scholarly journals Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

2021 ◽  
Vol 35 (4) ◽  
pp. 273-280
Author(s):  
Pillai Honey Nagarajan ◽  
N. Tajunisha
Keyword(s):  
Neural Network ◽  
Ovarian Cancer ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Ct Images ◽  
Generative Learning ◽  
Cancer Types

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

2015 ◽  
Vol 26 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Francesco Ciompi ◽  
Bartjan de Hoop ◽  
Sarah J. van Riel ◽  
Kaman Chung ◽  
Ernst Th. Scholten ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Automatic Classification

scholarly journals Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0234959 ◽  
Author(s):  
Daniel Motta ◽  
Alex Álisson Bandeira Santos ◽  
Bruna Aparecida Souza Machado ◽  
Otavio Gonçalvez Vicente Ribeiro-Filho ◽  
Luis Octavio Arriaga Camargo ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Classification

scholarly journals Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network

2020 ◽  
Vol 8 (7) ◽  
pp. 486-486
Author(s):  
Gaoshuang Liu ◽  
Jie Hua ◽  
Zhan Wu ◽  
Tianfang Meng ◽  
Mengxue Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Endoscopic Images

scholarly journals Semi-Supervised Convolutional Neural Network for Law Advice Online

2019 ◽  
Vol 9 (17) ◽  
pp. 3617 ◽  
Author(s):  
Fen Zhao ◽  
Penghua Li ◽  
Yuanyuan Li ◽  
Jie Hou ◽  
Yinguo Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Learning Task ◽  
Automatic Classification ◽  
Labor Cost ◽  
Internet Technology ◽  
Data Annotation

With the rapid developments of Internet technology, a mass of law cases is constantly occurring and needs to be dealt with in time. Automatic classification of law text is the most basic and critical process in the online law advice platform. Deep neural network-based natural language processing (DNN-NLP) is one of the most promising approaches to implement text classification. Meanwhile, as the convolutional neural network-based (CNN-based) methods developed, CNN-based text classification has already achieved impressive results. However, previous work applied amounts of manually-annotated data, which increased the labor cost and reduced the adaptability of the approach. Hence, we present a new semi-supervised model to solve the problem of data annotation. Our method learns the embedding of small text regions from unlabeled data and then integrates the learned embedding into the supervised training. More specifically, the learned embedding regions with the two-view-embedding model are used as an additional input to the CNN’s convolution layer. In addition, to implement the multi-task learning task, we propose the multi-label classification algorithm to assign multiple labels to an instance. The proposed method is evaluated experimentally subject to a law case description dataset and English standard dataset RCV1 . On Chinese data, the simulation results demonstrate that, compared with the existing methods such as linear SVM, our scheme respectively improves by 7.76%, 7.86%, 9.19%, and 2.96% the precision, recall, F-1, and Hamming loss. Analogously, the results suggest that compared to CNN, our scheme respectively improves by 4.46%, 5.76%, 5.14% and 0.87% in terms of precision, recall, F-1, and Hamming loss. It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text. Furthermore, the design concept proposed is promising, which can be utilized in other real-world applications such as news classification and public opinion monitoring.

A comparative analysis of euphemistic sentences in news using feature weight scheme and intelligent techniques

2021 ◽  
pp. 1-12
Author(s):  
K. Seethappan ◽  
K. Premalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Automatic Classification ◽  
Weighting Scheme ◽  
Feature Weighting ◽  
Classification Algorithms ◽  
Weighting Schemes

Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset.

Automatic classification of pavement crack using deep convolutional neural network

2018 ◽  
Vol 21 (4) ◽  
pp. 457-463 ◽  
Author(s):  
Baoxian Li ◽  
Kelvin C. P. Wang ◽  
Allen Zhang ◽  
Enhui Yang ◽  
Guolong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Deep Convolutional Neural Network

Jamo Unit Convolutional Neural Network Based Automatic Classification of Frequently Asked Questions with Spelling Errors

2019 ◽  
Vol 46 (6) ◽  
pp. 563-569
Author(s):  
Youngjin Jang ◽  
Harksoo Kim ◽  
Dongho Kang ◽  
Sebin Kim ◽  
Hyunki Jang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Spelling Errors
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