Ra-CNN

Zhan, Zhiwei; Liao, Guo‐Shiou; Ren, Xiang; Xiong, Guangsi; Zhou, Weilin; Jiang, Wenchao; Hong, Xia

doi:10.4018/ijssci.311446

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Previous studies have identified a new approach to analyzing the logical structure and hierarchical relationship of emotional factors through the use of explanatory structure models. Scholars such as Zhan (2022) and Zemmouchi-Ghomari (2021) have contributed to this area of research.…”

Section: Application Of Sem To Behavioral Researchmentioning

confidence: 99%

Semantic Web-Based Structural Equation Modeling and Mediating Effects Are Used to Investigate Key Factors

Ma,

Gai

2024

International Journal on Semantic Web and Information Systems

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Land desertification is the key contradiction restricting the sustainable development of Chinese society. Farmers and herders' behavior in desert management is particularly important for the smooth development of the desertification control project. Although farmers and herders express willingness, they do not engage in desert management behavior. The research through random sampling survey analyzes survey data from 572 farmers and herders in the Kubuqi Desert region of Inner Mongolia using structural equation modeling and mediation analysis, based on the TPB. The aim is to understand the paradoxical willingness and behavior of farmers and herders to participate in desert management. The study found that farmers and herders' willingness to participate is a crucial factor that influences their behavior. The authors suggest cultivating a sense of ecological responsibility and strengthening ecological education to guide the behavior of farmers and herders towards more sustainable practices.

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Section: Application Of Sem To Behavioral Researchmentioning

confidence: 99%

Semantic Web-Based Structural Equation Modeling and Mediating Effects Are Used to Investigate Key Factors

Ma,

Gai

2024

International Journal on Semantic Web and Information Systems

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“…The of natural language processing (NLP) using named entity recognition (NER) is challenging (Pathoee et al, 2022;Capuano et al, 2022). In the medical field, staff enter patients' information in computers that store the information in the medical institution's information system, creating electronic medical records (EMR) (Alomani et al, 2022;Zhan et al, 2022;Zemmouchi-Ghomari, 2021). EMR named entity recognition is an important application and extension of named entity recognition in EMR text analysis (Pareek et al, 2022;Hu et al, 2022;Ismail et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM

Lai,

Jie

2023

International Journal on Semantic Web and Information Systems

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Aiming at the problems of missing local context features, single word vector representation, and low entity recognition accuracy, a method for e-medical recording with named entity recognition, which is based on BERT and model fusion, is proposed. First, with the model of BERT for pre-training, the preceding and following contextual information is fused for the enhancement of word semantic representation and alleviation of the problem of polysemy; second, the network of bi-directional long-short term memory is for obtaining the sequence feature matrix, generation of optimal sequence in global sense achieved through the conditional random field model; finally, data enhancement is used to alleviate the class imbalance and improve the model ability in generalization. Results of the experiments find model proposal measured by F1 on CCKS21 data set reaches 0.8548, which is 0.51% and 0.08% higher than models with ID-CNNs-CRF and multi-task RNN. This demonstrates the excellent performance of the method proposed in this paper in improving named entity recognition.

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Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

Leo,

Carcagnì,

Signore

et al. 2024

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Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients.

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Ra-CNN

Cited by 4 publications

References 14 publications

Semantic Web-Based Structural Equation Modeling and Mediating Effects Are Used to Investigate Key Factors

Semantic Web-Based Structural Equation Modeling and Mediating Effects Are Used to Investigate Key Factors

A Named Entity Recognition Approach for Electronic Medical Records Using BERT Semantic Enhancement and BiLSTM

Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

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