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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Enhancing Cyclone Center Identification in Radar Images through Deep Learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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OpenSLU: A unified, modularized, and extensible toolkit for spoken language understanding 61

OpenSLU: A unified, modularized, and extensible toolkit for ...

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61st Annual Meeting of the Association for Computational Linguistics, ACL-DEMO 2023

作者： Qin, Libo Chen, Qiguang Xu, Xiao Feng, Yunlong Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China

ISBN: (纸本)9781959429708

Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding. Specifically, OpenSLU unifies 10 SLU models for both single-intent and multi-intent scenarios, which support both non-pretrained and pretrained models simultaneously. Additionally, OpenSLU is highly modularized and extensible by decomposing the model architecture, inference, and learning process into reusable modules, which allows researchers to quickly set up SLU experiments with highly flexible configurations. OpenSLU is implemented based on PyTorch, and released at https://***/LightChen233/OpenSLU. © ACL-DEMO 2023. All rights reserved.

关键词： Semantics

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An Integrated Navigation Algorithm Based on LSTM Neural Network

An Integrated Navigation Algorithm Based on LSTM Neural Netw...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Cao, Yu Bai, Hongyang Liang, Huaju Zou, Guanyu School of Energy and Power Engineering Nanjing University of Science and Technology Nanjing210094 China Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information Technology Jiaxing314001 China

ISBN: (纸本)9789811966125

For the issue that the positioning accuracy of GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation system is reduced because of the presence of Satellite blocking area in complex environment, a method based on LSTM (Long-Short Term Memory) neural network is designed to improve system. While satellite valid, the GNSS position increments are treated as the true value to train, and when the satellites reject, the GNSS information is replaced by the accumulated position increment predicted by the trained network to correct the accumulated error of INS. Experiments are based on the emulational IMU information, and the consequence show that the proposed algorithm can effectively suppress the divergence of pure INS caused by the loss of satellite signals, and improve the accuracy of the system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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STFormer: A dual-stage transformer model utilizing spatio-temporal graph embedding for multivariate time series forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第3期46卷 6951-6967页

作者： Xiao, Yuteng Liu, Zhaoyang Yin, Hongsheng Wang, Xingang Zhang, Yudong Jinan China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Xuzhou University of Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing China

Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Recurrent neural networks

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A Keyword Extraction Method for Traditional Chinese Medicine Constitution Identification Based on Batch Data Hybrid Sampling 7

A Keyword Extraction Method for Traditional Chinese Medicine...

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Xing, Yuhao Yu, Bihui Xu, Hongsen Shenyang Ligong University School of Information Science and Engineering Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350334197

Keyword extraction for traditional Chinese medicine (TCM) constitution identification refers to using keyword extraction methods to extract critical symptoms from task corpora to achieve the TCM constitution identification task. In this paper, we constructed a dataset for keyword extraction in TCM constitution identification and implemented keyword extraction using sequence labeling methods. However, we discovered an issue of uneven category distribution during the keyword extraction process, which affected the model's performance. To address this problem, we propose a model training method based on batch data hybrid sampling algorithm. This method divides the training dataset into several batches. It strategically balances the ratio of common and rare category data within each batch, achieving an even distribution of data categories within each training batch and reducing the impact of insufficient learning on rare categories by the model. Experimental results demonstrate that our proposed method improves the performance of keyword extraction for TCM constitution identification, with an average F1 score improvement of 4.29% for rare category keyword extraction. © 2023 IEEE.

关键词： Extraction

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RAGRAPH: A General Retrieval-Augmented Graph Learning Framework 38

RAGRAPH: A General Retrieval-Augmented Graph Learning Framew...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Jiang, Xinke Qiu, Rihong Xu, Yongxin Zhang, Wentao Zhu, Yichen Zhang, Ruizhe Fang, Yuchen Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China University of Electronic Science and Technology of China China Center on Frontiers of Computing Studies Peking University Beijing China Big Data Technology Research Center Nanhu Laboratory Jiaxing China Peking University Information Technology Institute Binhai Tianjin China

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability. © 2024 Neural information processing systems foundation. All rights reserved.

