Data Science (DS)
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Belief Condensation Filtering For RssiBased State Estimation In Indoor Localization
(2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ... 
Poisson excess relative risk models: New implementations and software
(SORT, 20190301)Two new implementations for fitting Poisson excess relative risk methods are proposed for assumed simple models. This allows for estimation of the excess relative risk associated with a unique exposure, where the background ... 
Anxiety and depressive symptoms are related to core symptoms, general helath outcome and medical comorbidities in eating disorders
(European Eating Disorders Review, 201904)Objective: The goal of this study is to identify potential factors that have a significant effect on anxiety and depression of patients with eating disorders (ED) using the betabinomial regression (BBR) approach on a broad ... 
On the evaluation and selection of classifier learning algorithms with crowdsourced data
(Applied Soft Computing, 20190216)In many current problems, the actual class of the instances, the ground truth, is unavail able. Instead, with the intention of learning a model, the labels can be crowdsourced by harvesting them from different annotators. ... 
COPD classification models and mortality prediction capacity
(International Journal of COPD, 2019)Our aim was to assess the impact of comorbidities on existing COPD prognosis scores. Patients and methods: A total of 543 patients with COPD (FEV1 < 80% and FEV1/ FVC <70%) were included between January 2003 and January ... 
Predictors of one and two years’ mortality in patients with colon cancer: a prospective cohort study
(Plos One, 2018)Tools to aid in the prognosis assessment of colon cancer patients in terms of risk of mortality are needed. Goals of this study are to develop and validate clinical prediction rules for 1and 2year mortality in these ... 
Kmeans for massive data
(20190430)The $K$means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability and competitive computational complexity, when ... 
Early classification of time series using multiobjective optimization techniques
(Information Sciences, 20190423)In early classification of time series the objective is to build models which are able to make classpredictions for time series as accurately and as early as possible, when only a part of the series is available. It is ... 
Mallows and generalized Mallows model for matchings
(Bernoulli, 20190225)The Mallows and Generalized Mallows Models are two of the most popular probability models for distribu tions on permutations. In this paper, we consider both models under the Hamming distance. This models can be seen as ... 
perm mateda: A matlab toolbox of estimation of distribution algorithms for permutationbased combinatorial optimization problems
(ACM Transactions on Mathematical Software, 2018)Permutation problems are combinatorial optimization problems whose solutions are naturally codified as permutations. Due to their complexity, motivated principally by the factorial cardinality of the search space of ... 
Aggregated outputs by linear models: An application on marine litter beaching prediction
(Information Sciences, 20190101)In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning ... 
A betabinomial mixedeffects model approach for analysing longitudinal discrete and bounded outcomes
(Biometrical Journal, 201805)Patientreported outcomes (PROs) are currently being increasingly used as primary outcome measures in observational and experimental studies since they inform clinicians and researchers about the healthstatus of patients ... 
Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems
(Applied Soft Computing, 20170228)The advent of the socalled Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very di verse approaches. In this context this work focuses on the automatic con ... 
A statistical framework for radiation dose estimation with uncertainty quantification from the γH2AX assay
(PLoS ONE, 20181128)Over the last decade, the γ–H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double–strand–breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure ... 
On the relevance of preprocessing in predictive maintenance for dynamic systems
(Predictive Maintenance in Dynamic Systems, 2018)The complexity involved in the process of realtime datadriven monitoring dynamic systems for predicted maintenance is usually huge. With more or less indepth any datadriven approach is sensitive to data preprocessing, ... 
Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation
(Analytica Chimica Acta, 2018)The physicochemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. Online supervision of the turbidity point by means of ... 
Spatiotemporal information coupling in network navigation
(IEEE Transactions on Information Theory, 201812)Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging locationbased applications. In such cooperative networks, the positional information ... 
Crowd Learning with Candidate Labeling: an EMbased Solution
(Conference of the Spanish Association for Artificial Intelligence, 20180927)Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case ... 
A review on distance based time series classification
(Data Mining and Knowledge Discovery,, 20181101)Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and ... 
Detection of Sand Dunes on Mars Using a Regular Vinebased Classification Approach
(Knowledge Based Systems, 201808)This paper deals with the problem of detecting sand dunes from remotely sensed images of the surface of Mars. We build on previous approaches that propose methods to extract informative features for the classification of ...