检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Mahyar, Abtin Motamednia, Hossein Rahmati, Dara Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and engineering Shahid Beheshti University Tehran Iran

An accurate understanding of a self-driving vehicle’s surrounding environment is crucial for its navigation system. To enhance the effectiveness of existing algorithms and facilitate further research, it is essential to provide comprehensive data to the routing system. Traditional approaches rely on installing multiple sensors to simulate the environment, leading to high costs and complexity. In this paper, we propose an alternative solution by generating a top-down representation of the scene, enabling the extraction of distances and directions of other cars relative to the ego vehicle. We introduce a new synthesized dataset that offers extensive information about the ego vehicle and its environment in each frame, providing valuable resources for similar downstream tasks. Additionally, we present an architecture that transforms perspective view RGB images into bird’s-eye-view maps with segmented surrounding vehicles. This approach offers an efficient and cost-effective method for capturing crucial environmental information for self-driving cars. 1 © 2023, CC BY.

关键词： Birds

来源：评论

学校读者我要写书评

暂无评论

Generating CGAN-based Synthetic Dermoscopy Images and Classifying with Deep Lightweight CNN

Generating CGAN-based Synthetic Dermoscopy Images and Classi...

引用

Computing, Networking, Telecommunications & engineering sciences Applications (CoNTESA), International Conference on

作者： Suriya Sultana Israt Yasmin Muhammed J. A. Patwary Mohammad Arif Sobhan Bhuiyan Iftekhar Salam Mahdi H. Miraz Department of Computer Science and Engineering International Islamic University Chittagong School of Electrical and Electronic Engineering Xiamen University Malaysia Sepang Malaysia School of Computing and Data Science (SCDS) Xiamen University Malaysia Sepang Malaysia

Effective dermatological diagnosis and treatments significantly depend on the accurate categorisation of the skin lesions. It is difficult to classify skin lesions using automated diagnostic techniques due to class-imbalance datasets and the absence of labelled data. The applications of various deep learning methods have demonstrated superior performance in medical diagnosis in the recent past. Unfortunately, training these models requires a substantial quantity of labelled instances. A novel approach, combining Deep Learning convolutional neural networks (CNNs) as well as conditional generative adversarial networks (CGANs), for classifying skin lesion images, has been presented in this study. CGAN is an enhanced version of the GAN, in which the generator takes input images with specific class labels. Applying conditions for minority classes can help to equalise the dataset by increasing the sample size for those minority classes. In this research, CGANs were used to produce synthetic images of minority classes for a skin lesion dataset named PAD UFES-20. The images generated from CGANS were added then to the original dataset to train a lightweight deep-learning CNN network named MobileNetv2. The analysis of the results shows an improvement of 6% average accuracy and 5% recall value, when the synthetic data augmentation was added to the original dataset. This synthetic data augmentation approach can also widely be used in various medical classification applications to reduce the imbalance problems, resulting in enhanced diagnosis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust MIMO-OFDM Channel Estimation for 5G Networks Using Conditional Self-Attention GANs

Robust MIMO-OFDM Channel Estimation for 5G Networks Using Co...

引用

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： K. Aravinda Tejaswini Rangaraju Atul Singla Ashish Parmar Ali Abdulhussein Hameed Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Due to the rapid increase in Mobile networks data traffic, there is a need to improve on channel estimation methods for Multiple Input Multiple Output (MIMO) system that combined with the Orthogonal Frequency Division Multiplexing (OFDM) relevant to 5G and future generations. The papers introduced in this research present a novel method that is based on the use of Conditional Self-Attention Generative Adversarial Networks (CSAGANs) to improve the channel estimation. CSAGANs combine the generative adversarial network (GAN) that has the function of generating high quality synthetic image with long-range dependencies and self-attention mechanisms that enable Identify the dependencies of MIMO-OFDM channels. The conditioned aspect maintains that the estimated sources satisfy realistic conditions in the channels, which enhances the solutions' accuracy and robustness. Experimental outcomes also prove that in terms of MSE and BER, proposed methodology exhibits better performance as compared to conventional techniques like LS and MMSE. This means that the proposed method provides higher efficiency in cases where there are mobile nodes and constantly changing SNR as in the case of 5G technology. Moreover, it is revealed that the CSAGAN framework can effectively decrease computational costs which is why it can be applicable for real-time tasks. This work opens up avenues for developing more robust and accurate channel estimation schemes, which are required for the evolution of the forth coming wireless communication systems.

