检索结果-内蒙古大学图书馆

International Joint Conference on Neural Networks (IJCNN)

作者： Tong Zhang Di Wang Huanhuan Chen Zhiwei Zeng Wei Guo Chunyan Miao Lizhen Cui School of Computer Science and Technology Shandong University China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore Joint NTU-WeBank Research Centre on Fintech Nanyang Technological University Singapore School of Computer Science and Technology University of Science and Technology of China China School of Software Shandong University China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Nowadays, with the rapid growth of microblogging networks for news propagation, there are increasingly more people accessing news through such emerging social media. In the meantime, fake news now spreads at a faster pace and affects a larger population than ever before. Compared with traditional text news, the news posted on microblog often has attached images in the context. So how to correctly and autonomously detect fakes news in a multi-modal manner becomes a prominent challenge to be addressed. In this paper, we propose an end-to-end model, named BERT-based domain adaptation neural network for multi-modal fake news detection (BDANN). BDANN comprises three main modules: a multi-modal feature extractor, a domain classifier and a fake news detector. Specifically, the multi-modal feature extractor employs the pretrained BERT model to extract text features and the pretrained VGG-19 model to extract image features. The extracted features are then concatenated and fed to the detector to distinguish fake news. The role of the domain classifier is mainly to map the multi-modal features of different events to the same feature space. To assess the performance of BDANN, we conduct extensive experiments on two multimedia datasets: Twitter and Weibo. The experimental results show that BDANN outperforms the state-of-the-art models. Moreover, we further discuss the existence of noisy images in the Weibo dataset that may affect the results.

关键词： Feature extraction Adaptation models Twitter data mining Neural networks Detectors Bit error rate

来源：评论

学校读者我要写书评

暂无评论

Deep Multi-Resolution Mutual Learning for Image Inpainting

TechRxiv

引用

TechRxiv 2021年

作者： Zheng, Huan Zhang, Zhao Zhang, Haijun Yang, Yi Yan, Shuicheng Wang, Meng The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Hefei University of Technology Hefei230601 China The Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230601 China Shenzhen China The Centre for Artificial Intelligence University of Technology Sydney SydneyNSW Australia Singapore The National University of Singapore Singapore117583 Singapore

Deep image inpainting methods have improved the inpainting performance greatly due to the powerful representation ability of deep learning. However, current deep inpainting models still tend to produce unreasonable structures and blurry textures due to the ill-posed properties of the task, i.e., image inpainting is still a challenging topic. In this paper, we therefore propose a novel deep multi-resolution mutual learning (DMRML) strategy for progressive inpainting, which can fully explore the information from various resolutions. Specifically, we design a new image inpainting network, termed multi-resolution mutual network (MRM-Net), which takes the damaged images of various resolutions as input, then excavates and exploits the correlation among different resolutions to guide the image inpainting process. Technically, the setting of MRM-Net designs two new modules called multi-resolution information interaction (MRII) and adaptive content enhancement (ACE). MRII aims at discovering the correlation of multiple resolutions and exchanging information, and ACE focuses on enhancing the contents using the interacted features. We also present an memory preservation mechanism (MPM) to prevent from the information loss with the increasing layers. Extensive experiments on Paris Street View, Places2 and CelebA-HQ datasets demonstrate that our proposed MRM-Net can effectively recover the textures and structures, and performs favorably against other state-of-the-art methods. © , CC BY.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

The Design and Empirical Research of One Main Stem with Others Branches Monetary System Based on Blockchain Technology

The Design and Empirical Research of One Main Stem with Othe...

引用

International Conference on Communication Technology (ICCT)

作者： Liu Feng Lu Shiying Qi Jiayin Li Zhibing School of Computer Science and Technology East China Normal University Shanghai China Changsha University of Science and Technology Changsha China Institute of Artificial Intelligence and Change Management Shanghai University of International Business and Economics Shanghai China School of Data Science and Engineering East China Normal University Shanghai International Studies University Shanghai China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

The European debt crisis impacted the international monetary status of the euro as well as triggered a large number of discussions and opinions on the design of the monetary mechanism of the eurozone in academic circles. This study adopts DEA method and takes the euro area as an example to evaluate the two different monetary systems from the perspective of technical efficiency, pure technical efficiency and scale efficiency. Through empirical analysis, it can be found that when the one main stem with many other branches monetary system is suffering from crisis, the system is more stable. In contrast, the current scale of the euro monetary system is inefficient which cause a lot of waste of resource. This article gives a new currency system based on blockchain, which can maintain the existing euro-structured currency system, and is compatible with the euro zone sovereign countries to issue their own sovereign electronic currency, and the technology is more efficient. This article has reference significance for trying to solve or improve the European debt crisis.

