Combining temporal disaggregation forecasts with artificial neural networks

Zaier, L. Hedhili

doi:10.1504/ijbidm.2014.068457

Cited by 2 publications

(1 citation statement)

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“…Finally, the rapidly increasing analytical and data processing capabilities may also open avenues for a more widespread use of dried blood spots (DBS) samples in an antidoping context (Cox and Eichner, 2017). For instance, WADA as the main regulator of antidoping policies, strategically supports advances in antidoping with methodology that uses big data, and artificial intelligence for pattern recognition (Zaier, 2014), or initiatives to use machine learning techniques to enhance detection of substances (Maass, 2019).…”

Section: Abstract: Doping Steroids Testing Urine Serum Hematological ...mentioning

confidence: 99%

Hurdling the Challenges of the 2019 IAAF World Championships

2020

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Section: Abstract: Doping Steroids Testing Urine Serum Hematological ...mentioning

confidence: 99%