Institution
University of South Africa
Education•Pretoria, South Africa•
About: University of South Africa is a education organization based out in Pretoria, South Africa. It is known for research contribution in the topics: Context (language use) & Population. The organization has 8478 authors who have published 19960 publications receiving 237688 citations. The organization is also known as: Unisa.
Papers published on a yearly basis
Papers
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TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex, using the Cause of Death Ensemble model.
11,809 citations
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TL;DR: Prevalence and severity of health loss were weakly correlated and age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010, but population growth and ageing have increased YLD numbers and crude rates over the past two decades.
7,021 citations
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TL;DR: The results for 1990 and 2010 supersede all previously published Global Burden of Disease results and highlight the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account.
6,861 citations
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Jeffrey D. Stanaway1, Ashkan Afshin1, Emmanuela Gakidou1, Stephen S Lim1 +1050 more•Institutions (346)
TL;DR: This study estimated levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs) by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017 and explored the relationship between development and risk exposure.
2,910 citations
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TL;DR: This review, which focuses on the application of CNNs to image classification tasks, covers their development, from their predecessors up to recent state-of-the-art deep learning systems.
Abstract: Convolutional neural networks CNNs have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze 1 their early successes, 2 their role in the deep learning renaissance, 3 selected symbolic works that have contributed to their recent popularity, and 4 several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.
2,366 citations
Authors
Showing all 8743 results
Name | H-index | Papers | Citations |
---|---|---|---|
Malik Maaza | 68 | 561 | 13897 |
Eno E. Ebenso | 66 | 346 | 13359 |
Ernst Bauer | 63 | 666 | 18214 |
Dipti Srinivasan | 63 | 410 | 14418 |
Loan Truong | 63 | 251 | 13113 |
Robert J. Elliott | 63 | 641 | 18057 |
Simplice A. Asongu | 62 | 1051 | 18242 |
Michelle Fine | 62 | 187 | 13889 |
Linda Richter | 61 | 325 | 20160 |
Airton Deppman | 60 | 221 | 12129 |
Jian Zuo | 60 | 526 | 12698 |
Giovanni Blandino | 59 | 274 | 13159 |
Angus I. Kingon | 59 | 361 | 14582 |
Abdon Atangana | 59 | 341 | 14555 |
Luis Flores Castillo | 59 | 166 | 9886 |