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摘要 ：
Objective. Motor imagery-based brain-computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.
关键词 ：
channel attention channel attention motor imagery motor imagery data augmentation data augmentation deep learning deep learning brain-computer interface brain-computer interface
引用：
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| GB/T 7714 | Chen, Jiaming , Yi, Weibo , Wang, Dan et al. FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification [J]. | JOURNAL OF NEURAL ENGINEERING , 2022 , 19 (1) . |
| MLA | Chen, Jiaming et al. "FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification" . | JOURNAL OF NEURAL ENGINEERING 19 . 1 (2022) . |
| APA | Chen, Jiaming , Yi, Weibo , Wang, Dan , Du, Jinlian , Fu, Lihua , Li, Tong . FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification . | JOURNAL OF NEURAL ENGINEERING , 2022 , 19 (1) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Image super-resolution reconstruction is a research hotspot in the field of computer vision. Traditional image super-resolution reconstruction methods based on deep learning mostly up-sample low-resolution images ignoring categories and instances, which will cause some problems such as unrealistic texture in the reconstructed images or sawtooth phenomenon on the edge of instance. In this manuscript, we propose an image super-resolution reconstruction method based on instance spatial feature modulation and feedback mechanism. First, the prior knowledge of instance spatial features is introduced in the reconstruction process. Instance spatial features of low-resolution images are extracted to modulate super-resolution reconstruction features. Then, based on the feedback mechanism, the modulated low-resolution image features are iteratively optimized for the reconstruction results, so that the model can finally learn instance-level reconstruction ability. Experiments on COCO-2017 show that, compared with traditional deep learning-based image super-resolution reconstruction methods, the proposed method can obtain better image reconstruction results, and the reconstructed images have more realistic instance textures.
关键词 ：
Feedback network Feedback network Back projection Back projection Instance spatial feature Instance spatial feature Modulator Modulator Super-resolution Super-resolution
引用：
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| GB/T 7714 | Fu, Lihua , Jiang, Hanxu , Wu, Huixian et al. Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism [J]. | APPLIED INTELLIGENCE , 2022 , 53 (1) : 601-615 . |
| MLA | Fu, Lihua et al. "Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism" . | APPLIED INTELLIGENCE 53 . 1 (2022) : 601-615 . |
| APA | Fu, Lihua , Jiang, Hanxu , Wu, Huixian , Yan, Shaoxing , Wang, Junxiang , Wang, Dan . Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism . | APPLIED INTELLIGENCE , 2022 , 53 (1) , 601-615 . |
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摘要 ：
Network security has emerged as a crucial universal issue that affects enterprises, governments, and individuals. The strategies utilized by the attackers are continuing to evolve, and therefore the rate of attacks targeting the network system has expanded dramatically. An Intrusion Detection System (IDS) is one of the significant defense solutions against sophisticated cyberattacks. However, the challenge of improving the accuracy, detection rate, and minimal false alarms of the IDS continues. This paper proposes a robust and effective intrusion detection framework based on the ensemble learning technique using eXtreme Gradient Boosting (XGBoost) and an embedded feature selection method. Further, the best uniform feature subset is extracted using the up-to-date real-world intrusion dataset Canadian Institute for Cybersecurity Intrusion Detection (CICIDS2017) for all attacks. The proposed IDS framework has successfully exceeded several evaluations on a big test dataset over both multi and binary classification. The achieved results are promising on various measurements with an accuracy overall, precision, detection rate, specificity, F-score, false-negative rate, false-positive rate, error rate, and The Area Under the Curve (AUC) scores of 99.86%, 99.69%, 99.75%, 99.69%, 99.72%, 0.17%, 0.2%, 0.14%, and 99.72 respectively for abnormal class. Moreover, the achieved results of multi-classification are also remarkable and impressively great on all performance metrics.
关键词 ：
xgboost algorithm xgboost algorithm ensemble learning ensemble learning intrusion detection intrusion detection Network security Network security features selection features selection
引用：
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| GB/T 7714 | Mokbal, Fawaz , Dan, Wang , Osman, Musa et al. An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique [J]. | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (2) : 237-248 . |
| MLA | Mokbal, Fawaz et al. "An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique" . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY 19 . 2 (2022) : 237-248 . |
| APA | Mokbal, Fawaz , Dan, Wang , Osman, Musa , Ping, Yang , Alsamhi, Saeed . An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (2) , 237-248 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The accumulation and explosive growth of the electronic medical records (EMRs) make the name entity recognition (NER) technologies become critical for the meaningful use of EMR data and then the practice of evidence-based medicine. The dominate NER approaches use the distributed representation of the words and characters to build deep learning-based NER models. However, for the task of biomedical named entity recognition, there are a large amount of complicated medical terminologies that are composed of multiple words. Splitting these terminologies to learn the word and character embeddings might cause semantic ambiguities. In this paper, we treat each medical terminology as a concept and propose a concept-enhanced named entity recognition model (CNER), where the features from three different granularities (i.e., concept, word, and character) are combined together for bio-NER. The extensive experiments are conducted on two real-world corpora: fully labeled corpus and partially labeled corpus. CNER achieves the highest F1 score (fully labeled corpus: precision = 88.23, recall = 88.29, and F1 = 88.26; partially labeled corpus: precision = 87.03, recall = 88.19, and F1 = 87.61) by outperforming the baseline CW-BLSTM-CRF approach for 0.58% and 1.15% respectively, which demonstrates the effectiveness of the proposed approach.
关键词 ：
Concept feature Concept feature Deep neural network (DNN) Deep neural network (DNN) Named entity recognition (NER) Named entity recognition (NER) Semantic information analysis Semantic information analysis
引用：
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| GB/T 7714 | Zhao, Qing , Wang, Dan , Li, Jianqiang et al. Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs [J]. | JOURNAL OF SUPERCOMPUTING , 2020 , 76 (8) : 6399-6420 . |
| MLA | Zhao, Qing et al. "Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs" . | JOURNAL OF SUPERCOMPUTING 76 . 8 (2020) : 6399-6420 . |
| APA | Zhao, Qing , Wang, Dan , Li, Jianqiang , Akhtar, Faheem . Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs . | JOURNAL OF SUPERCOMPUTING , 2020 , 76 (8) , 6399-6420 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
To improve the accuracy of named entity recognition and reduce the cost of manual labeling, this study proposes a weakly supervised named entity recognition method based on the recurrent neural network (RNN), which utilizes the widely existing ontology in the medical field as the supplemental source of knowledge. In other words, a named entity recognition model is constructed by extracting semantic concept representation from medical ontology and integrating it with word and character embedding. First, the continuous bag of words model is utilized to extract semantic representation, including concept and word embedding. Then, the character-enhanced word embedding model is used to extract character representation. Finally, the tag sequence of Chinese medical text is obtained using a deep learning model RNN in combination with semantic and character embedding. The results of a comparative experiment on a true dataset of medical text show that the performance improvement of our proposed method compared with that of traditional methods reaches 2.2% to 6.1%, which verifies the effectiveness of our proposed method. © 2020, Editorial Department of Journal of HEU. All right reserved.
关键词 ：
Embeddings Embeddings Information retrieval Information retrieval Semantics Semantics Ontology Ontology Recurrent neural networks Recurrent neural networks
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| GB/T 7714 | Zhao, Qing , Wang, Dan , Xu, Shushi et al. A weakly supervised Chinese medical named entity recognition method [J]. | Journal of Harbin Engineering University , 2020 , 41 (3) : 425-432 . |
| MLA | Zhao, Qing et al. "A weakly supervised Chinese medical named entity recognition method" . | Journal of Harbin Engineering University 41 . 3 (2020) : 425-432 . |
| APA | Zhao, Qing , Wang, Dan , Xu, Shushi , Zhang, Xiaotong , Wang, Xiaoxi . A weakly supervised Chinese medical named entity recognition method . | Journal of Harbin Engineering University , 2020 , 41 (3) , 425-432 . |
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摘要 ：
Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K -distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K -distance neighborhood with relative K -distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection.
关键词 ：
Canopy Canopy cluster cluster LOF LOF outlier detection outlier detection SOFM SOFM
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| GB/T 7714 | Yang, Ping , Wang, Dan , Wei, Zhuojun et al. An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm [J]. | IEEE ACCESS , 2019 , 7 : 115914-115925 . |
| MLA | Yang, Ping et al. "An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm" . | IEEE ACCESS 7 (2019) : 115914-115925 . |
| APA | Yang, Ping , Wang, Dan , Wei, Zhuojun , Dui, Xiaolin , Li, Tong . An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm . | IEEE ACCESS , 2019 , 7 , 115914-115925 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Permanent magnetic nanocrystalline Sm5Co19Hf0.4 alloy and Sm5Co19Hf0.4CNTs0.4 alloy with high room-temperature coercivity were prepared. The microstructure, crystal structure and magnetic properties were studied. The results show that the mixed doping of Hf elements and CNTs does not lead to phase decomposition of Ce5Co19 type structure, while it results in fine grains and uniform distribution of the microstructure. Energy Dispersive X-ray Spectrometry (EDX) analyses confirm that CNTs move into grain boundaries of the nanocrystalline Sm5Co19Hf0.4CNTs0.4 alloy, which can improve the coercivity of the nanocrystalline Sm5Co19 alloy for the grain boundary pinning effect. Rietyeld refinement show that Hf comes into the Sm vacancy, thus decreasing the lattice parameters and increasing the axial ratio c/a, which further enhance the magnetocrystalline anisotropy, and strengthen the coercivity of nanocrystalline alloy. The results of the study can promote the design of Sm-Co alloy with high magnetocrystalline anisotropy and intrinsic coercivity. © 2018, Northwest Institute for Nonferrous Metal Research. Published by Elsevier BV. All rights reserved.
关键词 ：
Atom occupancy; Crystal structure; Element doping; Intrinsic coercivity; Nanocrystalline Atom occupancy; Crystal structure; Element doping; Intrinsic coercivity; Nanocrystalline
引用：
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| GB/T 7714 | Wang, D. , Qiao, Y. , Liu, D. et al. Effect of Hf Elements and CNTs Alloy Mixed Doping on Structure and Magnetic Properties of Nanocrystalline Sm5Co19 Alloy [Hf, CNTs混合掺杂对纳米晶Sm5Co19合金结构和 磁性能的影响] [J]. | Rare Metal Materials and Engineering , 2018 , 47 (3) : 1001-1006 . |
| MLA | Wang, D. et al. "Effect of Hf Elements and CNTs Alloy Mixed Doping on Structure and Magnetic Properties of Nanocrystalline Sm5Co19 Alloy [Hf, CNTs混合掺杂对纳米晶Sm5Co19合金结构和 磁性能的影响]" . | Rare Metal Materials and Engineering 47 . 3 (2018) : 1001-1006 . |
| APA | Wang, D. , Qiao, Y. , Liu, D. , Hua, G. , Wang, H. , Liu, X. et al. Effect of Hf Elements and CNTs Alloy Mixed Doping on Structure and Magnetic Properties of Nanocrystalline Sm5Co19 Alloy [Hf, CNTs混合掺杂对纳米晶Sm5Co19合金结构和 磁性能的影响] . | Rare Metal Materials and Engineering , 2018 , 47 (3) , 1001-1006 . |
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摘要 ：
An increasing number of reviews from the customers have been available online. Thus, sentiment classification for such reviews has attracted more and more attention from the natural language processing (NLP) community. Related literature has shown that sentiment analysis can benefit from Deep Belief Networks (DBN). However, determining the structure of the deep network and improving its performance still remains an open question. In this paper, we propose a sophisticated algorithm based on fuzzy mathematics and genetic algorithm, called evolutionary fuzzy deep belief networks with incremental rules (EFDBNI). We evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that EFDBNI brings out significant improvement over existing methods.
关键词 ：
Deep learning Deep learning Fuzzy set Fuzzy set Genetic algorithm Genetic algorithm Semi-supervised Semi-supervised Sentiment classification Sentiment classification
引用：
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| GB/T 7714 | Yang, Ping , Wang, Dan , Du, Xiao-Lin et al. Evolutionary DBN for the Customers' Sentiment Classification with Incremental Rules [C] . 2018 : 119-134 . |
| MLA | Yang, Ping et al. "Evolutionary DBN for the Customers' Sentiment Classification with Incremental Rules" . (2018) : 119-134 . |
| APA | Yang, Ping , Wang, Dan , Du, Xiao-Lin , Wang, Meng . Evolutionary DBN for the Customers' Sentiment Classification with Incremental Rules . (2018) : 119-134 . |
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摘要 ：
[Context and motivation] In the increasingly competitive software market, it is essential for software companies to have a comprehensive understanding of development progress and user preferences of their corresponding application domain. [Question/problem] However, given the huge number of existing software applications, it is impossible to gain such insights via manual inspection. [Principal ideas/results] In this paper, we present a research preview of automatic user preferences elicitation approach. Specifically, our approach first clusters software applications into different categories based on their descriptions, and then identifies features of each category. We then link such features to corresponding user reviews and automatically classify sentiments of each review In order to understand user preferences over such feature In addition, we have carefully planned evaluations that will be carried out to further polish our work. [Contributions] Our proposal aims to help software companies to identify features of applications in a particular domain, as well as user preferences with regard to those features. We argue such analysis is especially important for startup companies that have few knowledge about the domain.
关键词 ：
Machine learning Machine learning Natural language processing Natural language processing Sentiment analysis Sentiment analysis Topic modeling Topic modeling User preferences User preferences
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| GB/T 7714 | Li, Tong , Zhang, Fan , Wang, Dan . Automatic User Preferences Elicitation: A Data-Driven Approach [C] . 2018 : 324-331 . |
| MLA | Li, Tong et al. "Automatic User Preferences Elicitation: A Data-Driven Approach" . (2018) : 324-331 . |
| APA | Li, Tong , Zhang, Fan , Wang, Dan . Automatic User Preferences Elicitation: A Data-Driven Approach . (2018) : 324-331 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Objective: The objective of this study was to propose a graph-based semantic search approach by addressing the inherent complexity and ambiguity of medical terminology in queries and clinical text for enhanced medical information retrieval. Methods: The supportive use of a medical domain ontology exploits the light-weight semantics discovered from queries and documents for enhanced document ranking. First, the implicit information regarding concepts and the relations between them is discovered in the documents and queries and is used to evaluate the relevance of the query-document; then, the semantic linkages between concepts distributed in target documents and reference documents are built and used to score the document's popularity; finally, the above two evaluations are integrated to produce the final ranking list for document ranking. Results: Empirical experiments are conducted on two different datasets. The results demonstrate that the proposed graph-based approach significantly outperforms the baselines. For example, the average performance improvement on two datasets of the best variant of GSRM compared to the best baseline achieve 7.2% and 7.9% in terms of P@20 and NDCG@20, respectively, which illustrates the effectiveness of the proposed approach.
关键词 ：
Medical search Medical search Semantic information retrieval Semantic information retrieval Electronic medical records Electronic medical records Document ranking Document ranking
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| GB/T 7714 | Zhao, Qing , Kang, Yangyang , Li, Jianqiang et al. Exploiting the semantic graph for the representation and retrieval of medical documents [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2018 , 101 : 39-50 . |
| MLA | Zhao, Qing et al. "Exploiting the semantic graph for the representation and retrieval of medical documents" . | COMPUTERS IN BIOLOGY AND MEDICINE 101 (2018) : 39-50 . |
| APA | Zhao, Qing , Kang, Yangyang , Li, Jianqiang , Wang, Dan . Exploiting the semantic graph for the representation and retrieval of medical documents . | COMPUTERS IN BIOLOGY AND MEDICINE , 2018 , 101 , 39-50 . |
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