Zobrazeno 1 - 10
of 86
pro vyhledávání: '"N. A. Awang"'
Autor:
N. A. Hamiruddin, N. A. Awang, S. N. Mohd Shahpudin, N. S. Zaidi, M. A. M. Said, B. Chaplot, H. M. Azamathulla
Publikováno v:
Water Science and Technology, Vol 84, Iss 9, Pp 2113-2130 (2021)
Currently, research trends on aerobic granular sludge (AGS) have integrated the operating conditions of extracellular polymeric substances (EPS) towards the stability of AGS systems in various types of wastewater with different physical and biochemic
Externí odkaz:
https://doaj.org/article/51668712088d453d8fadae904db2768a
Autor:
Sabin, L., Guerrero, M. A., Ramos-Larios, G., Boumis, P., Zijlstra, A. A., Iskandar, D. N. F. Awang, Barlow, M. J., Toalá, J. A., Parker, Q. A., Corradi, R. M. L., Morris, R. A. H.
We present the first instalment of a deep imaging catalogue containing 58 True, Likely and Possible extended PNe detected with the Isaac Newton Telescope Photometric H$\alpha$ Survey (IPHAS). The three narrow-band filters in the emission lines of H$\
Externí odkaz:
http://arxiv.org/abs/2108.13612
Autor:
Iskandar, Dayang N. F. Awang, Zijlstra, Albert A., McDonald, Iain, Abdullah, Rosni, Fuller, Gary A., Fauzi, Ahmad H., Abdullah, Johari
Publikováno v:
Galaxies, 2020, 8, 88
This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep
Externí odkaz:
http://arxiv.org/abs/2101.12628
Publikováno v:
IEEE Access, Vol 7, Pp 9634-9644 (2019)
Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classifica
Externí odkaz:
https://doaj.org/article/f460f3a439b34dc3a7b4bb344b9277f8
Autor:
Muhammad Mohsin Butt, D. N. F. Awang Iskandar, Sherif E. Abdelhamid, Ghazanfar Latif, Runna Alghazo
Publikováno v:
Diagnostics, Vol 12, Iss 7, p 1607 (2022)
Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of
Externí odkaz:
https://doaj.org/article/bc174c3fd2324c229a448a25b02c688a
Autor:
Butt, Muhammad Mohsin1 (AUTHOR) engr.mohsin.butt@gmail.com, Iskandar, D. N. F. Awang1 (AUTHOR) dnfaiz@unimas.my, Abdelhamid, Sherif E.2 (AUTHOR) abdelhamidse@vmi.edu, Latif, Ghazanfar3,4 (AUTHOR) abdelhamidse@vmi.edu, Alghazo, Runna5 (AUTHOR) rghazo@pmu.edu.sa
Publikováno v:
Diagnostics (2075-4418). Jul2022, Vol. 12 Issue 7, pN.PAG-N.PAG. 17p.
Publikováno v:
Diagnostics, Vol 12, Iss 4, p 1018 (2022)
The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffe
Externí odkaz:
https://doaj.org/article/39228b1c7f2e4e6ea99fa108785c86e4
Autor:
Ghazanfar Latif, Abul Bashar, D. N. F. Awang Iskandar, Nazeeruddin Mohammad, Ghassen Ben Brahim, Jaafar M. Alghazo
Publikováno v:
Medical & Biological Engineering & Computing. 61:45-59
Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy t
Publikováno v:
Journal of Telecommunictions and Information Technology. 1:34-44
Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An
Autor:
Dayang N. F. Awang Iskandar, Albert A. Zijlstra, Iain McDonald, Rosni Abdullah, Gary A. Fuller, Ahmad H. Fauzi, Johari Abdullah
Publikováno v:
Galaxies, Vol 8, Iss 4, p 88 (2020)
This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep
Externí odkaz:
https://doaj.org/article/84bf6dfa906445bc9850ee26002b3d07