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Ontology type: schema:Periodical      Open Access: True


Journal Info

START YEAR

2016

PUBLISHER

BioMed Central

LANGUAGE

en

HOMEPAGE

http://bdataanalytics.biomedcentral.com/

Recent publications latest 20 shown

  • 2019-12 Evaluating associative classification algorithms for Big Data
  • 2019-12 Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments
  • 2018-12 Bio-inspired optimization algorithms applied to rectenna design
  • 2018-12 A review on multi-task metric learning
  • 2018-12 A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting
  • 2018-12 foo.castr: visualising the future AI workforce
  • 2018-12 A hybrid model for short term real-time electricity price forecasting in smart grid
  • 2018-12 Customized biomedical informatics
  • 2018-12 Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data
  • 2018-12 Depth image-based plane detection
  • 2018-12 Nonconvex matrix completion with Nesterov’s acceleration
  • 2018-12 Chinese text-line detection from web videos with fully convolutional networks
  • 2018-12 Identification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix)
  • 2017-12 Work ability assessment among acutely admitted patients using biomarkers
  • 2017-12 Latent feature models for large-scale link prediction
  • 2017-12 Building a Chinese discourse topic corpus with a micro-topic scheme based on theme-rheme theory
  • 2017-12 PorthoMCL: Parallel orthology prediction using MCL for the realm of massive genome availability
  • 2017-12 Expressive modeling for trusted big data analytics: techniques and applications in sentiment analysis
  • 2017-12 A comparison on scalability for batch big data processing on Apache Spark and Apache Flink
  • 2017-12 Two dimensional smoothing via an optimised Whittaker smoother
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

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    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/journal.1158761'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/journal.1158761'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/journal.1158761'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/journal.1158761'


     

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