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Muhammad Aamir
Muhammad Aamir
Huanggang Normal University
Verified email at hgnu.edu.cn
Title
Cited by
Cited by
Year
A deep learning approach for brain tumor classification using MRI images
M Aamir, Z Rahman, ZA Dayo, WA Abro, MI Uddin, I Khan, AS Imran, Z Ali, ...
Computers and Electrical Engineering 101, 108105, 2022
882022
Fractional-order fusion model for low-light image enhancement
Q Dai, YF Pu, Z Rahman, M Aamir
Symmetry 11 (4), 574, 2019
722019
A framework for efficient brain tumor classification using MRI images
Y Guan, M Aamir, Z Rahman, A Ali, WA Abro, ZA Dayo, MS Bhutta, Z Hu
AIMS Press, 2021
562021
A framework for automatic building detection from low-contrast satellite images
M Aamir, YF Pu, Z Rahman, M Tahir, H Naeem, Q Dai
Symmetry 11 (1), 3, 2018
482018
Spatiotemporal change of air-quality patterns in Hubei province—a pre-to post-COVID-19 analysis using path analysis and regression
M Aamir, Z Li, S Bazai, RA Wagan, UA Bhatti, MM Nizamani, S Akram
Atmosphere 12 (10), 1338, 2021
472021
An optimized architecture of image classification using convolutional neural network
M Aamir, Z Rahman, WA Abro, M Tahir, SM Ahmed
International Journal of Image, Graphics and Signal Processing 11 (10), 30, 2019
462019
A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter
Z Rahman, YF Pu, M Aamir, F Ullah
International Journal of Computers and Applications 41 (3), 207-217, 2019
432019
Multi-turn intent determination and slot filling with neural networks and regular expressions
WA Abro, G Qi, Z Ali, Y Feng, M Aamir
Knowledge-Based Systems 208, 106428, 2020
372020
Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease
Z Zhu, Z Rahman, M Aamir, SZA Shah, S Hamid, A Bilawal, S Li, M Ishfaq
RSC advances 13 (3), 2057-2069, 2023
302023
Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition
Z Rahman, YF Pu, M Aamir, S Wali
The Visual Computer 37 (5), 865-880, 2021
272021
A hybrid proposed framework for object detection and classification
M Aamir, YF Pu, Z Rahman, WA Abro, H Naeem, F Ullah, AM Badr
Journal of Information Processing Systems 14 (5), 1176-1194, 2018
252018
Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches
M Ishfaq, Z Rahman, M Aamir, I Ali, Y Guan, Z Hu
Molecular Diversity 27 (1), 371-387, 2023
242023
Machine learning-assisted prediction of the biological activity of aromatase inhibitors and data mining to explore similar compounds
M Ishfaq, M Aamir, F Ahmad, A M Mebed, S Elshahat
ACS omega 7 (51), 48139-48149, 2022
232022
Efficient image enhancement model for correcting uneven illumination images
Z Rahman, P Yi-Fei, M Aamir, S Wali, Y Guan
IEEE Access 8, 109038-109053, 2020
232020
Delay sensitive application partitioning and task scheduling in mobile edge cloud prototyping
A Lakhan, DK Sajnani, M Tahir, M Aamir, R Lodhi
2nd International Conference on 5G for Ubiquitous Connectivity: 5GU 2018, 59-80, 2020
232020
A Region-Based Efficient Network for Accurate Object Detection.
Y Guan, M Aamir, Z Hu, WA Abro, Z Rahman, ZA Dayo, S Akram
Traitement du Signal 38 (2), 2021
202021
A smart system for low-light image enhancement with color constancy and detail manipulation in complex light environments
Z Rahman, M Aamir, YF Pu, F Ullah, Q Dai
Symmetry 10 (12), 718, 2018
202018
Brain tumor classification utilizing deep features derived from high-quality regions in MRI images
M Aamir, Z Rahman, WA Abro, UA Bhatti, ZA Dayo, M Ishfaq
Biomedical Signal Processing and Control 85, 104988, 2023
192023
Predicting the environmental change of carbon emission patterns in South Asia: a deep learning approach using BiLSTM
M Aamir, MA Bhatti, SU Bazai, S Marjan, AM Mirza, A Wahid, A Hasnain, ...
Atmosphere 13 (12), 2011, 2022
182022
Joint intent detection and slot filling using weighted finite state transducer and BERT
WA Abro, G Qi, M Aamir, Z Ali
Applied Intelligence 52 (15), 17356-17370, 2022
162022
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