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Deep Learning for Dengue Fever Event Detection Using Online News
Khotimah P.H.a, Fachrur Rozie A.a, Nugraheni E.a, Arisal A.a, Suwarningsih W.a, Purwarianti A.b
a Indonesian Institute of Sciences, Research Center for Informatics, Bandung, Indonesia
b Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.Dengue fever currently has been a hyperendemic infectious disease in Indonesia. Early detection ability of the dengue fever events are essential for a timely and effective response to prevent outbreaks. This paper presents dengue fever event detection using online news. A previous study conducted an event detection task from sentences using word frequency burst to detect an ongoing event. However, news do not only report about the event (i.e., the event of dengue fever case) but also information regarding the disease. This paper focuses on detecting an event of dengue fever from online news. An assessment of different deep learning models is reported in this paper. Using k-fold cross validation, convolutional neural network (CNN) achieved the best performance (in average, test accuracy: 80.019%, precision: 78.561%, recall: 77.747%, and f1-score: 77.234%).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Dengue fevers,Detection ability,Event detection,Hyper-endemic,Infectious disease,K fold cross validations,Learning models,Word frequencies[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]deep learning,dengue fever,event detection,infectious disease[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICRAMET51080.2020.9298630[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]