Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Muhammad Armghan Latif"'
Autor:
Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, Salim Nasar Faraj Mursal, Saifur Rahman, Muawia Abdelkafi Magzoub, Muhammad Armghan Latif, Faisal Althobiani, Imran Khan, Ghulam Abbas
Publikováno v:
IEEE Access, Vol 11, Pp 127783-127799 (2023)
Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to the effect
Externí odkaz:
https://doaj.org/article/003b5aea1d3f49ea80d37120ca55dc1b
Autor:
Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, Faisal Althobiani, Salim Nasar Faraj Mursal, Saifur Rahman, Muawia Abdelkafi Magzoub, Muhammad Armghan Latif, Imran Khan Yousufzai
Publikováno v:
IEEE Access, Vol 11, Pp 118253-118267 (2023)
Bearing faults are critical in machinery; their early detection is vital to prevent costly breakdowns and ensure operational safety. This study presents a pioneering take on addressing the challenges of imbalanced datasets in bearing fault diagnosis.
Externí odkaz:
https://doaj.org/article/6cf513e0690943738edcb69b2e72d552
Autor:
Muhammad Armghan Latif, Noor Afshan, Zohaib Mushtaq, Nabeel Ahmed Khan, Muhammad Irfan, Grzegorz Nowakowski, Samar M. Alqhtani, Salim Nasar Faraj Mursal, Sergii Telenyk
Publikováno v:
IEEE Access, Vol 11, Pp 100887-100906 (2023)
Significant yield challenges are posed by biotic stress on coffee leaves, which has a negative effect on the revenue generation of this highly utilized commodity. Numerous studies have proposed techniques for the early detection and classification of
Externí odkaz:
https://doaj.org/article/2c30fd16221947a49c94fdc96e2e22a0