Tectonic discrimination and application based on convolution neural network and incomplete big data

2021 ◽  
Vol 220 ◽  
pp. 106662
Author(s):  
Can Ge ◽  
Jinjing Huo ◽  
Hai-Ou Gu ◽  
Fangyue Wang ◽  
He Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Convolution Neural Network

scholarly journals Deep Convolution Neural Network for Big Data Medical Image Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 105659-105670 ◽  
Author(s):  
Rehan Ashraf ◽  
Muhammad Asif Habib ◽  
Muhammad Akram ◽  
Muhammad Ahsan Latif ◽  
Muhammad Sheraz Arshad Malik ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Image Classification ◽  
Medical Image ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network ◽  
Medical Image Classification

Deep Learning Based Sentiment Classification on User-Generated Big Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1047-1056
Author(s):  
Akshi Kumar ◽  
Arunima Jaiswal
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Soft Computing ◽  
Sentiment Classification ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Computing Technique ◽  
Proposed Model ◽  
Soft Computing Techniques

Background: Sentiment analysis of big data such as Twitter primarily aids the organizations with the potential of surveying public opinions or emotions for the products and events associated with them. Objective: In this paper, we propose the application of a deep learning architecture namely the Convolution Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown Twitter datasets for sentiment classification. The performance efficacy of the proposed model has been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy, recall, and F-measure as key performance indicators. Methods: Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks. Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s performance. Results: The empirical analysis validates that the proposed implementation of the CNN model outperforms the baseline supervised learning algorithms with an accuracy of around 87% to 88%. Conclusion: Statistical analysis validates that the proposed CNN model outperforms the existing techniques and thus can enhance the performance of sentiment classification viability and coherency.

scholarly journals Semantic Web Mining for Analyzing Retail Environment Using Word2Vec and CNN-FK

2021 ◽  
Vol 26 (3) ◽  
pp. 311-318
Author(s):  
Praveen Kumar Yechuri ◽  
Suguna Ramadass
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Semantic Web ◽  
Web Mining ◽  
Convolution Neural Network ◽  
It Services ◽  
Retail Environment ◽  
Word Representation ◽  
Function Diagram ◽  
Work Done

Digital Technology is becoming increasingly essential to organizations. Related knowledge is important for a company to allow optimal use of its IT services. The use of Big Data is relatively new to this field. Handling Big data is not, at this stage, a problem for large business organizations in particular; it has also become a challenge for small and medium-sized businesses. Although Semantic Web analysis is largely focused on fundamental advances that are expected to make the Semantic Web a reality, there has not been much work done to demonstrate the feasibility and effect of the Semantic Web on business issues. The infrastructure of electronic information executives and business types has provided various enhancements for companies, such as the automated process of buying and selling products. Nevertheless, undertakings are checked for the multifaceted nature of the extension required to deal with an ever-increasing number of electronic details and procedures. This paper suggests a model with a neural network design and a word representation system named Word2Vec for analyzing retail environment. Firstly, Word2vec manages the text data and shows it as a function diagram and a feature map is given to the Convolution Neural Network (CNN) that extracts the features and classifies them. The IMDB dataset, the Cornell dataset, the Amazon Products Dataset and the Twitter dataset were analyzed in the proposed model. The proposed Convolution Neural Network Fisher Kernel (CNN-FK) model is compared with the existing SVM model for analyzing retail environment in semantic web mining. The new approach has increased efficiency when compared to existing models.

scholarly journals Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cheng Zhang ◽  
Xingjun Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Ming Dynasty ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Big Data Analysis ◽  
Convolution Neural Network ◽  
Chinese Characters ◽  
Deep Convolution Neural Network

In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters.

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

Sign in / Sign up

Export Citation Format

Share Document