Data-driven computing in dynamics
نویسندگان
چکیده
منابع مشابه
Enhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining
This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...
متن کاملLocative media and data-driven computing experiments
Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insigh...
متن کاملData Integrity Checking Protocol with Data Dynamics in Cloud Computing
We introduce a model for provable data possession (PDP) which allows a client that has stored data at an un-trusted server to verify that the server possesses the original data without retrieving it. In a previous work, Ateniese et al. proposed a remote data integrity checking protocol that supports data partial dynamics. In this paper, we present a new remote data possession checking protocol ...
متن کاملData Replication-Based Scheduling in Cloud Computing Environment
Abstract— High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications. Sometimes accessing data becomes...
متن کاملData driven discovery of nonlinear dynamics
We demonstrate that sparse regression and compressive sensing techniques are capable of accurately determining a set of functions governing a nonlinear dynamical system. We analyze a technique introduced by Brunton, Proctor, and Kutz, 2016 [1] that builds a sparse representation of a dynamical system by computing sequential least squares fittings of the data to identify the governing equations....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal for Numerical Methods in Engineering
سال: 2017
ISSN: 0029-5981
DOI: 10.1002/nme.5716