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Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields. In some cases they allow constructing more precise forecasting models, leveraging the predictive potential of many variables. Unfortunately, in practice we do not know which observed predictors have a direct impact on the target variable. Moreover, adding unrelated variables may diminish the quality...
Problems associated with cell biochemistry are nowadays of general concern. In particular, much attention is being paid to the problem of changes in cytosolic ATP Levels. ATeam, a genetically encoded fluorescence resonance energy transfer (FRET)-based biological indicator, can monitor the fluorescence emission ratio against ATP concentration. Up to now, change curve has always acted as a bridge between...
Hardware failures in cloud data centers may cause substantial losses to cloud providers and cloud users. Therefore, the ability to accurately predict when failures occur is of paramount importance. In this paper, we present FailureSim, a simulator based on CloudSim that supports failure prediction. FailureSim obtains performance related information from the cloud and classifies the status of the hardware...
The Big data challenge includes dealing with a big number of heterogeneous and multidimensional datasets of all possible sizes not only with data of big size. As a result a huge number of Machine Learning (ML) tasks, which must be solved dramatically exceeds the number of data scientists who can solve these tasks. Next many ML tasks require critical input from subject matter experts (SME) and end...
Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages...
Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning...
In this paper, we discuss one of the most common tasks faced by marketers when faced with resource and time constraints, namely, consumer prioritization with the objective of optimizing one or more marketing key performance indicators such as consumer conversion. A key element in building predictive models is the ability to introduce features that capture historical user behaviors in an effective...
This paper proposes a new model for predicting the optimal warfarin dosing for African American patients. The prediction model is created using the multivariable regression method. The accuracy of dosing prediction is directly related to patient's safety. We show that the proposed model has better accuracy compare to all other available prediction methods for optimal dosing of warfarin.
In this paper we explore the possibility of automatic model selection in the supervised learning framework with the use of prediction intervals. First we compare two families of non-parametric approaches of constructing prediction intervals for arbitrary regression models. The first family of approaches is based on the idea of explaining the total prediction error as a sum of the model's error and...
Maintaining the financial sustainability of healthcare provision makes developments in e-systems of the utmost priority in healthcare. In particular, it leads to a radical review of healthcare delivery for the future as personalised, preventive, predictive and participatory, or p-Health. It is a vision that places e-systems at the core of healthcare delivery, in contrast to current practice . This...
In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system intelligence modeling was presented. In the processor, wavelet-fuzzy technique and neural network technique are combined. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function...
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