Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Helmi Z. M. Shafri"'
Autor:
Mohammed Ahmed AL-Kubaisi, Helmi Z. M. Shafri, Mohd Hasmadi Ismail, Mohd Johari Mohd Yusof, Shaiful Jahari bin Hashim
Publikováno v:
Geocarto International, Vol 38, Iss 1 (2023)
In this research, a deep learning approach for hyperspectral image (HSI) classification was developed, incorporating attention mechanisms, multiscale feature learning, and utilization of unsampled pixels. The proposed model, multiscale attention-base
Externí odkaz:
https://doaj.org/article/4d8819b433b240708c0e97ecd46a05a0
Publikováno v:
Geospatial Health, Vol 18, Iss 1 (2023)
This research proposes a ‘temporal attention’ addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang
Externí odkaz:
https://doaj.org/article/f0cf9d235c8e4f6e989b7faaa09d6a18
Publikováno v:
Earth, Vol 3, Iss 2, Pp 699-732 (2022)
Globally, urbanisation has been the most significant factor causing land use and land cover changes due to accelerated population growth and limited governmental regulation. Urban communities worldwide, particularly in Iraq, are on the frontline for
Externí odkaz:
https://doaj.org/article/0d12ab60ee214cd0b0e10c29a2f3ba77
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 12, Iss 2, p 76 (2023)
Land use and land cover changes driven by urban sprawl has accelerated the degradation of ecosystem services in metropolitan settlements. However, most optimisation techniques do not consider the dynamic effect of urban sprawl on the spatial criteria
Externí odkaz:
https://doaj.org/article/2de1e71ed3cd47debd3cf11cfff4c7b6
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 11, Iss 12, p 606 (2022)
Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the comp
Externí odkaz:
https://doaj.org/article/54c3bcd23cdc4953a56ac0ea44e093fe
Publikováno v:
Applied Sciences, Vol 12, Iss 21, p 10890 (2022)
During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other
Externí odkaz:
https://doaj.org/article/c9542263e3844a159036c0d1f103cf21
Publikováno v:
Land, Vol 11, Iss 11, p 1905 (2022)
In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped
Externí odkaz:
https://doaj.org/article/587a6eb63dd543b892ef14458ae68c39
Autor:
Mohamed Barakat A. Gibril, Bahareh Kalantar, Rami Al-Ruzouq, Naonori Ueda, Vahideh Saeidi, Abdallah Shanableh, Shattri Mansor, Helmi Z. M. Shafri
Publikováno v:
Remote Sensing, Vol 12, Iss 7, p 1081 (2020)
Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In thi
Externí odkaz:
https://doaj.org/article/3ba6b3ad295d4ea7af973bb9b8eea6d3
Autor:
Omer Saud Azeez, Biswajeet Pradhan, Helmi Z. M. Shafri, Nagesh Shukla, Chang-Wook Lee, Hossein Mojaddadi Rizeei
Publikováno v:
Applied Sciences, Vol 9, Iss 2, p 313 (2019)
Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon
Externí odkaz:
https://doaj.org/article/60f3b7e092cb4d02b3f14a63ac738163
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b393f2fba20a641d653bc64342807fc
https://doi.org/10.1109/igarss46834.2022.9884428
https://doi.org/10.1109/igarss46834.2022.9884428