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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: Scientific research and practical applications of SMS as a readily available and cheap source of enzymes for bioremediation, animal feed and energy feedstock are reviewed.
Abstract: Mushroom industries generate a virtually in-exhaustible supply of a co-product called spent mushroom substrate (SMS). This is the unutilised substrate and the mushroom mycelium left after harvesting of mushrooms. As the mushroom industry is steadily growing, the volume of SMS generated annually is increasing. In recent years, the mushroom industry has faced challenges in storing and disposing the SMS. The obvious solution is to explore new applications of SMS. There has been considerable discussion recently about the potentials of using SMS for production of value-added products. One of them is production of lignocellulosic enzymes such as laccase, xylanase, lignin peroxidase, cellulase and hemicellulase. This paper reviews scientific research and practical applications of SMS as a readily available and cheap source of enzymes for bioremediation, animal feed and energy feedstock.

205 citations

Journal ArticleDOI
TL;DR: A novel tool for an automated differentiation of shockable and non-shockable ventricular arrhythmias from 2 s electrocardiogram (ECG) segments is proposed and indicates that shockable life-threatening arrhythmia can be immediately detected and thus increase the chance of survival while CPR or AED-based support is performed.

205 citations

Journal ArticleDOI
TL;DR: The results show that MBA is able to provide faster convergence rate and also manages to achieve better optimal solutions compared to other efficient optimizers.

205 citations

Journal ArticleDOI
TL;DR: A novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition, which is ready for clinical validation on large databases and can be deployed for mass screening.
Abstract: Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.

205 citations

Journal ArticleDOI
TL;DR: The performance of a commercially available palm shell based activated carbon to remove lead ions from aqueous solutions by adsorption was evaluated and the model prediction was found to be in concordance with the experimental values.

205 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