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A Blockchain-based Intrusion Detection/Prevention Systems in IoT Network: A systematic review

A Blockchain-based Intrusion Detection/Prevention Systems in...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Shalabi, Khawla Al-Haija, Qasem Abu Al-Fayoumi, Mustafa Department of Cybersecurity King Hussein School of Computing Sciences Prince Sumaya University for Technology PO Box 1438 Amman11941 Jordan Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan

The Internet of Things (IoT) is an innovative technology used in manufacturing, environmental monitoring, home automation, human health applications, and other fields. IoT devices, however, are susceptible to various security risks. Cyber-attacks on IoT systems can halt the entire system, resulting in data loss. Intrusion Detection Systems (IDS) can quickly identify malicious activity that could have compromised the network's performance. Effective IDS and Intrusion Prevention System (IPS) are necessary to protect IoT systems. Blockchain technology presents another innovative method to strengthen security systems against the latest attacks. This paper focuses on conducting a Systematic Literature Review (SLR) on Blockchain-based Intrusion Detection/Prevention Systems in IoT Networks. We reviewed several relevant blockchain-based IDS and IPS proposed for IoT networks and their mechanisms. The most recent research articles, published between 2017 and 2022, were selected from several databases. Consequently, a comprehensive review of these selected papers sheds light on their techniques, advantages, and drawbacks. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Intrusion detection

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Operator Placement for IoT Data Streaming Applications in Edge computing Environment 22nd

Operator Placement for IoT Data Streaming Applications in ...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Sixin Tang, Bing Yang, Qing Liu, Yuanyuan School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan411201 China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou510725 China

ISBN: (纸本)9783031226762

With the rapid development of IoT applications, how to process large and continuous data streams in real time for IoT applications has become a challenge in recent years. This paper presents a model to predict the Maximum Sustainable Throughput (MST) of data streaming applications at the edge, and then proposes GACP, a constraint programming-base operator placement strategy to maximize MST using the genetic algorithm. We evaluated the proposed operator placement strategy in terms of MST, the number of edge servers in usage, and the percentage of communications within edge servers. Experimental results show that compared with the existing heuristic algorithms, the proposed GACP strategy can achieve better MST, which is 2–5 times higher than existing algorithms. © 2023, Springer Nature Switzerland AG.

关键词： Edge computing

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IoT networks attacks detection using multi-novel features and extra tree random - voting ensemble classifier (ER-VEC)

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Journal of Ambient Intelligence and Humanized computing 2023年第12期14卷 16637-16651页

作者： Sarwar, Ayesha Mushtaq, Muhammad Faheem Akram, Urooj Rustam, Furqan Hamza, Ameer Rupapara, Vaibhav Ullah, Saleem Department of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Artificial Intelligence The Islamia University of Bahawalpur Bahawalpur63100 Pakistan School of Computer Science University College Dublin DublinD04 V1W8 Ireland School of Computing and Information Sciences Florida International University MiamiFL United States

Internet usage is increasing day by day all over the world and as a result, technology is also developing to make daily life appliances as smart as possible. Millions of devices are connected using IoT technology, and the vulnerabilities of these devices are still exploitable by attackers. Having access to IoT devices through a Bot Master allows the Bot Master to attack a targeted server with these devices. To detect malicious traffic in IoT networks, there is a need for an intelligent mechanism. Although there have been many studies on the detection of botnet malware, accuracy and efficiency remain a gap. This study focuses on an automatic system that can detect botnet malware with high accuracy. A new ensemble model has been proposed in this study, known as the Extra Tree Random Voting Ensemble Classifier (ER-VEC), which is a combination of two tree-based models called Extra Tree and Random Forest. The proposed model is tested on several malicious traffic in the IoT networks datasets such as IoTID20, MedBIoT, UNSW-NB15, N-BaIoT, and ER-VEC achieving 99.99%, 99.91%, 95.64%, and 100% accuracy scores, respectively. In comparison with the proposed model, other machine learning models were also employed, and ER-VEC significantly outperformed them in terms of accuracy, precision, recall, F1-score, and error rate across all datasets. In addition, we performed K-Fold cross-validation and found that ER-VEC achieved an accuracy score of 98% and a standard deviation of 0.04±. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Botnet

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