scholarly journals Self-Attention Networks and Adaptive Support Vector Machine for Aspect-Level Sentiment Classification

2022 ◽  
Author(s):  
Meizhan Liu ◽  
Fengyu Zhou ◽  
JiaKai He ◽  
Ke Chen ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Semantic Space ◽  
Sentiment Classification ◽  
Classification Model ◽  
Support Vector ◽  
Attention Networks ◽  
Semantic Relationships ◽  
Dimension Space ◽  
Two Stages

Abstract Aspect-level sentiment classification aims to integrating the context to predict the sentiment polarity of aspect-specific in a text, which has been quite useful and popular, e.g. opinion survey and products’ recommending in e-commerce. Many recent studies exploit a Long Short-Term Memory (LSTM) networks to perform aspect-level sentiment classification, but the limitation of long-term dependencies is not solved well, so that the semantic correlations between each two words of the text are ignored. In addition, traditional classification model adopts SoftMax function based on probability statistics as classifier, but ignores the words’ features in the semantic space. Support Vector Machine (SVM) can fully use the information of characteristics and it is appropriate to make classification in the high dimension space, however which just considers the maximum distance between different classes and ignores the similarities between different features of the same classes. To address these defects, we propose the two-stages novel architecture named Self Attention Networks and Adaptive SVM (SAN-ASVM) for aspect-level sentiment classification. In the first-stage, in order to overcome the long-term dependencies, Multi-Heads Self Attention (MHSA) mechanism is applied to extract the semantic relationships between each two words, furthermore 1-hop attention mechanism is designed to pay more attention on some important words related to aspect-specific. In the second-stage, ASVM is designed to substitute the SoftMax function to perform sentiment classification, which can effectively make multi-classifications in high dimensional space. Extensive experiments on SemEval2014, SemEval2016 and Twitter datasets are conducted, compared experiments prove that SAN-ASVM model can obtains better performance.

scholarly journals FETAL TYPE CLASSIFICATION BASED ON FETAL ELECTROCARDIOGRAPH IN NON-STRESS TESTS

2021 ◽  
pp. 2140052
Author(s):  
JIANLI LIU ◽  
YIMIN YANG ◽  
SONG ZHANG ◽  
XUWEN LI ◽  
LIN YANG ◽  
...  
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Classification Model ◽  
Support Vector ◽  
Stress Tests ◽  
Nonlinear Parameters ◽  
Design Algorithm ◽  
Long Time

Electronic fetal heart rate (FHR) monitoring is a technical means to evaluate the state of the fetus in the uterus by monitoring FHR. The main purpose is to detect intrauterine hypoxia and take corresponding medical measures timely. Because the fetus sleeps quietly for up to 1 hour sometimes, ultrasound Doppler is not easy to continuously detect for a long time. The electronic fetal monitor obtains the fetal heart rate, which not only improves the accuracy and comfort, but also the convenient implementation of long-term monitoring. It is beneficial to reduce perinatal fetal morbidity and mortality. This study used maternal–fetal Holter monitor which is based on the technology of fetal electrocardiograph (FECG) to collect the FHR, and then design algorithm to extract the baseline FHR, acceleration, variation, sleep-wake cycle and nonlinear parameters. There were significant differences in the 22 parameters between the normal and the suspicious group. Using the 22 characteristic parameters, the support vector machine was used to classify the normal and the suspected group of fetuses. 80% of the data was used to train a classification model. The remaining 20% of the data was used as a test set and its accuracy reached 93.75%.

scholarly journals Fruit Classification Model Based on Residual Filtering Network for Smart Community Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yulin Chen ◽  
Hailing Sun ◽  
Guofu Zhou ◽  
Bao Peng
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Smart Cities ◽  
Rapid Development ◽  
Classification Model ◽  
Support Vector ◽  
Artificial Intelligence Technology ◽  
Smart Community ◽  
Residual Block ◽  
Two Stages

With the rapid development of computer vision and robot technology, smart community robots based on artificial intelligence technology have been widely used in smart cities. Considering the process of feature extraction in fruit classification is very complicated. And manual feature extraction has low reliability and high randomness. Therefore, a method of residual filtering network (RFN) and support vector machine (SVM) for fruit classification is proposed in this paper. The classification of fruits includes two stages. In the first stage, RFN is used to extract features. The network consists of Gabor filter and residual block. In the second stage, SVM is used to classify fruit features extracted by RFN. In addition, a performance estimate for the training process carried out by the K -fold cross-validation method. The performance of this method is assessed with the accuracy, recall, F1 score, and precision. The accuracy of this method on the Fruits-360 dataset is 99.955%. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed method over existing systems on fruit classification.

