Evaluation-oriented façade defects detection using rule-based deep learning method

2021 ◽  
Vol 131 ◽  
pp. 103910
Author(s):  
Jingjing Guo ◽  
Qian Wang ◽  
Yiting Li
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Rule Based

scholarly journals A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paramita Ray ◽  
Amlan Chakrabarti
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Learning Method ◽  
Rule Based ◽  
Aspect Extraction ◽  
Art Methods ◽  
Rule Based Approach

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties

2019 ◽  
Vol 579 ◽  
pp. 124207 ◽  
Author(s):  
Yongqi Liu ◽  
Hui Qin ◽  
Zhendong Zhang ◽  
Liqiang Yao ◽  
Yongqiang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reservoir Operation ◽  
Learning Method ◽  
Rule Based ◽  
Operation Rule ◽  
Multiple Uncertainties

scholarly journals Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction

2021 ◽  
Vol 11 (13) ◽  
pp. 5832
Author(s):  
Wei Gou ◽  
Zheng Chen
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Error Correction ◽  
Language Processing ◽  
Classification Model ◽  
Spelling Error ◽  
Learning Method ◽  
Post Processing ◽  
Rule Based ◽  
Model Training

Chinese Spelling Error Correction is a hot subject in the field of natural language processing. Researchers have already produced many great solutions, from the initial rule-based solution to the current deep learning method. At present, SpellGCN, proposed by Alibaba’s team, achieves the best results of which character level precision over SIGHAN2013 is 98.4%. However, when we apply this algorithm to practical error correction tasks, it produces many false error correction results. We believe that this is because the corpus used for model training contains significantly more errors than the text used for model correcting. In response to this problem, we propose performing a post-processing operation on the error correction tasks. We employ the initial model’s output as a candidate character, obtain various features of the character itself and its context, and then use a classification model to filter the initial model’s false error correction results. The post-processing idea introduced in this paper can apply to most Chinese Spelling Error Correction models to improve their performance over practical error correction tasks.

scholarly journals Identifying Diagnosis Evidence of Liver Cancer in Chinese Radiology Reports Using BERT-based Deep Learning Method (Preprint)

2020 ◽  
Author(s):  
Hui Chen ◽  
Honglei Liu ◽  
Ni Wang ◽  
Yanqun Huang ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
Language Processing ◽  
Cancer Diagnosis ◽  
Data Driven ◽  
Learning Method ◽  
Rule Based ◽  
Radiological Features ◽  
Radiology Reports ◽  
Computer Aided

BACKGROUND Liver cancer remains to be a substantial disease burden in China. As one of the primary diagnostic means for liver cancer, the dynamic enhanced computed tomography (CT) scan provides detailed diagnosis evidence that is recorded in the free-text radiology reports. OBJECTIVE In this study, we combined knowledge-driven deep learning methods and data-driven natural language processing (NLP) methods to extract the radiological features from these reports, and designed a computer-aided liver cancer diagnosis framework.In this study, we combined knowledge-driven deep learning methods and data-driven natural language processing (NLP) methods to extract the radiological features from these reports, and designed a computer-aided liver cancer diagnosis framework. METHODS We collected 1089 CT radiology reports in Chinese. We proposed a pre-trained fine-tuning BERT (Bidirectional Encoder Representations from Transformers) language model for word embedding. The embedding served as the inputs for BiLSTM (Bidirectional Long Short-Term Memory) and CRF (Conditional Random Field) model (BERT-BiLSTM-CRF) to extract features of hyperintense enhancement in the arterial phase (APHE) and hypointense in the portal and delayed phases (PDPH). Furthermore, we also extracted features using the traditional rule-based NLP method based on the content of radiology reports. We then applied random forest for liver cancer diagnosis and calculated the Gini impurity for the identification of diagnosis evidence. RESULTS The BERT-BiLSTM-CRF predicted the features of APHE and PDPH with an F1 score of 98.40% and 90.67%, respectively. The prediction model using combined features had a higher performance (F1 score, 88.55%) than those using the single kind of features obtained by BERT-BiLSTM-CRF (84.88%) or traditional rule-based NLP method (83.52%). The features of APHE and PDPH were the top two essential features for the liver cancer diagnosis. CONCLUSIONS We proposed a BERT-based deep learning method for diagnosis evidence extraction based on clinical knowledge. With the recognized features of APHE and PDPH, the liver cancer diagnosis could get a high performance, which was further increased by combining with the radiological features obtained by the traditional rule-based NLP method. The BERT-BiLSTM-CRF had achieved the state-of-the-art performance in this study, which could be extended to other kinds of Chinese clinical texts. CLINICALTRIAL None

scholarly journals Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1678
Author(s):  
Giovanni Bortolan ◽  
Ivaylo Christov ◽  
Iana Simova
Keyword(s):  
Deep Learning ◽  
Second Phase ◽  
Learning Method ◽  
Rule Based ◽  
Time Frequency ◽  
Ecg Signals ◽  
Cpu Time ◽  
Learning Procedure ◽  
Validation Score ◽  
Wavelet Transform Analysis

The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

A Deep Learning Method for Frame Selection in Videos for Structure from Motion Pipelines

2021 ◽  
Author(s):  
Francesco Banterle ◽  
Rui Gong ◽  
Massimiliano Corsini ◽  
Fabio Ganovelli ◽  
Luc Van Gool ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure From Motion ◽  
Learning Method ◽  
Frame Selection

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

Sign in / Sign up

Export Citation Format

Share Document