检索结果-内蒙古大学图书馆

Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Salil Bharany Nasser Taleb M. Tariq Sadiq Nayab Kanwal Mohamed Abdelhakim Taher M. Ghazal Manas Pradhan Ateeq Ur Rehman CET Department Tech Amritsar India UK Faculty of Management Canadian University Dubai Dubai UAE School of Architecture Engineering University of Brighton School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Engineering and IT University of Dubai School of Information Technology Skyline University College Sharjah UAE

Increased hardware capability has allowed many businesses to store massive amounts of data in the previous few years. Streams of data may go on forever, so they are used in so many different applications. Analyzing and querying these streams is challenging for a stream of dynamic information. The term "big data" refers to a collection of methods and tools that must be combined in novel ways to unearth the vast amounts of previously unseen information buried within vast, diverse, and intricate databases. There are several ways to obtain, organize and analyze data in the big data world. In order to deal with streaming data, efficient data mining procedures must be in place. In this article, we examine a variety of Big Data-related concerns and challenges. This paper introduces the features, and challenges of managing big data.

关键词： Planets Transforms Big Data Media Telecommunications Security Surges

来源：评论

学校读者我要写书评

暂无评论

Differential Diagnostic Value of Machine Learning–Based Models for Embolic Stroke

引用

Clinical and Applied Thrombosis/Hemostasis 2023年 29卷 10760296231203663-10760296231203663页

作者： Kuo, HsunYu Liu, Tsai-Wei Huang, Yo-Ping Chin, Shy-Chyi Ro, Long-Sun Kuo, Hung-Chou Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Neurology Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Institute of Electrical and Electronics Engineers Taipei Taiwan Institution of Engineering and Technology Taipei Taiwan Fellow of Chinese Automatic Control Society Taipei Taiwan Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan

Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.). The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke. © The Author(s) 2023.

关键词： atrial fibrillation cancer-associated thrombosis data augmentation differential diagnosis diffusion-weighted imaging machine learning

来源：评论

学校读者我要写书评

暂无评论

Synthetic Dataset Generation Methods for computer Vision Application

Synthetic Dataset Generation Methods for Computer Vision App...

引用

International Conference on Smart Systems and Technologies (SST)

作者： Matej Arlovic Davor Damjanovic Franko Hrzic Josip Balen Faculty of Electrical Engineering Computer Science and Information Technology J. J. Strossmayer University of Osijek Osijek Croatia Department of Orthopaedic Surgery and Sports Medicine Boston Children’s Hospital Harvard Medical School Boston MA USA Boston MA United States Center for Artificial Intelligence and Cybersecurity University of Rijeka Rijeka Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Deep learning models rely on datasets for training, validation, and testing. They require diverse and representative datasets to represent real-world scenarios effectively. The performance of deep learning models is heavily influenced by data quality and quantity. High-quality and diverse datasets are essential for developing robust and accurate models. However, acquiring data for datasets can pose challenges since the time and complexity associated with manual data labeling. In order to address this problem, synthetic data generated by 3D software like Unreal Engine 5, Blender, and NVIDIA Omniverse, as well as AI-generated images in Stable Diffusion and DALL-E 3, are employed. Synthetic data facilitate the rapid and straightforward generation of aggregated datasets that accurately represent real-world scenarios. The impact of synthetic data on the performance and quality of deep learning model outcomes requires further investigation.

关键词： Deep learning Solid modeling Three-dimensional displays Computational modeling Manuals Diffusion models Data models Software Engines Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Spectral-Spatial Fusion Hypergraph Convolutional network for Multispectral Point Cloud Classification

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

引用

information Systems and Computing Technology (ISCTech), International Conference on

作者： Jiangbo Huang Qingwang Wang Zifeng Zhang Sivang Liu Zhifeng Wang Tao Shen Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China School of Information Engineering and Automation Kunming University of Science and Technology Kunming China General Logistics Department of PLA Real Estate Resource Management Center Beijing China Beijing Anlu International Technology Co Beijing China

Graph Neural networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Approach to Security Games with Signaling

arXiv

引用

arXiv 2022年

作者： Żychowski, Adam Mańdziuk, Jacek Bondi, Elizabeth Venugopal, Aravind Tambe, Milind Ravindran, Balaraman Center for Research on Computation and Society Harvard University United States Faculty of Mathematics and Information Science Warsaw University of Technology Poland Robert Bosch Centre for Data Science and AI IIT Madras India Department of Computer Science and Engineering IIT Madras India

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings. In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender’s strategies (expected payoffs). © 2022, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

引用

Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Shear-driven anomalous diffusion: Memory effects and stochastic resetting