SVM Ensemble-based Noise Detection Method for Image Denoising

2020 ◽  
Vol 13 (8) ◽  
pp. 1153-1165
Author(s):  
Xiaofen Jia ◽  
Chen Wang ◽  
Yongcun Guo ◽  
Baiting Zhao ◽  
Yourui Huang
Keyword(s):  
Mean Value ◽  
Classification Model ◽  
Support Vector ◽  
Noise Detection ◽  
Denoising Method ◽  
Noise Point ◽  
Noise Classification ◽  
Sharp Edges ◽  
Two Stages ◽  
Denoised Image

Background: To preserve sharp edges and image details while removing noise, this paper presents a denoising method based on Support Vector Machine (SVM) ensemble for detecting noise. Methods: The proposed method ISVM can be divided into two stages: noise detection and noise recovery. In the first stage, local binary features and weighted difference features are extracted as input features vector of ISVM, and multiple sub-SVM classifiers are integrated to form the noise classification model of ISVM by iteratively updating the sample weight. The pixels are divided into noise points and signal points. In the noise recovery stage, according to the classification results of the previous stage, only the gray value of the noise point is replaced, and the replacement value is the weighted mean value with the reciprocal of the quadratic square of the distance as the weight. Results: Finally, the replacement value at the noise point and the original pixel value of the signal point are reconstructed to get the denoised image. Conclusion: The experiments demonstrate that ISVM can achieve a noise detection rate of up to 99.68%. ISVM is highly effective in the denoising task, produces a visually pleasing denoised image with clear edge information, and offers remarkable improvement compared to that of the BPDF and DAMF.

scholarly journals A rolling bearing fault diagnosis method based on LSSVM

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401989956
Author(s):  
Xuejin Gao ◽  
Hongfei Wei ◽  
Tianyao Li ◽  
Guanglu Yang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Kernel Function ◽  
Dimensional Space ◽  
Rolling Bearing ◽  
Classification Model ◽  
Support Vector ◽  
Bearing Fault ◽  
Diagnosis Method

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

Multi-View Modeling Method for Functional MRI Images

2021 ◽  
Vol 11 (2) ◽  
pp. 432-436
Author(s):  
Jinlong Zhu ◽  
Xiujian Hu ◽  
Chao Zhang ◽  
Guanglei Sheng
Keyword(s):  
Dimensional Space ◽  
Feature Learning ◽  
Classification Performance ◽  
Mapping Method ◽  
Classification Model ◽  
Fmri Data ◽  
Support Vector ◽  
Learning Framework ◽  
Svm Algorithm ◽  
Aided Diagnosis

This paper proposes a new unsupervised fuzzy feature mapping method based on fMRI data and combines it with multi-view support vector machine to construct a classification model for computer-aided diagnosis of autism. Firstly, a multi-output TSK fuzzy system is adopted to map the original feature data to the linear separable high-dimensional space. Then a manifold regularization learning framework is introduced, and a new method of unsupervised fuzzy feature learning is proposed. Finally, a multi-view SVM algorithm is used for classification tasks. The experimental results show that the method in this paper can effectively extract important features from the fMRI data in the resting state and improve the model's interpretability on the premise of ensuring the superior and stable classification performance of the model.

Non-Linear Dynamics of Cardiovascular Variability Signals

1994 ◽  
Vol 33 (01) ◽  
pp. 81-84 ◽  
Author(s):  
S. Cerutti ◽  
S. Guzzetti ◽  
R. Parola ◽  
M.G. Signorini
Keyword(s):  
Dimensional Space ◽  
Severe Heart Failure ◽  
Parametric Models ◽  
Deterministic Systems ◽  
Finite Dimensional ◽  
Return Maps ◽  
Pathological Conditions ◽  
Non Linear ◽  
Space State

Abstract:Long-term regulation of beat-to-beat variability involves several different kinds of controls. A linear approach performed by parametric models enhances the short-term regulation of the autonomic nervous system. Some non-linear long-term regulation can be assessed by the chaotic deterministic approach applied to the beat-to-beat variability of the discrete RR-interval series, extracted from the ECG. For chaotic deterministic systems, trajectories of the state vector describe a strange attractor characterized by a fractal of dimension D. Signals are supposed to be generated by a deterministic and finite dimensional but non-linear dynamic system with trajectories in a multi-dimensional space-state. We estimated the fractal dimension through the Grassberger and Procaccia algorithm and Self-Similarity approaches of the 24-h heart-rate variability (HRV) signal in different physiological and pathological conditions such as severe heart failure, or after heart transplantation. State-space representations through Return Maps are also obtained. Differences between physiological and pathological cases have been assessed and generally a decrease in the system complexity is correlated to pathological conditions.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Chun Qiu ◽  
Sai Li ◽  
Shenghui Yang ◽  
Lin Wang ◽  
Aihui Zeng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Expression Profiles ◽  
Independent Set ◽  
Classification Model ◽  
Support Vector ◽  
Feature Subset ◽  
Hub Genes ◽  
Effective Prevention ◽  
Key Genes ◽  
Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

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