Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Alhassan Mabrouk"'
Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search
Autor:
Abdelghani Dahou, Ahmad O. Aseeri, Alhassan Mabrouk, Rehab Ali Ibrahim, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz
Publikováno v:
Diagnostics, Vol 13, Iss 9, p 1579 (2023)
Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substanti
Externí odkaz:
https://doaj.org/article/6f49110510aa4039936f46f85bc973be
Publikováno v:
Diagnostics, Vol 13, Iss 5, p 834 (2023)
As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to impr
Externí odkaz:
https://doaj.org/article/94bb1a8f0008464ea71612458758c403
Publikováno v:
IEEE Access, Vol 8, Pp 85616-85638 (2020)
Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. T
Externí odkaz:
https://doaj.org/article/c885d02c8a124b14b9460e43c5acc2b2
Autor:
Mohamed Abd Elaziz, Abdelghani Dahou, Shaker El-Sappagh, Alhassan Mabrouk, Mohamed Medhat Gaber
Publikováno v:
Applied Sciences, Vol 12, Iss 19, p 9710 (2022)
This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature sel
Externí odkaz:
https://doaj.org/article/461ae352b0ae4a92ac8ecd2dc25645dc
Autor:
Alhassan Mabrouk, Rebeca P. Díaz Redondo, Abdelghani Dahou, Mohamed Abd Elaziz, Mohammed Kayed
Publikováno v:
Applied Sciences, Vol 12, Iss 13, p 6448 (2022)
Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods
Externí odkaz:
https://doaj.org/article/a79a59d7bb7b422698b8cfcdeae9f4ec
Autor:
Hadeer Adel, Abdelghani Dahou, Alhassan Mabrouk, Mohamed Abd Elaziz, Mohammed Kayed, Ibrahim Mahmoud El-Henawy, Samah Alshathri, Abdelmgeid Amin Ali
Publikováno v:
Mathematics, Vol 10, Iss 3, p 447 (2022)
This paper presents an alternative event detection model based on the integration between the DistilBERT and a new meta-heuristic technique named the Hunger Games Search (HGS). The DistilBERT aims to extract features from the text dataset, while a bi
Externí odkaz:
https://doaj.org/article/8b839f31a86348caa820ba9dede9bc91
Publikováno v:
Sensors, Vol 21, Iss 2, p 636 (2021)
Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research
Externí odkaz:
https://doaj.org/article/0e8e9fcacf3b44e19ba16da172b5c370
Autor:
Alhassan Mabrouk, Abdelghani Dahou, Mohamed Abd Elaziz, Rebeca P. Díaz Redondo, Mohammed Kayed
Publikováno v:
Computational Intelligence and Neuroscience. 2022:1-22
The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being
Publikováno v:
Technological Forecasting and Social Change. 192:122546
Publikováno v:
IEEE Access, Vol 8, Pp 85616-85638 (2020)
Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. T