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Ashad Kabir, Ph.D
Ashad Kabir, Ph.D
Deputy Leader of Data Mining Research Group and Senior Lecturer, Charles Sturt University, Australia
Verified email at csu.edu.au - Homepage
Title
Cited by
Cited by
Year
Depression detection from social network data using machine learning techniques
M Islam, MA Kabir, A Ahmed, ARM Kamal, H Wang, A Ulhaq
Health information science and systems 6 (1), 1-12, 2018
2152018
Blockchain versus database: a critical analysis
MJM Chowdhury, A Colman, MA Kabir, J Han, P Sarda
2018 17th IEEE International conference on trust, security and privacy in …, 2018
1282018
PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images
NK Chowdhury, M Rahman, MA Kabir
Health information science and systems 8 (1), 1-14, 2020
742020
Individualized time-series segmentation for mining mobile phone user behavior
IH Sarker, A Colman, MA Kabir, J Han
The Computer Journal 61 (3), 349-368, 2018
742018
Blockchain as a notarization service for data sharing with personal data store
MJM Chowdhury, A Colman, MA Kabir, J Han, P Sarda
2018 17th ieee international conference on trust, security and privacy in …, 2018
672018
Hdskg: Harvesting domain specific knowledge graph from content of webpages
X Zhao, Z Xing, MA Kabir, N Sawada, J Li, SW Lin
2017 ieee 24th international conference on software analysis, evolution and …, 2017
582017
User-centric social context information management: an ontology-based approach and platform
MA Kabir, J Han, J Yu, A Colman
Personal and Ubiquitous Computing 18 (5), 1061-1083, 2014
482014
Investigating smart home security: Is blockchain the answer?
S Arif, MA Khan, SU Rehman, MA Kabir, M Imran
IEEE Access 8, 117802-117816, 2020
422020
Blockchain for fraud prevention: a work-history fraud prevention system
P Sarda, MJM Chowdhury, A Colman, MA Kabir, J Han
2018 17th IEEE international conference on trust, security and privacy in …, 2018
342018
Phone call log as a context source to modeling individual user behavior
IH Sarker, A Colman, MA Kabir, J Han
Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016
332016
ECOVNet: An ensemble of deep convolutional neural networks based on efficientnet to detect COVID-19 from chest X-rays
NK Chowdhury, MA Kabir, M Rahman, N Rezoana
arXiv preprint arXiv:2009.11850, 2020
322020
Differences in health news from reliable and unreliable media
S Dhoju, M Main Uddin Rony, M Ashad Kabir, N Hassan
Companion Proceedings of The 2019 World Wide Web Conference, 981-987, 2019
322019
SCIMS: a social context information management system for socially-aware applications
MA Kabir, J Han, J Yu, A Colman
International Conference on Advanced Information Systems Engineering, 301-317, 2012
312012
A novel quick seizure detection and localization through brain data mining on ECoG dataset
MK Siddiqui, MZ Islam, MA Kabir
Neural Computing and Applications 31 (9), 5595-5608, 2019
302019
An improved naive bayes classifier-based noise detection technique for classifying user phone call behavior
IH Sarker, MA Kabir, A Colman, J Han
Australasian Conference on Data Mining, 72-85, 2017
232017
Modeling and coordinating social interactions in pervasive environments
MA Kabir, J Han, A Colman
2011 16th IEEE International Conference on Engineering of Complex Computer …, 2011
202011
Detecting autism spectrum disorder using machine learning techniques
MD Hossain, MA Kabir, A Anwar, MZ Islam
Health Information Science and Systems 9 (1), 1-13, 2021
182021
Leveraging official content and social context to recommend software documentation
J Li, Z Xing, MA Kabir
IEEE Transactions on Services Computing 14 (2), 472-486, 2018
172018
Behavior-oriented time segmentation for mining individualized rules of mobile phone users
IH Sarker, A Colman, MA Kabir, J Han
2016 IEEE international conference on data science and advanced analytics …, 2016
172016
Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method
NK Chowdhury, MA Kabir, MM Rahman, SMS Islam
Computers in Biology and Medicine 145, 105405, 2022
162022
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