Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Terutoshi Tada"'
Autor:
Zaman, Halimah Badioze, Smeaton, Alan F., Shih, Timothy K., Velastin, Sergio, Terutoshi, Tada, Jørgensen, Bo Nørregaard, Aris, Hazleen, Ibrahim, Nazrita
Publikováno v:
Zaman, H B, Smeaton, A F, Shih, T K, Velastin, S, Terutoshi, T, Jørgensen, B N, Aris, H & Ibrahim, N 2021, Preface . in H B Zaman, A F Smeaton, T K Shih, S Velastin, T Terutoshi, B N Jørgensen, H Aris & N Ibrahim (eds), Advances in Visual Informatics : 7th International Visual Informatics Conference, IVIC 2021, Kajang, Malaysia, November 23–25, 2021, Proceedings . Springer, Lecture Notes in Computer Science, vol. 13051, pp. v-vi, 7th International Conference on Advances in Visual Informatics, IVIC 2021, Kajang, Malaysia, 23/11/2021 .
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3062::0291ee21be350fc9605a36f1478f8d1a
https://portal.findresearcher.sdu.dk/da/publications/78d3210a-24f8-4098-99cb-c20e2bf0cf1f
https://portal.findresearcher.sdu.dk/da/publications/78d3210a-24f8-4098-99cb-c20e2bf0cf1f
Publikováno v:
AINA Workshops
It is difficult for human beings to check the garbage accumulated at the bottom of the ocean, so research on the accumulation of garbage and its influence on the living things is difficult to advance. The garbage thrown by humans into the sea affects
Publikováno v:
Salleh, N S M, Suliman, A & Jørgensen, B N 2021, Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer . in H Badioze Zaman, A F Smeaton, T K Shih, S Velastin, T Terutoshi, B N Jørgensen, H Aris & N Ibrahim (eds), Advances in Visual Informatics : 7th International Visual Informatics Conference, IVIC 2021, Kajang, Malaysia, November 23–25, 2021, Proceedings . Springer, Lecture Notes in Computer Science, vol. 13051, pp. 600-609, 7th International Conference on Advances in Visual Informatics, IVIC 2021, Kajang, Malaysia, 23/11/2021 . https://doi.org/10.1007/978-3-030-90235-3_52
Machine learning can perform electricity load prediction on the demand side. This paper compared the electricity prediction errors between two machine learning algorithms: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) architecture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3062::9b96af477fd39979b7e8e03ca0e20b93
https://portal.findresearcher.sdu.dk/da/publications/8e5a9ae3-1584-4628-835b-459e21443057
https://portal.findresearcher.sdu.dk/da/publications/8e5a9ae3-1584-4628-835b-459e21443057