Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Manar Abu Talib"'
Autor:
Manar Abu Talib, Mohammad Adel Moufti, Qassim Nasir, Yousuf Kabbani, Dana Aljaghber, Yaman Afadar
Publikováno v:
International Dental Journal, Vol 74, Iss 6, Pp 1471-1482 (2024)
Background: During preclinical training, dental students take radiographs of acrylic (plastic) blocks containing extracted patient teeth. With the digitisation of medical records, a central archiving system was created to store and retrieve all x-ray
Externí odkaz:
https://doaj.org/article/dd11da85571745b4bab022bdc11b57bf
Autor:
Qassim Nasir, Manar Abu Talib, Muhammad Arbab Arshad, Tracy Ishak, Romaissa Berrim, Basma Alsaid, Youssef Badway, Omnia Abu Waraga
Publikováno v:
Energy Informatics, Vol 7, Iss 1, Pp 1-26 (2024)
Abstract False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minima
Externí odkaz:
https://doaj.org/article/f5b40a8d40f94d06886bdbeec24e440a
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-19 (2024)
Abstract With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 ca
Externí odkaz:
https://doaj.org/article/3129b1150588493889cdf5860c881caf
Publikováno v:
Frontiers in Education, Vol 9 (2024)
Artificial intelligence integration, specifically ChatGPT, is becoming increasingly popular in educational contexts. This research paper provides a systematic literature review that examines the effects of incorporating ChatGPT into education. The st
Externí odkaz:
https://doaj.org/article/ccd047b6a28940a6aea886951acf902d
Autor:
Meriem Aoudia, Mustafa B. M. Alaraj, Omnia Abu Waraga, Takua Mokhamed, Manar Abu Talib, Maamar Bettayeb, Qassim Nasir, Chaouki Ghenai
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
With the rise of the 3Ds—decarbonization, decentralization, and digitalization—the number of electric vehicles is projected to increase, necessitating the implementation of modern technologies to avoid unnecessary energy wastage. Numerous studies
Externí odkaz:
https://doaj.org/article/4b9edc3cf14348a393087de3dc4520d3
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 3, Pp 102015- (2024)
Artificial Intelligence (AI) approaches have been increasingly used in financial markets as technology advances. In this research paper, we conduct a Systematic Literature Review (SLR) that studies financial trading approaches through AI techniques.
Externí odkaz:
https://doaj.org/article/b5f524b78c824b10a2078417850d7f7f
Autor:
Ali Bou Nassif, Manar Abu Talib, Mohammad Azzeh, Shaikha Alzaabi, Rawan Khanfar, Ruba Kharsa, Lefteris Angelis
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract Software defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modu
Externí odkaz:
https://doaj.org/article/f40d5f1c5a8e4e7db0e2d2a2220c2d36
Publikováno v:
Data in Brief, Vol 51, Iss , Pp 109718- (2023)
Worldwide, electricity production exceeds its consumption which leads to wasted financial and energy resources. Machine learning models can be utilized to predict the future consumption to avoid these significant losses. This paper presents the data
Externí odkaz:
https://doaj.org/article/13b8129aab484160af532b9224b4b366
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2569 (2024)
Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach
Externí odkaz:
https://doaj.org/article/af452eabaac44f7aab0582aace5997b4
Publikováno v:
e-Informatica Software Engineering Journal, Vol 18, Iss 1 (2023)
Background: Software Defect Prediction (SDP) is a vital step in software development. SDP aims to identify the most likely defect-prone modules before starting the testing phase, and it helps assign resources and reduces the cost of testing. Aim: Alt
Externí odkaz:
https://doaj.org/article/34030c3b45dc4d3badc0269693ce483c