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RISS 인기검색어
- 검색키워드
- 저자 : Hidayaturrahman (검색결과 3 건)
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Microplastics in Wastewater Treatment Plants (WWTPs): A Review
Haerul Hidayaturrahman,이태관,권혁준 한국수처리학회 2019 한국수처리학회지 Vol.27 No.5
Microplastics (MPs) in the aquatic environment has recently been documented as an environmental threat due to their negative impact on the ecosystem. Wastewater treatment plants (WWTPs) are expected to be main recipients of microplastics entering into freshwater. Everyday, continuously, treated water containing microplastics from WWTP is discharged in large quantities. The aim of this article is providing a comprehensive knowledge to better understand characteristics of MPs in wastewater, the fate and transport of MPs during treatment process in WWTP, abundance of MPs discharged from WWTP and removal efficiency of MPs in lab scale and existing technologies at WWTPs. The concentration of microplastics varying from 0-91 MP/L (final effluent) and approximately > 32 MP/m3 in fresh water. Microplastics can be removed in the primary, secondary and tertiary treatment with the removal efficiency were 41%-65%, 0.2%-14% ,and 0.2-14%, respectively. Treated water and sewage sludge act as direct and indirect pollutants into freshwater. The presence of microplastics in the environment has potential as a threat to the aquatic organism (ecotoxicological and ecological risk). Some studies have tried to remove microplastics in wastewater using artificial microplastic with performance with a range of 60-99%. On the other hand, it is necessary to introduce new technology and improve existing technologies in the WWTP that can be correctly used to remove microplastic from wastewater.
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Sarcasm Detection in Twitter - Performance Impact While Using Data Augmentation: Word Embeddings
Alif Tri Handoyo,Hidayaturrahman,Derwin Suhartono,Criscentia Jessica Setiadi 한국지능시스템학회 2022 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.22 No.4
Sarcasm is the use of words commonly used to ridicule someone or for humorous purposes. Several studies on sarcasm detection have utilized different learning algorithms. However,most of these learning models have always focused on the contents of expression only, thusleaving the contextual information in isolation. As a result, they failed to capture the contextualinformation in the sarcastic expression. Moreover, some datasets used in several studies havean unbalanced dataset, thus impacting the model result. In this paper, we propose a contextualmodel for sarcasm identification in Twitter using various pre-trained models and augmentingthe dataset by applying Global Vector representation (GloVe) for the construction of wordembedding and context learning to generate more sarcastic data, and also perform additionalexperiments by using the data duplication method. Data augmentation and duplication impactis tested in various datasets and augmentation sizes. In particular, we achieved the bestperformance after using the data augmentation method to increase 20% of the data labeledas sarcastic and improve the performance by 2.1% with an F1 Score of 40.44% compared to38.34% before using data augmentation in the iSarcasm dataset.
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