Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Aznul Qalid Md Sabri"'
Publikováno v:
IEEE Access, Vol 12, Pp 47091-47109 (2024)
Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modali
Externí odkaz:
https://doaj.org/article/b5b38111d02f4052ac389d50e1bddef2
Autor:
Ayham Alomari, Ahmad Sami Al-Shamayleh, Norisma Idris, Aznul Qalid Md Sabri, Izzat Alsmadi, Danah Omary
Publikováno v:
IEEE Access, Vol 11, Pp 112483-112501 (2023)
Abstractive summarization is distinguished by using novel phrases that are not found in the source text. However, most previous research ignores this feature in favour of enhancing syntactical similarity with the reference. To improve novelty aspects
Externí odkaz:
https://doaj.org/article/d4304272523b463e8f6ffeb5bfdde4f8
Autor:
Zenab Elgamal, Aznul Qalid Md Sabri, Mohammad Tubishat, Dina Tbaishat, Sharif Naser Makhadmeh, Osama Ahmad Alomari
Publikováno v:
IEEE Access, Vol 10, Pp 51428-51446 (2022)
The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redunda
Externí odkaz:
https://doaj.org/article/4a219f7cfb8244f799af5a3a5a00aec0
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 3, Pp 2713-2745 (2021)
Abstract Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by m
Externí odkaz:
https://doaj.org/article/cf15518559944e6ea29318e3943211ff
Publikováno v:
IEEE Access, Vol 9, Pp 34264-34275 (2021)
Autism spectrum disorder is a very common disorder. An early diagnosis of autism is essential for the prognosis of this disorder. The common diagnosis method utilizes behavioural cues of autistic children. Doctors require years of clinical training t
Externí odkaz:
https://doaj.org/article/c724b116cdcd4b76b3c030cfbb331958
Autor:
Mwenge Mulenga, Sameem Abdul Kareem, Aznul Qalid Md Sabri, Manjeevan Seera, Suresh Govind, Chandramathi Samudi, Saharuddin Bin Mohamad
Publikováno v:
IEEE Access, Vol 9, Pp 23565-23578 (2021)
Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in
Externí odkaz:
https://doaj.org/article/3fbdbb65ee514f438cb6b7c7ab39fb61
Publikováno v:
IEEE Access, Vol 9, Pp 97296-97319 (2021)
Machine learning (ML)-based detection of diseases using sequence-based gut microbiome data has been of great interest within the artificial intelligence in medicine (AIM) community. The approach offers a non-invasive alternative for colorectal cancer
Externí odkaz:
https://doaj.org/article/8a2a815c6dee4ac2b0dd1a04a87becde
Autor:
Mwenge Mulenga, Sameem Abdul Kareem, Aznul Qalid Md Sabri, Manjeevan Seera, Suresh Govind, Chandramathi Samudi, Saharuddin Bin Mohamad
Publikováno v:
IEEE Access, Vol 11, Pp 44636-44636 (2023)
In the above article [1], the acknowledgment for the research grant was referencing an incorrect grant reference number.
Externí odkaz:
https://doaj.org/article/dea451cead304e2ebfcf17696d571d74
Publikováno v:
IEEE Access, Vol 11, Pp 44649-44649 (2023)
In the above article [1], the acknowledgment for the research grant was referencing an incorrect grant reference number.
Externí odkaz:
https://doaj.org/article/48d6ee7967cb4f078f9819904510a992
Autor:
Zenab Mohamed Elgamal, Norizan Mohd Yasin, Aznul Qalid Md Sabri, Rami Sihwail, Mohammad Tubishat, Hazim Jarrah
Publikováno v:
Computation, Vol 9, Iss 6, p 68 (2021)
The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features an
Externí odkaz:
https://doaj.org/article/dc7d1def9676422083addc7cf1e1fea6