Using Data Mining Techniques for COVID-19: A Systematic Review
Sanjib Ghosh,
Lipon Chandra Das
Issue:
Volume 8, Issue 2, June 2022
Pages:
36-42
Received:
2 April 2022
Accepted:
1 June 2022
Published:
16 June 2022
DOI:
10.11648/j.ijdst.20220802.11
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Abstract: The primary goal of this survey is to determine the most widely used data mining approaches and knowledge gaps from published publications. The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Since the threat of pandemics has raised public health concerns, researchers to uncover hidden knowledge have used data extraction techniques. Web of Science, Scopus, and PubMed databases were used to conduct systematic research. Then, to choose good papers, all retrieved publications were reviewed in a stepwise procedure using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. All of the data were examined and summarized using a few different classifications. Out of 300 citations, 50 papers were eligible through a systematic review. The review results showed that the most favorite DM belonged to Natural language processing (22%), and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies predominately apply supervised learning techniques (90%). We found infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline concerning healthcare scopes. The most common software used in the studies was SPSS (22%) and R (20%). Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease. The results revealed valuable research conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. However, most research will need in terms of treatment and disease control.
Abstract: The primary goal of this survey is to determine the most widely used data mining approaches and knowledge gaps from published publications. The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Since the threat of pandemics has raised public health concerns, researchers to uncover hidden knowledge have used da...
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Fine Crack Detection Algorithm Based on Improved SSD
Mai Ziying,
Hu Shaolin,
Huang Xiaomin,
Ke Ye
Issue:
Volume 8, Issue 2, June 2022
Pages:
43-47
Received:
25 May 2022
Accepted:
9 June 2022
Published:
16 June 2022
DOI:
10.11648/j.ijdst.20220802.12
Downloads:
Views:
Abstract: The fine cracks on the workpiece surface are the significant potential hazard to safety of industrial production process. In order to identify fine cracks on the workpiece surface, an improved SSD (Single Shot MultiBox Detector) algorithm is built in this paper and applied to detect fine cracks. Based on the SSD network, the dilated convolution module is proposed in the convolutional operation to ensure access to global feature and by reducing the pooling layer treatment. In order to achieve the effective cracks detection, the cracks images are divided into two cases: obvious bold cracks and vague fine cracks, and mark them respectively. The obvious bold cracks are marked as "neg" and detected by SSD network framework, while the vague fine cracks are marked as "crack" and detected by SSD network with reduced pooling layer. This improvement is helpful to increase the detection accuracy of fine cracks. In this paper, the actual crack images are used to verify the improved algorithm. Results show that under the training and testing with workpiece crack data set, the improved algorithm can effectively detect fine cracks such that the detection precision toward the number of cracks in the image is higher than 80%. The aforementioned algorithms present potential application for the detection of fine cracks.
Abstract: The fine cracks on the workpiece surface are the significant potential hazard to safety of industrial production process. In order to identify fine cracks on the workpiece surface, an improved SSD (Single Shot MultiBox Detector) algorithm is built in this paper and applied to detect fine cracks. Based on the SSD network, the dilated convolution mod...
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