Institution
Universiti Teknologi Petronas
Education•Ipoh, Malaysia•
About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.
Topics: Adsorption, Ionic liquid, Catalysis, Membrane, Biomass
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a review of recent technological developments in which the advantages of ionic liquids as processing solvents for biopolymers for the production of a plethora of green composites have been gradually realized.
115 citations
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TL;DR: Significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.
Abstract: Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.
114 citations
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TL;DR: In this article, the authors focused on hydrotalcite derived catalysts consisting of different metals like Fe, Ni, Cu, Pt etc. and their performances in hydrogen production at different conditions.
114 citations
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TL;DR: Among the synthesized ionic liquids, [C4(Mim)2][(2HSO4)(H2SO4)4] showed higher % yield of LA up to 47.52 from bamboo biomass at 110°C for 60min, which is the better yield at low temperature and short time compared to previous reports.
114 citations
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TL;DR: This paper reviews and critically analyzes the latest developments in the mathematical modeling and simulation techniques that have been reported for the characteristics and mechanisms of nutrient release from CRFs.
113 citations
Authors
Showing all 6203 results
Name | H-index | Papers | Citations |
---|---|---|---|
Muhammad Imran | 94 | 3053 | 51728 |
Muhammad Shahbaz | 92 | 1001 | 34170 |
Muhammad Farooq | 92 | 1341 | 37533 |
Markus P. Schlaich | 74 | 472 | 25674 |
Abdul Basit | 74 | 570 | 20078 |
Keat Teong Lee | 71 | 276 | 16745 |
Abdul Latif Ahmad | 68 | 490 | 22012 |
Cor J. Peters | 52 | 262 | 9472 |
Suzana Yusup | 52 | 437 | 8997 |
Muhammad Nadeem | 52 | 409 | 9649 |
Umer Rashid | 51 | 381 | 10081 |
Hamidi Abdul Aziz | 49 | 345 | 9083 |
Serge Palacin | 45 | 201 | 8376 |
Muhammad Awais | 43 | 272 | 6704 |
Zakaria Man | 43 | 245 | 5301 |