关键词： Europe Economics Currencies Blockchain Market research Analytical models Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Multidimensional scaling, Sammon mapping, and Isomap: Tutorial and survey

arXiv

引用

arXiv 2020年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, metric MDS, and non-metric MDS. Sammon mapping and Isomap can be considered as special cases of metric MDS and kernel classical MDS, respectively. In this tutorial and survey paper, we review the theory of MDS, Sammon mapping, and Isomap in detail. We explain all the mentioned categories of MDS. Then, Sammon mapping, Isomap, and kernel Isomap are explained. Out-of-sample embedding for MDS and Isomap using eigenfunctions and kernel mapping are introduced. Then, Nystrom approximation and its use in landmark MDS and landmark Isomap are introduced for big data embedding. We also provide some simulations for illustrating the embedding by these methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

引用

IETE Journal of Research 2023年第11期69卷 XLIV-LVII页

作者： M. Kavitha Margret E. Golden Julie Y. Harold Robinson D. Vijayanandh S. Vimal Seifidine Kadry Ahmed M. El-Sherbeeny Mohammed A. El-Meligy 1 Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India 2 Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India 3 School of Information Technology and Engineering Vellore Institute of Technology Vellore India 4 Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore India 5 Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamilnadu India 6 Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway 7 Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey

arXiv

引用

arXiv 2020年

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space. SNE considers Gaussian distribution for the probability in both the input and embedding spaces. However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively. In this tutorial and survey paper, we explain SNE, symmetric SNE, t-SNE (or Cauchy-SNE), and t-SNE with general degrees of freedom. We also cover the out-of-sample extension and acceleration for these methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Locally linear embedding and its variants: Tutorial and survey

arXiv

引用

arXiv 2020年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science & David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada

This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE. Then, we cover out-of-sample embedding using linear reconstruction, eigenfunctions, and kernel mapping. Incremental LLE is explained for embedding streaming data. Landmark LLE methods using the Nystrom approximation and locally linear landmarks are explained for big data embedding. We introduce the methods for parameter selection of number of neighbors using residual variance, Procrustes statistics, preservation neighborhood error, and local neighborhood selection. Afterwards, Supervised LLE (SLLE), enhanced SLLE, SLLE projection, probabilistic SLLE, supervised guided LLE (using Hilbert-Schmidt independence criterion), and semi-supervised LLE are explained for supervised and semi-supervised embedding. Robust LLE methods using least squares problem and penalty functions are also introduced for embedding in the presence of outliers and noise. Then, we introduce fusion of LLE with other manifold learning methods including Isomap (i.e., ISOLLE), principal component analysis, Fisher discriminant analysis, discriminant LLE, and Isotop. Finally, we explain weighted LLE in which the distances, reconstruction weights, or the embeddings are adjusted for better embedding;we cover weighted LLE for deformed distributed data, weighted LLE using probability of occurrence, SLLE by adjusting weights, modified LLE, and iterative LLE. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation

引用

Scientific reports 2025年第1期15卷 18423页

作者： Lalitha Krishnasamy Siva C Rajesh Kumar Dhanaraj Mahmoud Ahmad Al-Khasawneh Taher Al-Shehari Nasser A Alsadhan Shitharth Selvarajan Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India. Department of Information Technology Nandha Engineering College Erode Tamil Nadu India. Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India. School of Computing Skyline University College University City Sharjah 1797 Sharjah UAE. Applied Science Research Center Applied Science Private University Amman Jordan. Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University 11362 Riyadh Saudi Arabia. Computer Science Department College of Computer and Information Sciences King Saud University 12372 Riyadh Saudi Arabia. Department of Computer Science Kebri Dehar University 250 Kebri Dehar Ethiopia. ShitharthS@kdu.edu.et. Department of Computer Science and Engineering Chennai Institute of Technology Chennai India. ShitharthS@kdu.edu.et. Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India. ShitharthS@kdu.edu.et. School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds UK. ShitharthS@kdu.edu.et.