关键词： Accuracy 5G mobile communication OFDM Channel estimation Estimation Generative adversarial networks MIMO Mathematical models Computational complexity Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Regularized Newton Methods for Monotone Variational Inequalities with Hölder Continuous Jacobians

arXiv

引用

arXiv 2022年

作者： Liu, Chengchang Luo, Luo Department of Computer Science and Engineering The Chinese Unversity of Hong Kong Hong Kong School of Data Science Fudan University China

This paper considers the problems of solving monotone variational inequalities with Hölder continuous Jacobians. By employing the knowledge of Hölder parameter ν, we propose the ν-regularized extra-Newton method within at most O(ǫ−2/(2+ν)) iterations to obtain an ǫ-accurate solution. In the case of ν is unknown, we propose the universal regularized extra-Newton method within O(ǫ−4/(3(1+ν))) iteration complexity. © 2022, CC BY.

关键词： Newton-Raphson method

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels

arXiv

引用

arXiv 2023年

作者： Wang, Sihan Wu, Fuping Li, Lei Gao, Zheyao Hong, Byung-Woo Zhuang, Xiahai School of Data Science Fudan University Shanghai China Institute of Biomedical Engineering University of Oxford Oxford United Kingdom Computer Science Department Chung-Ang University Seoul Korea Republic of

Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization problem of this task is inherently challenging, requiring well-designed constraints. In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints. Firstly, we extend a conventional non-convex energy function as an intensity constraint and implement it with U-Net. For shape constraint, synthetic images are generated from anatomical labels via image-to-image translation, as shape supervision for the segmentation network. Moreover, augmentation invariance is applied to facilitate the segmentation network to learn the latent features in terms of shape. We evaluated the proposed framework using the public datasets from MICCAI2019 MSCMR Challenge, and achieved promising results on cardiac MRIs with Dice scores of 0.5737, 0.7796, and 0.6287 in Myo, LV, and RV, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Analysis of cloud services using OWASP security design

Analysis of cloud services using OWASP security design

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Elumalaivasan Poongavanam Munirathinam. T Kayalvizhi. V Sankar Murugesan Department of Data Science and Business System SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Sona College of Technology Salem Tamil Nadu India School of Computer Science and Engineering and Information Science Presidency University Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

Cloud computing is extremely popular model among enterprises. It provides more availability of cloud resources when needed with high scalability and extremely cost-effective model. Security is one of the least understood concepts to provide and access for OWASP applications in cloud computing. Many enterprises especially banking clients are risk-averse and have less cloud adoption rate. In this study, security research in the area of cloud security is reviewed and AWS (Amazon Web Service) is the top provider of cloud computing. Since, we have also demonstrated how it works in the security exploration. This paper's major goal is to make cloud computing security is a key activity and prevent unauthorized access to the apps used in the real world.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Stroke Prediction Using Machine Learning

Stroke Prediction Using Machine Learning

引用

International Conference on Advances in Electronics, computers and Communications (ICAECC)

作者： Abinandhini D M Aman Kumar Gudi Vishnu Teja Mandava Sukesh Divya S Naman Chauhan I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. With a mortality rate of 5.5 million per year, it ranks as the second leading cause of death globally. Over 15 million individuals experience a stroke each year, and one person dies from one every four minutes. According to the World Health Organization, stroke is the main cause of death and disability worldwide (WHO). Identifying the many stroke warning signs helps lessen the severity of the stroke. A stroke can be avoided in up to 80% of instances because it is typically the result of a poor lifestyle. As a result, stroke prediction becomes important and should be employed to stop it from causing long-term harm. The current study uses a variety of machine learning models, including Gaussian Naive Bayes, Logistic Regression, Support Vector Machine (SVM), KNN and Random Forest to predict stroke. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that KNN had the least accuracy of 76.32% and Random Forest had the highest accuracy of 94.81%.

