Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Adekanmi Adeyinka Adegun"'
Publikováno v:
Smart Cities, Vol 7, Iss 4, Pp 2182-2207 (2024)
Object detection in remotely sensed (RS) satellite imagery has gained significance in smart city concepts, which include urban planning, disaster management, and environmental monitoring. Deep learning techniques have shown promising outcomes in obje
Externí odkaz:
https://doaj.org/article/4d381b85d42e40d8a0099348e6851c89
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-24 (2023)
Abstract Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics of the imagery. These images are characterized by features such as spectral signature
Externí odkaz:
https://doaj.org/article/1c93cc93ed7049b0b14c8b122b4e33c9
Publikováno v:
Sensors, Vol 23, Iss 13, p 5849 (2023)
Introduction: Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service deliver
Externí odkaz:
https://doaj.org/article/f765facf69d345f4ad5d35c192509298
Publikováno v:
Applied Sciences, Vol 11, Iss 7, p 3025 (2021)
The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as h
Externí odkaz:
https://doaj.org/article/101928a315e14cd48ff44c90abbfdb5e
Publikováno v:
Sensors; Volume 23; Issue 13; Pages: 5849
Introduction: Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service deliver
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::03850073cc8679289ce59eaad192a36b
Autor:
Aderonke Anthonia, Kayode, Noah Oluwatobi, Akande, Adekanmi Adeyinka, Adegun, Marion Olubunmi, Adebiyi
Publikováno v:
Medical Devices (Auckland, N.Z.)
Aderonke Anthonia Kayode,1 Noah Oluwatobi Akande,1 Adekanmi Adeyinka Adegun,1 Marion Olubunmi Adebiyi21Computer Science Department, Landmark University, Omu-Aran, Kwara State, Nigeria; 2Department of Computer and Information Sciences, Covenant Univer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::1dcc022d6c49979dd5c827e7b4a36c7f
https://www.dovepress.com/an-automated-mammogram-classification-system-using-modified-support-ve-peer-reviewed-article-MDER
https://www.dovepress.com/an-automated-mammogram-classification-system-using-modified-support-ve-peer-reviewed-article-MDER