引用

Physics of Fluids 2025年第6期37卷

作者： Sandev, Trifce Iomin, Alexander Kurths, Jürgen Kocarev, Ljupco Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 Skopje1000 Macedonia Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 Skopje1000 Macedonia Department of Physics Korea University Seoul02841 Korea Republic of Solid State Institute Technion Haifa32000 Israel Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physics Humboldt-Universität zu Berlin Berlin Germany Faculty of Computer Science and Engineering Ss. Cyril and Methodius University PO Box 393 Skopje1000 Macedonia

We consider a shear-driven anomalous diffusion by introducing a memory kernel in the Fokker-Planck equation, which results from the long-tailed waiting time of the particle. We analyze the probability density function and the corresponding moments in the framework of the subordination approach. The moments, obtained analytically, show that the system exhibits characteristic crossover anomalous dynamics. We also explore corresponding process under stochastic resetting, and we find that the system reaches a non-equilibrium stationary state in the long time limit that also results in saturation of the evolution of corresponding mean squared displacement, variance, skewness, and kurtosis. © 2025 Author(s).

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

引用

Journal of New Media 2022年第4期4卷 165-177页

作者： Ying Ma Jiaqi Zhang Pengyu Liu Zhihao Wei Lingfei Zhang Xiaowei Jia School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810007China The Information Department Beijing University of TechnologyBeijing100124China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing key Laboratory Beijing100124China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing100871China Department of Computer Science University of Pittsburgh15260USA

Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon *** forest monitoring request in-suit data from field *** recent development of the remote sensing technic,large-scale satellite data become available for surface *** to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain *** developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing ***,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high *** at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative *** are three improvements in this ***,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the ***,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss ***,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during *** then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature *** experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorit

关键词： Remote sensing forest segmentation boundary preserving double loss function

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in E-health: A Comprehensive Survey of Anxiety

Machine Learning in E-health: A Comprehensive Survey of Anxi...

引用

2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Teelhawod, Bibi Nushrina Akhtar, Faijan Heyat, Md Belal Bin Tripathi, Pragati Mehrotra, Rajat Asfaw, Ashamo Betelihem Shorman, Omar Al Masadeh, Mahmoud University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Gautam Buddha University Department of Electrical Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Aktu UP India Northwestern Polytechnical University School of Software Shaanxi Xian China Najran University College of Engineering and A1 Shrouk Trading Company Najran Saudi Arabia Yarmouk University Computer Engineering Department Irbid Jordan

ISBN: (纸本)9781665416566

Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy, hunger loss and desperate ideas. This study aims to define the extent of the research carried on anxiety with machine learning (ML) and to generate a database. We highlight serious problems for additional research obligations and communications. This paper designs a perception-based image of ML We have investigated deep insights of material to re-claim the current understanding of anxiety with ML from engineering detection to clinical treatment. We found 18 research articles and 28 closest keywords in the Web of science (WoS) database. In addition, we extracted the information including objective, subject, major, scoring technique, classifier, and performance measure from previously published 18 articles. This study has discovered the research gap and future direction, so far focused on anxiety with ML. In addition, it also delivers objectives for the upcoming work to be competent in a significant and scientific manner. © 2021 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severity Level of Parkinson's Disease from Spontaneous Speech

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severi...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Thiri Wai Yu-Shan Liao Ting-Yun Liao Chin-Hsien Lin Chi-Sheng Hung Li-Chen Fu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Neurology National Taiwan University Hospital Bei-Hu Branch Graduate Institute of Biomedical Engineering College of Medical Science and Technology Taipei Medical University Taipei Taiwan Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan NTU Center for Artificial Intelligence (AI) and Advanced Robotics

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Parkinson's disease (PD) is the cause of the gradual decline of nerve cells that control movement disorder disease, which is most common among the elderly in the US after Alzheimer's disease. There are several studies on detecting Parkinson's disease from speech using machine learning techniques; however, most of them focus on classifying healthy patients (HC) against Parkinson's disease (PD). This paper focuses on developing a screening system that could detect healthy (HC) vs. mild Parkinson's (MP) vs. severe Parkinson's (SP) from spontaneous speech. Four acoustic feature sets were compared for the screening system. Our proposed AeXGB was also compared with six different classifying approaches. The result has shown that extracting the phonation features and using AeXGB could achieve an accuracy of 92% for classifying HC vs. MP vs. SP, which outperforms the traditional machine learning approach for three class classifications of Parkinson's severity level from spontaneous speech.

关键词： Deep learning Accuracy Parkinson's disease Autoencoders Neurons Speech enhancement Biomarkers Feature extraction Multilingual Older adults

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：