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Institution

University of Malaya

EducationKuala Lumpur, Malaysia
About: University of Malaya is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Fiber laser. The organization has 25087 authors who have published 51491 publications receiving 1036791 citations. The organization is also known as: UM & Universiti Malaya.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigated the dynamic relationship between coal consumption, economic growth, trade openness and CO2 emissions in case of India and found that coal consumption as well as trade openness contributes to CO2 emission.
Abstract: This study investigates the dynamic relationship between coal consumption, economic growth, trade openness and CO2 emissions in case of India. In doing so, Narayan and Popp, Journal of Applied Statistics 2010; 37:1425–1438, structural break unit test is applied to test the order of integration of the variables. Long run relationship between the variables is tested by applying the ARDL bounds testing approach to cointegration developed by Pesaran et al. Journal of Applied Econometrics 2001; 16:289–326. The results confirm the existence of cointegration for long run between coal consumption, economic growth, trade openness and CO2 emissions. Our empirical exercise indicates the presence of environmental Kuznets curve (EKC) in long run as well as in short run. Coal consumption as well as trade openness contributes to CO2 emissions. The causality analysis reports the feedback hypothesis between economic growth and CO2 emissions and same inference is drawn between coal consumption and CO2 emissions. Moreover, trade openness Granger causes economic growth, coal consumption and CO2 emissions.

378 citations

Proceedings ArticleDOI
01 Dec 2013
TL;DR: This paper introduces a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints and significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations.
Abstract: This paper presents an approach to text recognition in natural scene images. Unlike most existing works which assume that texts are horizontal and frontal parallel to the image plane, our method is able to recognize perspective texts of arbitrary orientations. For individual character recognition, we adopt a bag-of-key points approach, in which Scale Invariant Feature Transform (SIFT) descriptors are extracted densely and quantized using a pre-trained vocabulary. Following [1, 2], the context information is utilized through lexicons. We formulate word recognition as finding the optimal alignment between the set of characters and the list of lexicon words. Furthermore, we introduce a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints. Experimental results on public datasets and the proposed dataset show that our method significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations.

378 citations

Journal ArticleDOI
TL;DR: This review discusses the available methods of various retinal feature extractions and automated analysis for diagnosis of diabetic retinopathy.

376 citations

Journal ArticleDOI
TL;DR: In this article, the dark fermentative bio-hydrogen production from biomass is discussed and the types of potential biomass that could be the source for biohydrogen generation such as food and starch-based wastes, cellulosic materials, dairy wastes, palm oil mill effluent and glycerol are discussed.

373 citations


Authors

Showing all 25327 results

NameH-indexPapersCitations
Diederick E. Grobbee1551051122748
Intae Yu134137289870
Ovsat Abdinov12986478489
Jyothsna Rani Komaragiri129109782258
Odette Benary12884474238
Paul M. Vanhoutte12786862177
Irene Vichou12676272520
Ian O. Ellis126105175435
Louisa Degenhardt126798139683
Matthew Jones125116196909
Andrius Juodagalvis118106967138
Martin Ravallion11557055380
R. St. Denis11292165326
Xiao-Ming Chen10859642229
A. Yurkewicz10651451537
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202391
2022418
20213,698
20203,646
20193,239
20183,203