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Institution

Universiti Teknologi Malaysia

EducationJohor Bahru, Malaysia
About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the advantages and limitations of these gasifiers are compared, followed by discussion on the key parameters that are critical for the optimum production of syngas, which can be used directly as fuel source for power generation and transport fuel.
Abstract: Synthesis gas, also known as syngas, produced from biomass materials has been identified as a potential source of renewable energy. Syngas is mainly consists of CO and H 2 , which can be used directly as fuel source for power generation and transport fuel, as well as feedstock for chemical production. Syngas is produced through biomass gasification process that converts solids to gas phase via thermochemical conversion reactions. This paper critically reviews the type of gasifiers that have been used for biomass gasification, including fixed bed, fluidized bed, entrained flow and transport reactor types. The advantages and limitations of these gasifiers are compared, followed by discussion on the key parameters that are critical for the optimum production of syngas. Depending on the biomass feedstock, the properties and characteristics of syngas produced can be varied. It is thus essential to thoroughly characterise the properties of biomass to understand the limitations in order to identify the suitable methods for gasification. This paper later focuses on a specific biomass – oil palm-based for syngas production in the context of Malaysia, where palm biomass is readily available in abundance. The properties and suitability for gasification of the major palm biomass, including empty fruit bunch, oil palm fronds and palm kernel shells are reviewed. Optimization of the gasification process can significantly improve the prospect of commercial syngas production.

121 citations

Journal ArticleDOI
TL;DR: In this paper, the effect of using the wavy-up and wavydown rectangular winglets as special forms of winglet is numerically investigated in a relatively low Reynolds number flow.

120 citations

Journal ArticleDOI
TL;DR: The ratio of biomass to MW absorber not only affected the temperature profiles of the EFB but also pyrolysis products such as bio-oil, char, and gas.

120 citations

Journal ArticleDOI
TL;DR: In this article, the adsorption performance of Reactive Black 5 (RB5) and methylene blue (MB) onto activated carbon produced from textile sludge (TSAC) was investigated.

120 citations

Journal ArticleDOI
TL;DR: The proposed CBPLR has significant impact in penalized logistic regression by selecting fewer genes with high area under the curve and low misclassification rate, which could conceivably be used in other research that implements gene selection in the field of high dimensional cancer classification.
Abstract: The CBPLR showed superior results in terms of AUR and misclassification rate.In terms of the number of selected genes, the CBPLR outperformed APLR and LASSO.The CBPLR performed remarkably well in stability test.The classification accuracy for the CBPLR method is quite consistent and high. An important application of DNA microarray data is cancer classification. Because of the high-dimensionality problem of microarray data, gene selection approaches are often employed to support the expert systems in diagnostic capability of cancer with high classification accuracy. Penalized logistic regression using the least absolute shrinkage and selection operator (LASSO) is one of the key steps in high-dimensional cancer classification, as gene coefficient estimation and gene selection simultaneously. However, the LASSO has been criticized for being biased in gene selection. The adaptive LASSO (APLR) was originally proposed to overcome the selection bias by assigning a consistent weight to each gene. In high-dimensional data, however, the adaptive LASSO faces practical problems in choosing the type of initial weight. In practice, the LASSO estimator itself has been used as an initial weight. However, this may not be preferable because the LASSO is inconsistent in itself. To address this issue, an alternative initial weight in adaptive penalized logistic regression (CBPLR) is proposed. The effectiveness of the CBPLR is examined on three well-known high-dimensional cancer classification datasets using number of selected genes, area under the curve, and misclassification rate. The experimental results reveal that the proposed CBPLR is quite efficient and feasible for cancer classification. Additionally, the proposed weight is compared with APLR and LASSO and exhibits competitive performance in both classification accuracy and gene selection. The proposed CBPLR has significant impact in penalized logistic regression by selecting fewer genes with high area under the curve and low misclassification rate. Thus, the proposed weight could conceivably be used in other research that implements gene selection in the field of high dimensional cancer classification.

120 citations


Authors

Showing all 21852 results

NameH-indexPapersCitations
Xin Li114277871389
Muhammad Imran94305351728
Ahmad Fauzi Ismail93135740853
Bin Tean Teh9247133359
Muhammad Farooq92134137533
M. A. Shah9258337099
Takeshi Matsuura8554026188
Peter Willett7647929037
Peter C. Searson7437421806
Ozgur Kisi7347819433
Imran Ali7230019878
S.M. Sapuan7071319175
Peter J. Fleming6652924395
Mohammad Jawaid6550319471
Muhammad Tahir65163623892
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202371
2022347
20212,811
20203,003
20193,148
20182,980