S
Siddique Latif
Researcher at University of Southern Queensland
Publications - 73
Citations - 2163
Siddique Latif is an academic researcher from University of Southern Queensland. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 18, co-authored 57 publications receiving 1146 citations. Previous affiliations of Siddique Latif include University of Groningen & Information Technology University.
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Journal ArticleDOI
Community detection in networks: A multidisciplinary review
Muhammad Aqib Javed,Muhammad Shahzad Younis,Siddique Latif,Siddique Latif,Junaid Qadir,Adeel Baig,Adeel Baig +6 more
TL;DR: A contemporary survey on the methods of community detection and its applications in the various domains of real life by reviewing prevailing community detection algorithms that range from traditional algorithms to state of the art algorithms for overlapping community detection.
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Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Siddique Latif,Muhammad Usman,Sanaullah Manzoor,Waleed Iqbal,Junaid Qadir,Gareth Tyson,Ignacio Castro,Adeel Razi,Maged N. Kamel Boulos,Adrian Weller,Jon Crowcroft +10 more
TL;DR: This paper attempts to systematise the various COVID-19 research activities leveraging data science, where data science is defined broadly to encompass the various methods and tools that can be used to store, process, and extract insights from data.
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How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare
TL;DR: It is built the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future.
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Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study
TL;DR: The first to conduct a case study on the public health system of Pakistan showing that mHealth can offer tremendous opportunities for a developing country with a severe scarcity of health infrastructure and resources is conducted.
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Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection
TL;DR: This paper proposes a Recurrent Neural Networks-based automated cardiac auscultation solution, and explores the use of various RNN models, and demonstrates that these models significantly outperform the best reported results in the literature.