Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Abdullah Sheneamer"'
Autor:
Kishor Kumar Reddy C., Vijaya Sindhoori Kaza, Madana Mohana R., Abdulrahman Alamer, Shadab Alam, Mohammed Shuaib, Sultan Basudan, Abdullah Sheneamer
Publikováno v:
PeerJ Computer Science, Vol 10, p e2491 (2024)
This research addresses the critical issue of cryptojacking attacks, a significant cybersecurity threat where malicious actors covertly exploit computational resources for unauthorized cryptocurrency mining, particularly in wireless sensor networks (
Externí odkaz:
https://doaj.org/article/f6ff16dc01894a33921d4588b5f9e6c2
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
IntroductionMachine learning (ML) algorithms and statistical modeling offer a potential solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological,
Externí odkaz:
https://doaj.org/article/f0dcebe9e61442688880102828f3c412
Autor:
C. Kishor Kumar Reddy, Pulakurthi Anaghaa Reddy, Himaja Janapati, Basem Assiri, Mohammed Shuaib, Shadab Alam, Abdullah Sheneamer
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors.
Externí odkaz:
https://doaj.org/article/ae300abf4999408d8e56ebfbad4b7c51
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 7862 (2024)
The automatic detection of fires and the determination of their causes play a crucial role in mitigating the catastrophic consequences of such events. The literature reveals substantial research on automatic fire detection using machine learning mode
Externí odkaz:
https://doaj.org/article/ab61f985872d4051937b130a19dc9ee0
Autor:
Abdullah Sheneamer
Publikováno v:
PeerJ Computer Science, Vol 10, p e1838 (2024)
System security for web-based applications is paramount, and for the avoidance of possible cyberattacks it is important to detect vulnerable JavaScript functions. Developers and security analysts have long relied upon static analysis to investigate v
Externí odkaz:
https://doaj.org/article/79d988f5828444de9300ba102e28bb1b
Autor:
Abdullah Sheneamer
Publikováno v:
PLoS ONE, Vol 19, Iss 11, p e0313607 (2024)
The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are exampl
Externí odkaz:
https://doaj.org/article/bc7912605ede4e66894dd816c38aa5b8
Autor:
Essa Alhazmi, Abdullah Sheneamer
Publikováno v:
IEEE Access, Vol 11, Pp 27579-27589 (2023)
Students learning performance is one of the core components for assessing any educational systems. Students performance is very crucial in tackling issues of learning process and one of the important matters to measure learning outcomes. The ability
Externí odkaz:
https://doaj.org/article/474655e0e79740c0a342a7903a999ae5
Publikováno v:
IEEE Access, Vol 9, Pp 84828-84844 (2021)
Code clones. In this work, we propose a novel detection framework using machine learning for automated detection of all four type of clones. The features extracted from a pair of code blocks are combined for possible detection of a clone with respect
Externí odkaz:
https://doaj.org/article/70d870d2906f43a2855705e56e2f181a
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
IEEE Access, Vol 9, Pp 84828-84844 (2021)
Code clones. In this work, we propose a novel detection framework using machine learning for automated detection of all four type of clones. The features extracted from a pair of code blocks are combined for possible detection of a clone with respect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd96630d33a94fc193611b14915180de
https://doi.org/10.1109/access.2021.3079156
https://doi.org/10.1109/access.2021.3079156