The Impact of Feature Representation to the Biclustering of Symptoms-Herbs in TCM

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.

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Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

PeerJ ◽

10.7717/peerj.7731 ◽

2019 ◽

Vol 7 ◽

pp. e7731 ◽

Cited By ~ 7

Author(s):

Tao Wang ◽

Changhua Lu ◽

Guohao Shen ◽

Feng Hong

Keyword(s):

Neural Network ◽

Sleep Apnea ◽

Convolutional Neural Network ◽

Single Channel ◽

Feature Representation ◽

Training Data ◽

Ecg Signal ◽

Automatic Feature Extraction ◽

Adjacent Segments ◽

The Impact

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

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Assessing Regression-Based Sentiment Analysis Techniques in Financial Texts

10.5753/eniac.2019.9329 ◽

2019 ◽

Cited By ~ 1

Author(s):

Taynan Ferreira ◽

Francisco Paiva ◽

Roberto Silva ◽

Angel Paula ◽

Anna Costa ◽

...

Keyword(s):

Sentiment Analysis ◽

Feature Representation ◽

Support Vector ◽

Data Set ◽

Feature Representations ◽

Textual Data ◽

Enormous Amount ◽

Financial Domain ◽

Classification Tasks ◽

The Impact

Sentiment analysis (SA) is increasing its importance due to the enormous amount of opinionated textual data available today. Most of the researches have investigated different models, feature representation and hyperparameters in SA classification tasks. However, few studies were conducted to evaluate the impact of these features on regression SA tasks. In this paper, we conduct such assessment on a financial domain data set by investigating different feature representations and hyperparameters in two important models -- Support Vector Regression (SVR) and Convolution Neural Networks (CNN). We conclude presenting the most relevant feature representations and hyperparameters and how they impact outcomes on a regression SA task.

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TargetMM: accurate missense mutation prediction by utilizing local and global sequence information with classifier ensemble

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666201204140438 ◽

2020 ◽

Vol 23 ◽

Author(s):

Fang Ge ◽

Jun Hu ◽

Yi-Heng Zhu ◽

Muhammad Arif ◽

Dong-Jun Yu

Keyword(s):

Missense Mutation ◽

Prediction Models ◽

Feature Representation ◽

Classifier Ensemble ◽

Sequence Information ◽

Mutation Site ◽

Feature Extracting ◽

Independent Test ◽

Benchmark Datasets ◽

The Impact

Aim and Objective: Missense mutation (MM) may lead to various human diseases by disabling proteins. Accurate prediction of MM is important and challenging for both protein function annotation and drug design. Although several computational methods yielded acceptable success rates, there is still room for further enhancing the prediction performance of MM. Materials and Methods: In the present study, we designed a new feature extracting method, which considers the impact degree of residues in the microenvironment range to the mutation site. Stringent cross-validation and independent test on benchmark datasets were performed to evaluate the efficacy of the proposed feature extracting method. Furthermore, three heterogeneous prediction models were trained and then ensembled for the final prediction. Results: By combining the feature representation method and classifier ensemble technique, we reported a novel MM predictor called TargetMM for identifying the pathogenic mutations from the neutral ones. Conclusion: Comparison outcomes based on statistical evaluation demonstrate that TargetMM outperforms the prior advanced methods on the independent test data. The source code and benchmark datasets of TargetMM are freely available at https://github.com/sera616/TargetMM.git for academic use.

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Study of Precipitation Forecast Based on Deep Belief Networks

Algorithms ◽

10.3390/a11090132 ◽

2018 ◽

Vol 11 (9) ◽

pp. 132 ◽

Cited By ~ 4

Author(s):

Jinglin Du ◽

Yayun Liu ◽

Zhijun Liu

Keyword(s):

Weather Forecasting ◽

Human Life ◽

Current Trend ◽

Predictive Performance ◽

Feature Representation ◽

Precipitation Forecast ◽

Semantic Features ◽

Belief Networks ◽

Deep Belief Networks ◽

The Impact

Due to the impact of weather forecasting on global human life, and to better reflect the current trend of weather changes, it is necessary to conduct research about the prediction of precipitation and provide timely and complete precipitation information for climate prediction and early warning decisions to avoid serious meteorological disasters. For the precipitation prediction problem in the era of climate big data, we propose a new method based on deep learning. In this paper, we will apply deep belief networks in weather precipitation forecasting. Deep belief networks transform the feature representation of data in the original space into a new feature space, with semantic features to improve the predictive performance. The experimental results show, compared with other forecasting methods, the feasibility of deep belief networks in the field of weather forecasting.

