ERNIE-BiLSTM Based Chinese Text Sentiment Classification Method

Chi, Chengying; Zhan, Xu

doi:10.1109/iccea53728.2021.00024

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Reference [4] uses pure deep learning to achieve target recognition, but the recognition accuracy does not meet the requirements; In the literature [5] , it is proposed to use the deep learning algorithm to detect the drug text area and then perform the text recognition method for visual-based drug recognition. After matching the text information [6] , the recognition accuracy fully meets the requirements, but it is difficult for this method to solve the problem that the shape of the text in the image is distorted in the case of random placement of drugs; Reference [7] uses a semi-supervised text recognition method based on STN(Spatial Transformation Network) and CNN (Convolutional Neural Network) to achieve invariance to spatial transformation and improve the recognition of drug text in the case of random placement of drugs, but this method cannot deal with the problem of the loss of text information caused by the long distance of medicines; Relatively speaking, it is difficult to avoid misidentification by simply using image information. Literature [8] uses RFID (Radio Frequency Identification) technology to complete modern intelligent sorting, and the accuracy rate fully meets the requirements and is little affected by interference factors, but this method It is unavoidable that medicines are mixed with packaging.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of drug pick-and-place robot for elderly service

Diao¹,

Huang²,

Lin³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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At present, there is a lack of targeted methods for service robots in drug identification and pick-and-place. The traditional visual-based drug identification method often has low recognition accuracy when performing repeated drug identification tasks, and is relatively affected by the shooting angle. To solve these problems, this paper applies digital image processing, radio frequency identification and voice technology and other technologies to propose a drug picking and placing robot system for the elderly. In order to further guarantee the reliability and accuracy of drug identification, this paper takes use of radio frequency information and voice information on the basis of image recognition. In order to achieve real-time and accurate positioning and path planning, this paper uses the information from the host computer and encoder on the basis of using lidar positioning. Experiments such as fixed-point movement and drug grasping test, as well as drug identification test show that the robot system has extremely high reliability and accuracy in drug identification.

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Section: Introductionmentioning

confidence: 99%

Design and implementation of drug pick-and-place robot for elderly service

Diao¹,

Huang²,

Lin³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

View full text Add to dashboard Cite

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“…Secondly, a DL model is prepared or trained with the data to classify emotion. Along with different processing techniques, different DL models are investigated in the last several years for emotion recognition (Batbaatar et al, 2019;Guo et al, 2021a). These DL-based methods run on preprocessed data and do not include explicit functionality (Batbaatar et al, 2019;Yang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN

Habib¹,

Akhand²,

Kamal³

2022

Journal of Computer Science

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Microblog, an online-based broadcast medium, is a widely used forum for people to share their thoughts and opinions. Recently, Emotion Recognition (ER) from microblogs is an inspiring research topic in diverse areas. In the machine learning domain, automatic emotion recognition from microblogs is a challenging task, especially, for better outcomes considering diverse content. Emoticon becomes very common in the text of microblogs as it reinforces the meaning of content. This study proposes an emotion recognition scheme considering both the texts and emoticons from microblog data. Emoticons are considered unique expressions of the users' emotions and can be changed by the proper emotional words. The succession of emoticons appearing in the microblog data is preserved and a 1D Convolutional Neural Network (CNN) is employed for emotion classification. The experimental result shows that the proposed emotion recognition scheme outperforms the other existing methods while tested on Twitter data.

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