Items where Author is "Zubair, M."

Article

Hussain, A. and Hussain, S.S. and Uddin, M.M. and Zubair, M. and Kumar, P. and Umair, M. (2022) An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification. Intelligent Systems Reference Library, 210. pp. 431-449.

Awan, A. and Baig, A. and Zubair, M. and Rahdar, A. and Nazar, M.F. and Farooqi, A.S. and Shalan, A.E. and Lanceros-Méndez, S. and Zafar, M.N. (2021) Green synthesis of molybdenum-based nanoparticles and their applications in energy conversion and storage: A review. International Journal of Hydrogen Energy.

Rizvi, S.S.H. and Zubair, M. and Ahmad, J. and Hashmani, M. and Khan, M.W. (2021) Wireless Communication as a Reshaping Tool for Internet of Things (IoT) and Internet of Underwater Things (IoUT) Business in Pakistan: A Technical and Financial Review. Wireless Personal Communications, 116 (2). pp. 1087-1105.

Sultana, S. and Hussain, S.S. and Hashmani, M. and Ahmad, J. and Zubair, M. (2021) A deep learning hybrid ensemble fusion for chest radiograph classification. Neural Network World, 31 (3). pp. 199-209.

Naqvi, S.R. and Jamshaid, S. and Naqvi, M. and Farooq, W. and Niazi, M.B.K. and Aman, Z. and Zubair, M. and Ali, M. and Shahbaz, M. and Inayat, A. and Afzal, W. (2018) Potential of biomass for bioenergy in Pakistan based on present case and future perspectives. Renewable and Sustainable Energy Reviews, 81. pp. 1247-1258.

Naqvi, S.R. and Jamshaid, S. and Naqvi, M. and Farooq, W. and Niazi, M.B.K. and Aman, Z. and Zubair, M. and Ali, M. and Shahbaz, M. and Inayat, A. and Afzal, W. (2018) Potential of biomass for bioenergy in Pakistan based on present case and future perspectives. Renewable and Sustainable Energy Reviews, 81. pp. 1247-1258.

Zubair, M. and Tang, T.B. (2014) A high resolution capacitive sensing system for the measurement of water content in crude oil. Sensors (Switzerland), 14 (7). pp. 11351-11361.

Conference or Workshop Item

Zubair, M. and Md Rais, H.B. and Al-Tashi, Q. (2023) U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images: A Novel Technique. In: UNSPECIFIED.

Zubair, M. and Md Rais, H.B. and Al-Tashi, Q. (2023) U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images: A Novel Technique. In: UNSPECIFIED.