Zobrazeno 1 - 10
of 225
pro vyhledávání: '"Khishe, Mohammad"'
Autor:
Sun, Chenyang, Khishe, Mohammad
Publikováno v:
Engineering Computations, 2024, Vol. 41, Issue 8/9, pp. 2134-2168.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/EC-05-2024-0415
This chapter proposes using the Moth Flame Optimization (MFO) algorithm for finetuning a Deep Neural Network to recognize different underwater sonar datasets. Same as other models evolved by metaheuristic algorithms, premature convergence, trapping i
Externí odkaz:
http://arxiv.org/abs/2303.00922
Autor:
Fan, Xiaoqian, Khishe, Mohammad, Alqahtani, Abdullah, Alsubai, Shtwai, Alanazi, Abed, Mohamed Zaidi, Monji
Publikováno v:
In Advanced Engineering Informatics October 2024 62 Part B
Autor:
Taiwo, Blessing Olamide, Fissha, Yewuhalashet, Hosseini, Shahab, Khishe, Mohammad, Kahraman, Esma, Adebayo, Babatunde, Sazid, Mohammed, Adesida, Patrick Adeniyi, Famobuwa, Oluwaseun Victor, Faluyi, Joshua Oluwaseyi, Akinlabi, Adams Abiodun
Publikováno v:
In Green and Smart Mining Engineering September 2024 1(3):346-361
Autor:
Maaroof, Bestan B., Rashid, Tarik A., Abdulla, Jaza M., Hassan, Bryar A., Alsadoon, Abeer, Mohammadi, Mokhtar, Khishe, Mohammad, Mirjalili, Seyedali
Publikováno v:
Arch Computat Methods Eng, 2022
Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization
Externí odkaz:
http://arxiv.org/abs/2202.03477
Autor:
Rahman, Khaista1 (AUTHOR), Khishe, Mohammad2,3,4 (AUTHOR) m_khishe@alumni.iust.ac.ir
Publikováno v:
Scientific Reports. 7/2/2024, Vol. 14 Issue 1, p1-23. 23p.
Autor:
Olamide Taiwo, Blessing, Gebretsadik, Angesom, Abbas, Hawraa H., Khishe, Mohammad, Fissha, Yewuhalashet, Kahraman, Esma, Rabbani, Ahsan, Abiodun Akinlabi, Adams
Publikováno v:
In Heliyon 30 June 2024 10(12)
Autor:
Tianqing, Hu, Khishe, Mohammad, Mohammadi, Mokhtar, Parvizi, Gholam-Reza, Karim, Sarkhel H. Taher, Rashid, Tarik A.
Publikováno v:
Biomedical Signal Processing and Control, 2021
Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) m
Externí odkaz:
http://arxiv.org/abs/2106.01435
The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is perhaps the fastest way to diagnose the
Externí odkaz:
http://arxiv.org/abs/2105.14192
Publikováno v:
In Heliyon 15 April 2024 10(7)