关键词： Support vector machines Accuracy Organizations Stroke (medical condition) Predictive models Vectors Physiology

来源：评论

学校读者我要写书评

暂无评论

Recognition of tooth numbers and conditions on dental panoramic radiographs for assisting dental chart filing

Recognition of tooth numbers and conditions on dental panora...

引用

Medical Imaging 2023: computer-Aided Diagnosis

作者： Muramatsu, Chisako Sakai, Koki Seino, Yuta Takahashi, Ryo Hayashi, Tatsuro Nishiyama, Wataru Zhou, Xiangrong Hara, Takeshi Katsumata, Akitoshi Fujita, Hiroshi Faculty of Data Science Shiga University 1-1-1 Banba Hikone Shiga522-8522 Japan Department of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University Gifu501-1193 Japan Media Co. Ltd Tokyo113-0033 Japan Department of Oral Radiology School of Dentistry Asahi University Mizuho Gifu501-0296 Japan

ISBN: (纸本)9781510660359

Dental panoramic radiographs are often obtained at dental clinic visits for diagnosis and recording purposes. Automated filing of dental charts can help dentists in reducing their workload and improving diagnostic efficiency. The purpose of this study is to develop a system that prerecords a dental chart by recognizing teeth with their numbers and restoration history on dental panoramic radiographs. The proposed system uses YOLO which detects 16 types of teeth and restoration conditions simultaneously. Based on the detected tooth types, they were further classified into 32 types and combined with the tooth conditions by post-processing. We tested our method on 870 panoramic images obtained at 10 different facilities by 5-fold cross validation. The proposed method obtained 0.99 recall and precision for recognition of 32 tooth types and 0.90 recall and 0.90 precision on determining the tooth condition. It has the potential to assist prefiling the dental charts for efficient dental care. © 2023 SPIE.

关键词： Restoration

来源：评论

学校读者我要写书评

暂无评论

Future of Cybersecurity

Future of Cybersecurity

引用

Recent Trends in Information Technology (CSCTIT), College of science International Conference of

作者： Yasamin Alagrash Senior lecturer in the computer science department college of science Mustansiriyah University Baghdad Iraq Department of computer science Ph.D. in computer science and informatics college of engineering Oakland university MI USA Computer science department MSC in data security university of technology Baghdad Iraq

Cybersecurity is critical to the digital world's ongoing progress. Whether you are a company owner or a customer, you must be prepared to adapt to new technology, policies, and methods that will aid in the creation of a safer and more secure online environment. As a result, the frequency of exposed network surfaces compels us to invest in more advanced protection measures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Video Inpainting with Cycle Consistency Constraints

Semi-Supervised Video Inpainting with Cycle Consistency Cons...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhiliang Wu Hanyu Xuan Changchang Sun Weili Guan Kang Zhang Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China School of Big Data and Statistics Anhui University China Department of Computer Science Illinois Institute of Technology USA School of Information Technology Monash University Australia Peng Cheng Laboratory China

Deep learning-based video inpainting has yielded promising results and gained increasing attention from re-searchers. Generally, these methods assume that the cor-rupted region masks of each frame are known and easily ob-tained. However, the annotation of these masks are labor-intensive and expensive, which limits the practical application of current methods. Therefore, we expect to relax this assumption by defining a new semi-supervised inpainting setting, making the networks have the ability of completing the corrupted regions of the whole video using the anno-tated mask of only one frame. Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively. Besides, we introduce a cycle consistency loss to regularize the training parameters of these two networks. In this way, the completion network and the mask prediction network can constrain each other, and hence the overall performance of the trained model can be maximized. Furthermore, due to the natural existence of prior knowledge (e.g., corrupted contents and clear bor-ders), current video inpainting datasets are not suitable in the context of semi-supervised video inpainting. Thus, we create a new dataset by simulating the corrupted video of real-world scenarios. Extensive experimental results are reported to demonstrate the superiority of our model in the video inpainting task. Remarkably, although our model is trained in a semi-supervised manner, it can achieve compa-rable performance as fully-supervised methods.

关键词：

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：