scholarly journals Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning

2021 ◽  
Vol 58 (1) ◽  
pp. 558-562
Author(s):  
Xintong Zhao ◽  
Steven Lopez ◽  
Semion Saikin ◽  
Xiaohua Hu ◽  
Jane Greenberg
Keyword(s):  
Deep Learning ◽  
Organic Materials ◽  
Knowledge Extraction ◽  
Materials Knowledge

scholarly journals Deep Learning approaches for Fashion Knowledge Extraction from Social Media: a review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Marco Mameli ◽  
Marina Paolanti ◽  
Rocco Pietrini ◽  
Giulia Pazzaglia ◽  
Emanuele Frontoni ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Knowledge Extraction ◽  
Learning Approaches

Knowledge Extraction of Adaptive Structural Learning of Deep Belief Network for Medical Examination Data

2019 ◽  
Vol 13 (01) ◽  
pp. 67-86 ◽  
Author(s):  
Shin Kamada ◽  
Takumi Ichimura ◽  
Toshihide Harada
Keyword(s):  
Deep Learning ◽  
Network Structure ◽  
Classification Accuracy ◽  
Input Data ◽  
Knowledge Extraction ◽  
Deep Belief Network ◽  
Medical Examination ◽  
Support Vector ◽  
Structural Learning ◽  
Belief Network

Deep learning has a hierarchical network structure to represent multiple features of input data. The adaptive structural learning method of Deep Belief Network (DBN) can reach the high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation–annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN (Adaptive DBN) was applied to the comprehensive medical examination data for cancer prediction. The developed prediction system showed higher classification accuracy for test data (99.5% for the lung cancer and 94.3% for the stomach cancer) than the several learning methods such as traditional RBM, DBN, Non-Linear Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Moreover, the explicit knowledge that makes the inference process of the trained DBN is required in deep learning. The binary patterns of activated neurons for given input in RBM and the hierarchical structure of DBN can represent the relation between input and output signals. These binary patterns were classified by C4.5 for knowledge extraction. Although the extracted knowledge showed slightly lower classification accuracy than the trained DBN network, it was able to improve inference speed by about 1/40. We report that the extracted IF-THEN rules from the trained DBN for medical examination data showed some interesting features related to initial condition of cancer.

A Method for Materials Knowledge Extraction from HTML Tables Based on Sibling Comparison

2016 ◽  
Vol 26 (06) ◽  
pp. 897-926 ◽  
Author(s):  
Xiaoming Zhang ◽  
Pengtao Lv ◽  
Chongchong Zhao ◽  
Jianxian Wang
Keyword(s):  
Information Acquisition ◽  
Knowledge Extraction ◽  
Sibling Comparison ◽  
Table Data ◽  
Rich Data ◽  
Multiple Cells ◽  
Materials Knowledge

There are rich data resources residing in available materials websites, and most of these data resources are shown in the form of HTML tables. However, it is difficult to distinguish the attributes and values because of the semi-structured feature of HTML tables. Therefore, identifying attributes in HTML tables is the key issue for the information acquisition. In this paper, based on sibling comparison, a method for materials knowledge extraction from HTML tables is proposed, which consists of three steps: acquiring sibling tables, identifying table pattern and extracting table data. We show how to use [Formula: see text]-measure to find the appropriate thresholds for matching of tables from materials websites when acquiring sibling tables. Further, we propose a strategy named FRFC (i.e. the First Row matching and First Column matching) to distinguish attributes and values, so that table pattern is identified. Moreover, the data from HTML tables is extracted based on their corresponding table patterns and mapped to a predefined schema, which will facilitate the population to materials ontology. The proposed approach is applicable to circumstances, where an attribute in the table may span multiple cells and matched attributes in sibling tables are more. We acquire desired accuracy ([Formula: see text]%) through using FRFC for identifying table pattern. The time about extraction may not increase significantly with increasing number of documents and cells in tables, so our approach is effective to process a large number of documents. A prototype named MTES is developed and demonstrates the effectiveness of our proposed approach.

Knowledge Extraction in Digit Recognition Using MNIST Dataset

2021 ◽  
Vol 17 (4) ◽  
pp. 52-75
Author(s):  
Rohit Rastogi ◽  
Himanshu Upadhyay ◽  
Akshit Rajan Rastogi ◽  
Divya Sharma ◽  
Prankur Bishnoi ◽  
...  
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Deep Learning ◽  
Handwriting Recognition ◽  
Knowledge Extraction ◽  
Input Devices ◽  
Digit Recognition ◽  
Learning Techniques ◽  
Data Layers ◽  
Better Than

In handwriting recognition, traditional systems have relied heavily on handcrafted features and a massive amount of prior data and knowledge. Deep learning techniques have been the focus of research in the field of handwriting digit recognition and have achieved breakthrough performance in the last few years for knowledge extraction and management. KM and knowledge pyramid helps the project with its relationship with big data and IoT. The layers were selected randomly by which the performance of all the cases was found different. Data layers of the knowledge pyramid are formed by the sensors and input devices, whereas knowledge layers are the result of knowledge extraction applied on data layers. The knowledge pyramid and KM helps in making the use of IoT and big data easily. In this manuscript, the knowledge management principles capture the handwritten gestures numerically and get it recognized correctly by the software. The application of AI and DNN has increased the acceptability significantly. The accuracy is better than other available software on the market.

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