Traffic congestion forecasting is one of the major elements of the Intelligent Transportation Systems (ITS). Traffic congestion in urban road networks significantly influences sustainability by increasing air pollution levels. Efficient congestion management enables drivers to bypass heavily trafficked areas and reducing pollutant emissions. However, properly forecasting congestion spread remains challenging due to complex, dynamic, and non-linear nature of traffic patterns. The advent of Internet of Things (IoT) devices has introduced valuable datasets that can support the development of intelligent and sustainable transportation for modern cities. This work presents a Deep Learning (DL) approach of Reinforcement Learning (RL) based Bidirectional Long Short-Term Memory (BiLSTM) with Adaptive Secretary Bird Optimizer (ASBO) for traffic congestion prediction. The experimentation is evaluated on Traffic Prediction dataset and achieved better Mean Square Error (MSE) and Mean Absolute Error (MAE) with results of 0.015 and 0.133 respectively. Compared to the existing algorithms like RL, Deep Q Learning (DQL), LSTM and BiLSTM, the RL - BiLSTM with ASBO outperformed with the parameters MSE, RMSE, R2, MAE and MAPE with 37%, 27.44%, 26%, 33.52% and 35.8% respectively. The better performance demonstrates that RL- BiLSTM with ASBO is well-suited to predict congestion patterns in road networks.

关键词： Adaptive secretary bird optimization Reinforcement learning Smart cities Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Unleashing the Strengths of Unlabeled data in Pan-cancer Abdominal Organ Quantification: The FLARE22 Challenge

arXiv

引用

arXiv 2023年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Ma, Shihao Young, Adamo Zhu, Cheng Meng, Kangkang Yang, Xin Huang, Ziyan Zhang, Fan Liu, Wentao Pan, YuanKe Huang, Shoujin Wang, Jiacheng Sun, Mingze Xu, Weixin Jia, Dengqiang Choi, Jae Won Alves, Natália de Wilde, Bram Koehler, Gregor Wu, Yajun Wiesenfarth, Manuel Zhu, Qiongjie Dong, Guoqiang He, Jian Wang, Bo He, Junjun Yang, Hua Yang, Huihua Huang, Bingding Lyu, Mengye Ma, Yongkang Guo, Heng Zhang, Rongguo Maier-Hein, Klaus The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Shanghai AI Laboratory Shanghai200232 China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing211113 China Ocean University of China Qingdao266100 China The Department of Computer Science University of Toronto Peter Munk Cardiac Centre University Health Network Vector Institute Toronto Canada Tinavi Medical Technologies Co. Ltd. Beijing100192 China University of Science and Technology Beijing Beijing100083 China The School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518055 China The Department of Radiological Algorithm Fosun Aitrox Information Technology Co. Ltd. Shanghai200033 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Technology University Shenzhen518000 China Xiamen University Xiamen361005 China Alibaba Beijing100084 China Infervision Medical Technology Co. Ltd. Beijing100025 China Hong Kong Centre for Cerebro-cardiovascular Health Engineering 000000 Hong Kong The Department of Radiology Armed Forces Yangju Hospital Yangju11429 Korea Republic of The Department of Radiology Radboudumc Nijmegen6525XZ Netherlands The Department of Medical Image Computing German Cancer Research Center Heidelberg69120 Germany ShenZhen Yorktal DMIT LLC Shenzhen518100 China Heidelberg69120 Germany The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing210008 China The College of Big Data and Internet Shenzhen Technology University Shenzhen518188 China The College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China The Manteia Technologies Co. Ltd Xiamen China The Alibaba DAMO Academy Beijing China The Peter Munk Cardiac Centre University Health Network Department of Laboratory M

Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations and lack a comprehensive evaluation of accuracy and efficiency in real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. We independently validated that a set of AI algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0% by using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce annotation requirements. The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively. They also enabled automatic extraction of key organ biology features, which was labor-intensive with traditional manual measurements. This opens the potential to use unlabeled data to boost performance and alleviate annotation shortages for modern AI models. © 2023, CC BY-NC-ND.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Inception-based network and multi-spectrogram ensemble applied for predicting respiratory anomalies and lung diseases

arXiv

引用

arXiv 2020年

作者： Pham, Lam Phan, Huy King, Ross Mertins, Alfred McLoughlin, Ian Competence Unit Data Science & Artificial Intelligence Center for Digital Safety & Security Austria Institute of Technology Austria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Institute for Signal Processing University of Lübeck Germany Singapore Institute of Technology Singapore

This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are firstly transformed into spectrograms where both spectral and temporal information are well presented, referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, referred to as back-end classification, for detecting whether patients suffer from lung-relevant diseases. Our experiments, conducted over the ICBHI benchmark meta-dataset of respiratory sound, achieve competitive ICBHI scores of 0.53/0.45 and 0.87/0.85 regarding respiratory anomaly and disease detection, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：