Institution
University of Malaya
Education•Kuala 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.
Topics: Population, Fiber laser, Laser, Ring (chemistry), Electrolyte
Papers published on a yearly basis
Papers
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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
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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
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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
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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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Diederick E. Grobbee | 155 | 1051 | 122748 |
Intae Yu | 134 | 1372 | 89870 |
Ovsat Abdinov | 129 | 864 | 78489 |
Jyothsna Rani Komaragiri | 129 | 1097 | 82258 |
Odette Benary | 128 | 844 | 74238 |
Paul M. Vanhoutte | 127 | 868 | 62177 |
Irene Vichou | 126 | 762 | 72520 |
Ian O. Ellis | 126 | 1051 | 75435 |
Louisa Degenhardt | 126 | 798 | 139683 |
Matthew Jones | 125 | 1161 | 96909 |
Andrius Juodagalvis | 118 | 1069 | 67138 |
Martin Ravallion | 115 | 570 | 55380 |
R. St. Denis | 112 | 921 | 65326 |
Xiao-Ming Chen | 108 | 596 | 42229 |
A. Yurkewicz | 106 | 514 | 51537 |