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Number of items: 7.

Article

Khalid Adam, Ismail Hammad and Izzeldin, I. Mohd and Ibrahim, Younis (2021) Analyzing the resilience of convolutional neural networks implemented on GPUs: Alexnet as a case study. International Journal of Electrical and Computer Engineering Systems, 12 (2). pp. 91-103. ISSN 1847-6996. (Published)

Khalid Adam, Ismail Hammad and Mohamed, Izzeldin I. and Ahmed, Nidal A. and Younis, Younis M. and Elfadil, Nazar (2020) Big Data Technique for the Weather Prediction using Hadoop MapReduce. International Journal of Modern Trends in Engineering and Research (IJMTER), 7 (5). pp. 1-6. ISSN 2349–9745. (Published)

Khalid Adam, Ismail Hammad and Noor Akma, Abu Bakar and Fakhreldin, Mohammed Adam Ibrahim and Mazlina, Abdul Majid (2018) Big Data and Learning Analytics: A Big Potential to Improve E-Learning. Advanced Science Letters, 24 (10). pp. 7838-7843. ISSN 1936-6612. (Published)

Conference or Workshop Item

Khalid Adam, Ismail Hammad and Jasni, Mohamad Zain and Mazlina, Abdul Majid and Noor Akma, Abu Bakar (2016) Big Data Prediction Framework for Weather Temperature Based on MapReduce Algorithm. In: Conference on Open Systems (ICOS) , 10-12 October 2016 , Langkawi. pp. 13-17.. ISSN 24733660 ISBN 9781509026036 (Published)

Khalid Adam, Ismail Hammad and Fakhreldin, Mohammed Adam Ibrahim and Jasni, Mohamad Zain and Mazlina, Abdul Majid (2015) Big Data Analysis and Storage. In: Proceedings of the 2015 International Conference on Operations Excellence and Service Engineering , 10-11 September 2015 , Orlando, Florida, USA. pp. 648-659.. (Published)

Thesis

Khalid Adam, Ismail Hammad (2021) Development of low-overhead soft error mitigation technique for safety critical neural networks applications. PhD thesis, Universiti Malaysia Pahang (Contributors, UNSPECIFIED: UNSPECIFIED).

Khalid Adam, Ismail Hammad (2017) Mapreduce algorithm for weather dataset. Masters thesis, Universiti Malaysia Pahang (Contributors, Thesis advisor: Mazlina, Abdul Majid).

This list was generated on Sun Nov 24 17:01:52 2024 +08.