scholarly journals Technical Domain Classification of Bangla Text using BERT

2021 ◽  
Author(s):  
Koyel Ghosh ◽  
Apurbalal Senapati
Keyword(s):  
Deep Learning ◽  
Computer Science ◽  
Communication Technology ◽  
Text Classification ◽  
Language Model ◽  
Learning Model ◽  
Coarse Grained ◽  
Deep Learning Model

Coarse-grained tasks are primarily based on Text classification, one of the earliest problems in NLP, and these tasks are done on document and sentence levels. Here, our goal is to identify the technical domain of a given Bangla text. In Coarse-grained technical domain classification, such a piece of the Bangla text provides information about specific Coarse-grained technical domains like Biochemistry (bioche), Communication Technology (com-tech), Computer Science (cse), Management (mgmt), Physics (phy) Etc. This paper uses a recent deep learning model called the Bangla Bidirectional Encoder Representations Transformers (Bangla BERT) mechanism to identify the domain of a given text. Bangla BERT (Bangla-Bert-Base) is a pretrained language model of the Bangla language. Later, we discuss the Bangla BERT accuracy and compare it with other models that solve the same problem.

Detection and classification of arrhythmia using an explainable deep learning model

2021 ◽  
Author(s):  
Yong-Yeon Jo ◽  
Joon-myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Yong-Hyeon Cho ◽  
Jae-Hyun Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Prabhakar ◽  
Dong-Ok Won
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Patient Information ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Medical Text ◽  
Deep Learning Model

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short-term memory (QC-LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC-LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.

scholarly journals Attention-Guided Digital Adversarial Patches on Visual Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dapeng Lang ◽  
Deyun Chen ◽  
Ran Shi ◽  
Yongjun He
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Detection Algorithm ◽  
Classification Model ◽  
Practical Application ◽  
Single Target ◽  
Attachment Mechanism ◽  
Human Eyes ◽  
Deep Learning Model

Deep learning has been widely used in the field of image classification and image recognition and achieved positive practical results. However, in recent years, a number of studies have found that the accuracy of deep learning model based on classification greatly drops when making only subtle changes to the original examples, thus realizing the attack on the deep learning model. The main methods are as follows: adjust the pixels of attack examples invisible to human eyes and induce deep learning model to make the wrong classification; by adding an adversarial patch on the detection target, guide and deceive the classification model to make it misclassification. Therefore, these methods have strong randomness and are of very limited use in practical application. Different from the previous perturbation to traffic signs, our paper proposes a method that is able to successfully hide and misclassify vehicles in complex contexts. This method takes into account the complex real scenarios and can perturb with the pictures taken by a camera and mobile phone so that the detector based on deep learning model cannot detect the vehicle or misclassification. In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. Through the test of different detectors, the patch generated in the single target detection algorithm can also attack other detectors and do well in transferability. Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. Affected by the real world, such as distance, light, angles, resolution, etc., the false classification of the target is realized by reducing the confidence level and background of the target, which greatly perturbs the detection results of the target detector. In COCO Dataset 2017, it reveals that the success rate of this algorithm reaches 88.7%.

1140P Validation of a deep learning model for the classification of lung cancer in a large cohort of biopsied samples

2021 ◽  
Vol 32 ◽  
pp. S926-S927
Author(s):  
G. Toyokawa ◽  
Y. Yamada ◽  
N. Haratake ◽  
Y. Shiraishi ◽  
T. Takenaka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Large Cohort ◽  
Deep Learning Model
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