another researched venue deriving from these advances is how the technology and analytics could benefit the prevention and treatment of chronic diseases in the escalating number of elderly people experiencing health issues. Many architectures are proposed in the literature, but they lack modularity and flexibility for different types of sensors. To that end, we propose a four layered and highly modular architecture for health analytics of elderly people. In the final analysis, we evaluate the approach by implementing part of the architecture on fog nodes and the cloud. Moreover, we deploy these affordable consumer grade sensors in an apartment in order to move toward the use of the system proposed. The data collected from this experiment is used as a preliminary test of the capabilities of the platform. We perform univariate series forecasting using a popular classical methods and the more recent deep learning methods by using the data collected in the validation stage. An accuracy comparison is presented. -- Mot(s) clé(s) en anglais : IoT, remote elderly monitoring, smart and connected Health, analytics, ambient assisted living, sensors

Scalable Profiling and Visualization for Characterizing Microbiomes

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information. The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters are linked to changes in microbiome compositions. In this work we propose novel approaches for the creation and interpretation of microbial community profiles. This includes: (a) a cloud-based, distributed computational system that generates detailed community profiles by processing large DNA sequencing datasets against large reference genome collections, (b) the creation of Microbiome Maps: interpretable, high-resolution visualizations of community profiles, and (c) a machine learning framework for characterizing microbiomes from the Microbiome Maps that delivers deep insights into microbial communities. The proposed approaches have been implemented in three software solutions: Flint, a large scale profiling framework for commercial cloud systems that can process millions of DNA sequencing fragments and produces microbial community profiles at a very low cost; Jasper, a novel method for creating Microbiome Maps, which visualizes the abundance profiles based on the Hilbert curve; and Amber, a machine learning framework for characterizing microbiomes using the Microbiome Maps generated by Jasper with high accuracy. Results show that Flint scales well for reference genome collections that are an order of magnitude larger than those used by competing tools, while using less than a minute to profile a million reads on the cloud with 65 commodity processors. Microbiome maps produced by Jasper are compact, scalable representations of extremely complex microbial community profiles with numerous demonstrable advantages, including the ability to display latent relationships that are hard to elicit. Finally, experiments show that by using images as input instead of unstructured tabular input, the carefully engineered software, Amber, can outperform other sophisticated machine learning tools available for classification of microbiomes