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pro vyhledávání: '"Khalooei A"'
Autor:
Anvari, Mohammad Akhavan, Kashefi, Rojina, Khazaie, Vahid Reza, Khalooei, Mohammad, Sabokrou, Mohammad
Anomaly detection involves identifying instances within a dataset that deviate from the norm and occur infrequently. Current benchmarks tend to favor methods biased towards low diversity in normal data, which does not align with real-world scenarios.
Externí odkaz:
http://arxiv.org/abs/2406.10617
Autor:
Morteza Khalooei, Masoomeh Torabideh, Ahmad Rajabizadeh, Sedigheh Zeinali, Hossein Abdipour, Awais Ahmad, Gholamreza Parsaseresht
Publikováno v:
Results in Chemistry, Vol 11, Iss , Pp 101811- (2024)
In this work, a laboratory examination of the effectiveness of the metal–organic framework ZIF-67 for the elimination of arsenate from aqueous media was evaluated. The properties of the adsorbent were specified by X-ray diffraction (XRD), field emi
Externí odkaz:
https://doaj.org/article/e957b7d444874a3592e4a30070d73e80
Publikováno v:
In Knowledge-Based Systems 25 November 2024 304
Autor:
Khalooei, Morteza, Torabideh, Masoomeh, Rajabizadeh, Ahmad, Zeinali, Sedigheh, Abdipour, Hossein, Ahmad, Awais, Parsaseresht, Gholamreza
Publikováno v:
In Results in Chemistry October 2024 11
Deep neural network models are used today in various applications of artificial intelligence, the strengthening of which, in the face of adversarial attacks is of particular importance. An appropriate solution to adversarial attacks is adversarial tr
Externí odkaz:
http://arxiv.org/abs/2202.02626
Autor:
Ali Khalooei, Mohsen Rahimi
Publikováno v:
Energy Science & Engineering, Vol 11, Iss 10, Pp 3536-3558 (2023)
Abstract This paper deals with the stability analysis and enhancement in the AC‐microgrid system comprising parallel‐operated voltage‐source inverters (VSIs) supplying an active‐load. The active‐load behaves such as a constant power load fr
Externí odkaz:
https://doaj.org/article/eaa07874fab64b2f84f6628531e70aaa
Akademický článek
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Publikováno v:
In Applied Soft Computing November 2023 147
Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting a
Externí odkaz:
http://arxiv.org/abs/1909.07801
In this paper, we propose a novel self-supervised representation learning by taking advantage of a neighborhood-relational encoding (NRE) among the training data. Conventional unsupervised learning methods only focused on training deep networks to un
Externí odkaz:
http://arxiv.org/abs/1908.10455