Högskolan i Skövde

his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Prediction of the number of weekly covid-19 infections: A comparison of machine learning methods
Högskolan i Skövde, Institutionen för informationsteknologi.
2022 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

The thesis two-folded problem aim was to identify and evaluate candidate Machine Learning (ML) methods and performance methods, for predicting the weekly number of covid-19 infections. The two-folded problem aim was created from studying public health studies where several challenges were identified. One challenge identified was the lack of using sophisticated and hybrid ML methods in the public health research area. In this thesis a comparison of ML methods for predicting the number of covid-19 weekly infections has been performed.

A dataset taken from the Public Health Agency in Sweden consisting of 101weeks divided into a 60 % training set and a 40% testing set was used in the evaluation.

Five candidate ML methods have been investigated in this thesis called Support Vector Regressor (SVR), Long Short Term Memory (LSTM), Gated Recurrent Network (GRU), Bidirectional-LSTM (BI-LSTM) and LSTM-Convolutional Neural Network (LSTM-CNN). These methods have been evaluated based on three performance measurements called Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R2. The evaluation of these candidate ML resulted in the LSTM-CNN model performing the best on RMSE, MAE and R2.

sted, utgiver, år, opplag, sider
2022. , s. 68
Emneord [en]
Machine learning, deep learning, covid-19, public health science, number of infection, regression, long short term memory, gated recurrent unit, support vector regressor, long short term memory-convolutional neural network, bidirectional-long short term memory
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-21302OAI: oai:DiVA.org:his-21302DiVA, id: diva2:1672236
Fag / kurs
Informationsteknologi
Utdanningsprogram
Data Science - Master’s Programme
Veileder
Examiner
Tilgjengelig fra: 2022-06-19 Laget: 2022-06-19 Sist oppdatert: 2022-06-19bibliografisk kontrollert

Open Access i DiVA

fulltext(1823 kB)146 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1823 kBChecksum SHA-512
ceec96679d6aeba149c6d105ddb5b709711b3b9f4e2a683bd54bc47311c3b75a337d70acd87f21fe80917adb4ff293c868f73857ade2e537f766b670d2aa3229
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 146 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 320 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf