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Deep Learning and Its Applications in Anomaly Detection and Natural Language Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 9150

Special Issue Editors

Prof. Dr. Jiang Zhong

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: big data analysis and mining; natural language processing; cloud network integration; network security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Xie

E-Mail Website
Guest Editor
Colleage of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA 30144, USA
Interests: natural language processing; data mining; computational intelligence
Dr. Weitong Chen

E-Mail Website
Guest Editor
Australian Institute for Machine Learning (AIML), School of Computer and Mathematical Science, Faculty of Sciences, Engineering & Technology, The University of Adelaide, SA5005 Adelaide, Austria
Interests: machine learning; deep learning; time series data analysis; weakly supervised learning
Prof. Dr. Xue Li

E-Mail Website
Guest Editor
School of Information Technology and Electronic Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
Interests: big data; natural language processing; knowledge graph; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural Language Processing (NLP) and anomaly detection are key branches of deep learning. NLP focuses on enabling machines to understand the human language. Anomaly detection aims to identify the unexpected items or events in data sets, and has been widely applied in fraud detection, network intrusion detection, and cancer detection. Recently, a lot of effort in NLP and anomaly detection has achieved remarkable success in tasks, such as question answering, machine translation, smart assistants, and fraud detection. Pre-trained language models, such as BERT, GPT-3, and ChatGPT, have been widely applied in NLP and anomaly detection. They are also crucial to a wide range of other research topics, for biomedical information processing, knowledge graph, and multimodal intelligence. However, numerous relevant unsolved theoretical and technological problems await further research. We welcome original research articles reporting the development of novel ideas, models, and algorithms on deep learning, and their application in anomaly detection and natural language processing.

This Special Issue welcomes submissions covering a wide range of topic areas (though not limited to these):

  • Deep learning/Machine learning;
  • Anomaly detection;
  • Named entity recognition;
  • Relation extraction;
  • Question answering;
  • Machine translation;
  • knowledge graph;
  • Disambiguation;
  • Summarization.

Prof. Dr. Jiang Zhong
Prof. Dr. Ying Xie
Dr. Weitong Chen
Prof. Dr. Xue Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • anomaly detection
  • natural language processing
  • named entity recognition
  • relation extraction
  • knowledge graph

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Published Papers (4 papers)

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Research

16 pages, 4649 KiB  
Article
Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network
by Zhuoran Chen, Yujin Zhang, Yongqi Wang, Jin Tian and Fei Wu
Appl. Sci. 2023, 13(16), 9196; https://doi.org/10.3390/app13169196 - 12 Aug 2023
Viewed by 1796
Abstract
Deep learning has injected a new sense of vitality into the field of image inpainting, allowing for the creation of more realistic inpainted images that are difficult to distinguish from the original ones. However, this also means that the malicious use of image [...] Read more.
Deep learning has injected a new sense of vitality into the field of image inpainting, allowing for the creation of more realistic inpainted images that are difficult to distinguish from the original ones. However, this also means that the malicious use of image inpainting technology to tamper with images could lead to more serious consequences. In this paper, we use an attention-based feature pyramid network (AFPN) to locate the inpainting traces left by deep learning. AFPN employs a feature pyramid to extract low- and high-level features of inpainted images. It further utilizes a multi-scale convolution attention (MSCA) module to optimize the high-level feature maps. The optimized high-level feature map is then fused with the low-level feature map to detect inpainted regions. Additionally, we introduce a fusion loss function to improve the training effectiveness. The experimental results show that AFPN exhibits remarkable precision in deep inpainting forensics and effectively resists JPEG compression and additive noise attacks. Full article
(This article belongs to the Special Issue Deep Learning and Its Applications in Anomaly Detection and Natural Language Processing)
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19 pages, 4885 KiB  
Article
SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics
by Zhe Ren, Xizhong Qin and Wensheng Ran
Appl. Sci. 2023, 13(15), 8609; https://doi.org/10.3390/app13158609 - 26 Jul 2023
Cited by 1 | Viewed by 1475
Abstract
Few-shot named entity recognition requires sufficient prior knowledge to transfer valuable knowledge to the target domain with only a few labeled examples. Existing Chinese few-shot named entity recognition methods suffer from inadequate prior knowledge and limitations in feature representation. In this paper, we [...] Read more.
Few-shot named entity recognition requires sufficient prior knowledge to transfer valuable knowledge to the target domain with only a few labeled examples. Existing Chinese few-shot named entity recognition methods suffer from inadequate prior knowledge and limitations in feature representation. In this paper, we utilize enhanced Span and Label semantic representations for Chinese few-shot Named Entity Recognition (SLNER) to address the problem. Specifically, SLNER utilizes two encoders. One encoder is used to encode the text and its spans, and we employ the biaffine attention mechanism and self-attention to obtain enhanced span representations. This approach fully leverages the internal composition of entity mentions, leading to more accurate feature representations. The other encoder encodes the full label names to obtain label representations. Label names are broad representations of specific entity categories and share similar semantic meanings with entities. This similarity allows label names to offer valuable prior knowledge in few-shot scenarios. Finally, our model learns to match span representations with label representations. We conducted extensive experiments on three sampling benchmark Chinese datasets and a self-built food safety risk domain dataset. The experimental results show that our model outperforms the F1 scores of 0.20–6.57% of previous state-of-the-art methods in few-shot settings. Full article
(This article belongs to the Special Issue Deep Learning and Its Applications in Anomaly Detection and Natural Language Processing)
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20 pages, 2698 KiB  
Article
LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
by Shuxian Liu, Le Deng, Huan Xu and Wei Wang
Appl. Sci. 2023, 13(13), 7739; https://doi.org/10.3390/app13137739 - 30 Jun 2023
Cited by 1 | Viewed by 2482
Abstract
The log data generated during operation of a software system contain information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most existing log anomaly detection methods are specific to a particular system, have cold-start [...] Read more.
The log data generated during operation of a software system contain information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most existing log anomaly detection methods are specific to a particular system, have cold-start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pretrained models and domain adaptation, which uses the pretraining model BERT to learn the semantic information of logs. This method can solve problems caused by the multiple meaning of words and log statement updates. The distance to determine anomalies in LogBD is constructed on the basis of domain adaptation, using TCNs to extract common features of different system logs and mapping them to the same hypersphere space. Lastly, experiments were conducted on two publicly available datasets to evaluate the method. The experimental results showed that the method can better solve the log instability problem and exhibits some improvement in the cross-system log anomaly detection effect. Full article
(This article belongs to the Special Issue Deep Learning and Its Applications in Anomaly Detection and Natural Language Processing)
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26 pages, 6077 KiB  
Article
MTR-SAM: Visual Multimodal Text Recognition and Sentiment Analysis in Public Opinion Analysis on the Internet
by Xing Liu, Fupeng Wei, Wei Jiang, Qiusheng Zheng, Yaqiong Qiao, Jizong Liu, Liyue Niu, Ziwei Chen and Hangcheng Dong
Appl. Sci. 2023, 13(12), 7307; https://doi.org/10.3390/app13127307 - 20 Jun 2023
Cited by 3 | Viewed by 2497
Abstract
Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point [...] Read more.
Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains. Full article
(This article belongs to the Special Issue Deep Learning and Its Applications in Anomaly Detection and Natural Language Processing)
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