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- 저자 : de Castilho, Richard Eckart (검색결과 4 건)
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Towards cross-platform interoperability for machine-assisted text annotation
de Castilho, Richard Eckart,Ide, Nancy,Kim, Jin-Dong,Klie, Jan-Christoph,Suderman, Keith Korea Genome Organization 2019 Genomics & informatics Vol.17 No.2
In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.
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Towards cross-platform interoperability for machine-assisted text annotation
Richard Eckart de Castilho,Nancy Ide,김진동,Jan-Christoph Klie,Keith Suderman 한국유전체학회 2019 Genomics & informatics Vol.17 No.2
In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.
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Henrique Gasparetto,Ana Luiza Barrachini Nunes,Fernanda de Castilhos,Nina Paula Gonçalves Salau 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.113 No.-
Soybean oil extraction using two green solvents was investigated from solvent selection to thermodynamics:ethyl acetate and 1-butanol. The screening of the solvents was performed using the Hansenparameters and Infinite Dilution Activity Coefficient (IDAC) obtained through the COnductor-likeScreening MOdels – Segment Activity Coefficient (COSMO-SAC) theory. The solvent selection was performedon ethyl acetate and 1-butanol in comparison with ethanol, a well-studied green solvent, andhexane, a non-renewable and industrially used solvent. The effects of temperature and solvent/solid ratioon the yield of soybean oil extraction were investigated through response surface methodology (RSM). The RSM obtained satisfactory statistical results, with R2adj of 0.9958 for ethyl acetate and 0.9729 for 1-butanol. The kinetic of the extractions were evaluated using two different models: mass transfer kineticand So and Macdonald. The last one obtained the best correlation to the data (R2 > 0.9964). The thermodynamicassessment showed endothermic, and spontaneous processes for both solvents. 1-Butanol, ethylacetate, and hexane have a better performance on the yield of soybean oil extraction than using ethanol;however, ethyl acetate is the best candidate to replace the industrial use of hexane due to its highest rateof soybean oil extraction at the process beginning.
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Costa Andre Luiz Ferreira,Fardim Karolina Aparecida Castilho,Ribeiro Isabela Teixeira,Jardini Maria Aparecida Neves,Braz-Silva Paulo Henrique,Orhan Kaan,de Castro Lopes Sérgio Lúcio Pereira 대한영상치의학회 2023 Imaging Science in Dentistry Vol.53 No.1
Purpose: This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively). Materials and Methods: CBCT images of 40 patients diagnosed with OS (N = 20) and NOS (N = 20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α = 5%). Results: The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy. Conclusion: TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.
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