scholarly journals Ensemble Neural Relation Extraction with Adaptive Boosting

2018 ◽  
Author(s):  
Dongdong Yang ◽  
Senzhang Wang ◽  
Zhoujun Li
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Noisy Data ◽  
Relation Extraction ◽  
Extraction Methods ◽  
Superior Performance ◽  
Semantic Meaning ◽  
Adaptive Boosting ◽  
Proposed Model ◽  
Ensemble Neural Networks

Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equally to the semantic meaning of the sentence. Next via adaptive boosting, we build strategically several such neural classifiers. By ensembling multiple such LSTM classifiers with adaptive boosting, we could build a more effective and robust joint ensemble neural networks based relation extractor. Experiment results on real dataset demonstrate the superior performance of the proposed model, improving F1-score by about 8% compared to the state-of-the-art models.

scholarly journals Enhancing the performance of cancer text classification model based on cancer hallmarks

2021 ◽  
Vol 10 (2) ◽  
pp. 316
Author(s):  
Noha Ali ◽  
Ahmed H. AbuEl-Atta ◽  
Hala H. Zayed
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Classification Model ◽  
Biomedical Text ◽  
Cancer Hallmarks ◽  
Embedding Technique ◽  
Proposed Model ◽  
Biomedical Text Classification

<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.</span></span>

WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-18
Author(s):  
Qi Xin ◽  
Shaohao Hu ◽  
Shuaiqi Liu ◽  
Ling Zhao ◽  
Shuihua Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Traumatic Injury ◽  
Recurrence Plot ◽  
Superior Performance ◽  
Eeg Classification ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block . The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

scholarly journals Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

2020 ◽  
Vol 34 (05) ◽  
pp. 9612-9619
Author(s):  
Zhao Zhang ◽  
Fuzhen Zhuang ◽  
Hengshu Zhu ◽  
Zhiping Shi ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Values ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Knowledge Graph ◽  
Incomplete Knowledge ◽  
Neighborhood Information ◽  
Local Neighborhood ◽  
Proposed Model ◽  
Knowledge Graphs

The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.

scholarly journals Robust place recognition based on salient landmarks screening and convolutional neural network features

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096696
Author(s):  
Jie Niu ◽  
Kun Qian
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Environmental Modeling ◽  
Superior Performance ◽  
Similarity Measurement ◽  
Natural Environments ◽  
Place Recognition ◽  
Average Precision ◽  
Specific Object

In this work, we propose a robust place recognition measurement in natural environments based on salient landmark screening and convolutional neural network (CNN) features. First, the salient objects in the image are segmented as candidate landmarks. Then, a category screening network is designed to remove specific object types that are not suitable for environmental modeling. Finally, a three-layer CNN is used to get highly representative features of the salient landmarks. In the similarity measurement, a Siamese network is chosen to calculate the similarity between images. Experiments were conducted on three challenging benchmark place recognition datasets and superior performance was achieved compared to other state-of-the-art methods, including FABMAP, SeqSLAM, SeqCNNSLAM, and PlaceCNN. Our method obtains the best results on the precision–recall curves, and the average precision reaches 78.43%, which is the best of the comparison methods. This demonstrates that the CNN features on the screened salient landmarks can be against a strong viewpoint and condition variations.

scholarly journals Graph Dilated Network with Rejection Mechanism

2020 ◽  
Vol 10 (7) ◽  
pp. 2421
Author(s):  
Bencheng Yan ◽  
Chaokun Wang ◽  
Gaoyang Guo
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Great Success ◽  
Convolution Kernel ◽  
Graph Structure ◽  
Proposed Model ◽  
Rejection Mechanism ◽  
Laplacian Smoothing ◽  
Graph Neural Networks ◽  
High Level

Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing. However, most of GNNs fall into the over-smoothing problem, i.e., when the model goes deeper, the learned representations become indistinguishable. This reflects the inability of the current GNNs to explore the global graph structure. In this paper, we propose a novel graph neural network to address this problem. A rejection mechanism is designed to address the over-smoothing problem, and a dilated graph convolution kernel is presented to capture the high-level graph structure. A number of experimental results demonstrate that the proposed model outperforms the state-of-the-art GNNs, and can effectively overcome the over-smoothing problem.

Robust and discriminative dictionary learning for face recognition

2018 ◽  
Vol 16 (02) ◽  
pp. 1840004 ◽  
Author(s):  
Guojun Lin ◽  
Meng Yang ◽  
Linlin Shen ◽  
Mingzhong Yang ◽  
Mei Xie
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Dictionary Learning ◽  
State Of The Art ◽  
Face Image ◽  
Superior Performance ◽  
Face Images ◽  
Proposed Model ◽  
Classification Capability ◽  
Discriminative Dictionary Learning

For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don’t cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

scholarly journals A Novel CapsNet based Image Reconstruction and Regression Analysis

2020 ◽  
Vol 2 (3) ◽  
pp. 156-164 ◽  
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Network Models ◽  
Superior Performance ◽  
Neural Network Models ◽  
Innovative Method ◽  
Proposed Model ◽  
Retrieval Efficiency ◽  
High Level ◽  
Better Than

In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced while handling high level features. Considering these issues in image classification and regression, the proposed model is designed with capsule network as an innovative method which is suitable to handle high level features. The experimental results of the proposed model are compared with conventional neural network models such as BPNN and CNN to validate the superior performance. The proposed model achieves better retrieval efficiency of 95.4% which is much better than other neural network models.

scholarly journals A modified Bayesian Convolutional Neural Network for Breast Histopathology Image Classification and Uncertainty Quantification

2020 ◽  
Author(s):  
Pushkar Khairnar ◽  
Ponkrshnan Thiagarajan ◽  
Susanta Ghosh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Uncertainty Quantification ◽  
State Of The Art ◽  
False Negative ◽  
Activation Function ◽  
Ensemble Averaging ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Histopathological Images

Convolutional neural network (CNN) based classification models have been successfully used on histopathological images for the detection of diseases. Despite its success, CNN may yield erroneous or overfitted results when the data is not sufficiently large or is biased. To overcome these limitations of CNN and to provide uncertainty quantification Bayesian CNN is recently proposed. However, we show that Bayesian-CNN still suffers from inaccuracies, especially in negative predictions. In the present work, we extend the Bayesian-CNN to improve accuracy and the rate of convergence. The proposed model is called modified Bayesian-CNN. The novelty of the proposed model lies in an adaptive activation function that contains a learnable parameter for each of the neurons. This adaptive activation function dynamically changes the loss function thereby providing faster convergence and better accuracy. The uncertainties associated with the predictions are obtained since the model learns a probability distribution on the network parameters. It reduces overfitting through an ensemble averaging over networks, which in turn improves accuracy on the unknown data. The proposed model demonstrates significant improvement by nearly eliminating overfitting and remarkably reducing (about 38%) the number of false-negative predictions. We found that the proposed model predicts higher uncertainty for images having features of both the classes. The uncertainty in the predictions of individual images can be used to decide when further human-expert intervention is needed. These findings have the potential to advance the state-of-the-art machine learning-based automatic classification for histopathological images.

scholarly journals Towards relation extraction from Arabic text: a review

2019 ◽  
Vol 5 (5) ◽  
pp. 212-215
Author(s):  
Abeer AlArfaj
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Relation Extraction ◽  
Arabic Language ◽  
Extraction Methods ◽  
The State ◽  
Semantic Relations ◽  
Arabic Text ◽  
Taxonomic Relation

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

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