Machine Learning With Big Data: Challenges and Approaches

L’Heureux, Alexandra; Grolinger, Katarina; ElYamany, Hany F.; Capretz, Miriam A. M.

doi:10.1109/access.2017.2696365

Cited by 689 publications

(395 citation statements)

References 80 publications

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“…Data analytics, based on machine learning (ML), relies on the so called data analytics pipeline [37], which is a string of data and process manipulations aimed at making ML cope with Big Data [38]. One of the crucial steps in that pipeline is the so called "processing manipulations" which focuses on modifying how data are processed and stored.…”

Section: Data Analyticsmentioning

confidence: 99%

Age of Information: A New Concept, Metric, and Tool

2017

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Age of information (AoI) was introduced in the early 2010s as a notion to characterize the freshness of the knowledge a system has about a process observed remotely. AoI was shown to be a fundamentally novel metric of timeliness, significantly different, to existing ones such as delay and latency. The importance of such a tool is paramount, especially in contexts other than transport of information, since communication takes place also to control, or to compute, or to infer, and not just to reproduce messages of a source. This volume comes to present and discuss the first body of works on AoI and discuss future directions that could yield more challenging and interesting research.1 In this volume we take into consideration works that have been published no later than June 2017. freshness of such information is paramount in a wide range of information, communication, and control systems. By now, age has been studied with considerable diversity of systems, being as a concept, a performance metric, and a tool.The purpose of this volume is to present a critical summary of this first body of works performed on AoI and discuss future research directions. Already at this early point we need to put down our first disclaimer: we have chosen to treat the early works with significantly more detail, going deeper in the derivations and presenting more results and insights from them than we do with more recent works. The reason for this is to achieve a tutorial nature in the volume, which can provide a solid ground of the AoI as a concept. Moreover, the first works, which we chose to present in more detail than the rest, aim to provide fundamentally new knowledge in the premise of maintaining information fresh in a system. This basic goal opens up a wide range of communication contexts that span from estimation and prediction, to applications such as vehicular networks and information caching, to name a few.With this in mind, we begin this volume presenting the AoI concept as it was originally introduced. For this, we discuss the original models of Kaul, Gruteser and Yates of [33], considering a system where a source is transmitting packets containing status updates to a destination. The analysis presented is based on a simple queueing model. Already in that work the minimization of AoI was shown to be non-trivial for the source sampling methods studied. However, it had already become clear that timely updating a destination about a remote system is neither the same as maximizing the utilization of the communication system, nor of ensuring that generated status updates are received with minimum delay. This is because utilization can be maximized by making the source send updates as fast as possible which would lead to the destination receiving delayed statuses because messages are backlogged in the communication system studied. In this case, delay suffered by the stream of status updates can be reduced by decreasing the rate of updates. Alternatively, decreasing the update rate can also lead to the destination having unnecessa...

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Section: Data Analyticsmentioning

confidence: 99%

Age of Information: A New Concept, Metric, and Tool

2017

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“…This initial analysis demonstrates that deep learning can provide improved results when compared to other machine learning techniques while potentially using more parsimonious feature sets allowing for greater ease of implementation in clinical settings. Deep learning has often been found to significantly outperform other machine learning techniques as the size of the dataset increases (L’Heureux et al, 2017), and as such finding even a small advantage for deep learning in this fairly small (by deep learning standards) dataset leads us to speculate that deep learning will perform significantly better than other techniques on larger datasets.…”

Section: Discussionmentioning

confidence: 99%

Differential Treatment Benefit Prediction For Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

Mehltretter

Fratila²,

Benrimoh

et al. 2019

Preprint

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BackgroundDepression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability.MethodsUsing STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature selection. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naïve approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population.ResultsOur deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% improvement in population remission rate (p= 0.01, C.I. 2.48% ± .5%).ConclusionOur model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from a number of treatment options. These models may have significant real-world clinical implications.

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“…The developed techniques based on machine learning [12,13] and deep learning [14][15][16] have generally been applied in estimating the defect severity of bearings and in diagnosing those defects under varying conditions. These approaches are an effective solution for big data analytics, which are common nowadays [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network

Nguyen

Kim

2018

Applied Sciences

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Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults.

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Machine Learning With Big Data: Challenges and Approaches

Cited by 689 publications

References 80 publications

Age of Information: A New Concept, Metric, and Tool

Age of Information: A New Concept, Metric, and Tool

Differential Treatment Benefit Prediction For Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network

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