Listar por tema "Predictive analytics"
Mostrando ítems 1-13 de 13
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Adaptive Novelty Detection over Contextual Data Streams at the Edge using One-class Classification
(2021)Online novelty detection is an emerging task in Edge Computing trying to identify novel concepts in contextual data streams which should be incorporated into predictive analytics and inferential models locally executed on ... -
Computationally efficient hierarchical Bayesian modeling framework for learning embedded model uncertainties
(2020)A hierarchical Bayesian modeling (HBM) framework has recently been developed for estimating the uncertainties in the parameters of physics-based models of systems, as well as propagating these uncertainties to estimate the ... -
Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach
(2022)Dynamic data-driven applications such as tracking and surveillance have emerged in the Internet of Things (IoT) environments. Such applications rely heavily on data generated by connected devices (e.g., sensors). Consequently, ... -
A delay-resilient and quality-aware mechanism over incomplete contextual data streams
(2016)We study the case of scheduling a Contextual Information Process (CIP) over incomplete multivariate contextual data streams coming from sensing devices in Internet of Things (IoT) environments. CIPs like data fusion, concept ... -
Distributed Localized Contextual Event Reasoning under Uncertainty
(2017)We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report, and react to a specific phenomenon. Each node (e.g., an unmanned vehicle or an ... -
Forecasting of day-ahead natural gas consumption demand in Greece using adaptive neuro-fuzzy inference system
(2020)(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate ... -
Knowledge reuse in edge computing environments
(2022)To cope with the challenge of managing numerous computing devices, humongous data volumes and models in Internet-of-Things environments, Edge Computing (EC) has emerged to serve latency-sensitive and compute-intensive ... -
Local & Federated Learning at the network edge for efficient predictive analytics
(2022)The ability to perform computation on devices present in the Internet of Things (IoT) and Edge Computing (EC) environments leads to bandwidth, storage, and energy constraints, as most of these devices are limited with ... -
A machine learning pipeline for predicting joint space narrowing in knee osteoarthritis patients
(2020)Osteoarthritis is the common form of arthritis in the knee (KOA). It is identified as one of the main causes of pain leading even to disability. To exploit the continuous increase in medical data concerning KOA, various ... -
Prediction of cement suspension groutability based on sand hydraulic conductivity
(2020)The experimental investigation reported herein aims toward the development of groutability prediction models based on the hydraulic conductivity of sand and other parameters affecting the groutability of cement suspensions. ... -
Prediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiative
(2020)Knee Osteoarthritis(KOA) is a serious disease that causes a variety of symptoms, such as severe pain and it is mostly observed in the elder people. The main goal of this study is to build a prognostic tool that will predict ... -
Predictive intelligence in analytics aggregation of partial ordered subsets
(2020)Nowadays, the increased amount of users' devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of ... -
Robust Bayesian optimal sensor placement for model parameter estimation and response predictions
(2020)Optimal sensor placement (OSP) in complex systems implies a configuration that maximizes the information gain by the sensors. This configuration is identified by maximizing, with respect to the location of the sensors, an ...