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Attention neural collaboration filtering based on GRU for recommender systems

Complex & Intelligent Systems ◽

10.1007/s40747-021-00274-4 ◽

2021 ◽

Author(s):

Hongbin Xia ◽

Yang Luo ◽

Yuan Liu

Keyword(s):

Deep Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Auxiliary Information ◽

User Preferences ◽

Feature Representation ◽

Long Distance ◽

Data Set ◽

Filtering Method ◽

The Impact

AbstractThe collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

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Video-Based Person Re-Identification With Unregulated Sequences

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2020040104 ◽

2020 ◽

Vol 12 (2) ◽

pp. 59-76

Author(s):

Wenjun Huang ◽

Chao Liang ◽

Chunxia Xiao ◽

Zhen Han

Keyword(s):

Video Sequence ◽

Feature Representation ◽

Original Video ◽

Sequence Detection ◽

Video Images ◽

Discriminative Feature ◽

Refinement Method ◽

Public Datasets ◽

The Impact ◽

Time Information

Video-based person re-identification (re-id) has recently attracted widespread attentions because extra space-time information and more appearance cues in videos can be used to improve the performance of image-based person re-id. Most existing approaches equally treat person video images, ignoring their individual discrepancy. However, in real scenarios, captured images are usually contaminated by various noises, especially occlusions, resulting in a series of unregulated sequences. Through investigating the impact of unregulated sequences to feature representation of video-based person re-id, the authors find a remarkable promotion by eliminating noisy sub sequences. Based on this interesting finding, an adaptive unregulated sub sequence detection and refinement method is proposed to purify original video sequence and obtain a more effective and discriminative feature representation for video-based person re-id. Experimental results on two public datasets demonstrate that the proposed method outperforms the state-of-the-art work.

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Cascaded Cross-Layer Fusion Network for Pedestrian Detection

Mathematics ◽

10.3390/math10010139 ◽

2022 ◽

Vol 10 (1) ◽

pp. 139

Author(s):

Zhifeng Ding ◽

Zichen Gu ◽

Yanpeng Sun ◽

Xinguang Xiang

Keyword(s):

Feature Fusion ◽

Pedestrian Detection ◽

Detection Performance ◽

Feature Representation ◽

Cross Layer ◽

Feature Maps ◽

Level Information ◽

High Level ◽

Center Map ◽

The Impact

The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets.

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DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images

Remote Sensing ◽

10.3390/rs13183755 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3755

Author(s):

Yongjie Guo ◽

Feng Wang ◽

Yuming Xiang ◽

Hongjian You

Keyword(s):

High Resolution ◽

Land Cover ◽

Spatial Information ◽

Land Cover Classification ◽

Feature Representation ◽

Semantic Gap ◽

Dual Gate ◽

Art Performance ◽

The Impact ◽

Very High

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction ability. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features; a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset.

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Assessment of Percussive Music Performances with Feature Learning

International Journal of Semantic Computing ◽

10.1142/s1793351x18400147 ◽

2018 ◽

Vol 12 (03) ◽

pp. 315-333 ◽

Cited By ~ 1

Author(s):

Chih-Wei Wu ◽

Alexander Lerch

Keyword(s):

Sparse Coding ◽

Music Performance ◽

Feature Learning ◽

Feature Representation ◽

Automatic Assessment ◽

Reliable Assessment ◽

Audio Recordings ◽

The Impact ◽

Learned Features

The automatic assessment of (student) music performance involves the characterization of the audio recordings and the modeling of human judgments. To build a computational model that provides a reliable assessment, the system must take into account various aspects of a performance including technical correctness and aesthetic standards. While some progress has been made in recent years, the search for an effective feature representation remains open-ended. In this study, we explore the possibility of using learned features from sparse coding. Specifically, we investigate three sets of features, namely a baseline set, a set of designed features, and a feature set learned with sparse coding. In addition, we compare the impact of two different input representations on the effectiveness of the learned features. The evaluation is performed on a dataset of annotated recordings of students playing snare exercises. The results imply the general viability of feature learning in the context of automatic assessment of music performances.

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