检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Li, Jun Li, Kexin Zhou, Yafeng Zhou, S. Kevin Institute of Computing Technology CAS Beijing100190 China University of Science and Technology of China Suzhou215123 China Department of Cardiology Dushu Lake Hospital Affiliated to Soochow University Suzhou215000 China Institution for Hypertension of Soochow University Suzhou215000 China

Targeted diagnosis and treatment plans for patients with coronary artery disease vary according to atherosclerotic plaque component. Coronary CT angiography (CCTA) is widely used for artery imaging and determining the stenosis degree. However, the limited spatial resolution and susceptibility to artifacts fail CCTA in obtaining lumen morphological characteristics and plaque composition. It can be settled by invasive optical coherence tomography (OCT) without much trouble for physicians, but bringing higher costs and potential risks to patients. Therefore, it is clinically critical to introduce annotations of plaque tissue and lumen characteristics from OCT to paired CCTA scans, denoted as the O2CTA problem in this paper. We propose a method to handle the O2CTA problem. CCTA scans are first reconstructed into multi-planar reformatted (MPR) images, which agree with OCT images in term of semantic contents. The artery segment in OCT, which is manually la-belled, is then spatially aligned with the entire artery in MPR images via the proposed alignment strategy. Finally, a classification model involving a 3D CNN and a Transformer, is learned to extract local features and capture dependence along arteries. Experiments on 55 paired OCT and CCTA we curate demonstrate that it is feasible to classify the CCTA based on the OCT labels, with an accuracy of 86.2%, while the manual readings of OCT and CCTA vary significantly, with a Kappa coefficient of 0.113. We will make our source codes, models, data, and results publicly available to benefit the research community. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Exploring The Impact Of Artificial Intelligence On Visual Communication In Unmasking Deep Fake And Ensuring Public Trust

Exploring The Impact Of Artificial Intelligence On Visual Co...

引用

Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Abbas Thajeel Rhaif Alsahlanee Abha Kiran Rajpoot Gaurav Agrawal Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of CSE(AIML) KIET Group of Institution Ghaziabad India Department of CSE(AI) KIET Group of Institution Ghaziabad India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

In this groundbreaking study, we delve into the pivotal role of the Vision Transformer (V-iT) in the realm of deepfake detection, seeking to unravel its effectiveness in navigating the intricate landscape of manipulated visual content. As image-generating technologies advance, the proliferation of Dfakes poses profound challenges to the integrity of digital media, sparking concerns about misinformation and eroding trust in visual communication. Against this backdrop, V-iT emerges as a cutting-edge solution, leveraging its unique ability to comprehend intricate patterns in visual data. Our research is anchored in a meticulous methodology, involving comprehensive experiments designed to assess V-iT's performance in deepfake detection. The results, spanning 10 diverse samples, reveal consistently high accuracy, precision, recall, and F1 rate values. V-iT's capacity to achieve accurate classifications, minimize false positives, and sensitively detect manipulated content underscores its robustness in addressing the complexities of deceptive media practices. This paper goes beyond a mere validation of V-iT's capabilities, offering a nuanced understanding of its performance metrics. The high precision values emphasize V-iT's accuracy in identifying manipulated content, while exceptional recall values highlight its sensitivity in capturing true positive instances. The balanced F1 rate provides a harmonious measure of V-iT's overall effectiveness, demonstrating its potential as a dependable tool in upholding the authenticity of visual communication.

关键词： Deepfakes Visualization Computer vision Accuracy Sensitivity Visual communication Weapons Resists Transformers Robustness

来源：评论

学校读者我要写书评

暂无评论

Power-Efficient Secured Hardware Design of AES Algorithm on High Performance FPGA 5

Power-Efficient Secured Hardware Design of AES Algorithm on ...

引用

5th International Conference on Contemporary computing and Informatics, IC3I 2022

作者： Kumar, Keshav Singh, Vijay Mishra, Gaurav Ravindra Babu, B. Tripathi, Nandita Kumar, Pramod University Institute of Computing Chandigarh University Punjab India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Amity University Uttar Pradesh Amity School of Engineering & Tchnology Uttar Pradesh Greater Noida India Adama Science and Technology University CSE Department Adama Ethiopia Amity University The Institution AIPS Uttar Pradesh Greater Noida India Lloyd Institute of Engineering and Technology Department of ECE Greater Noida India

ISBN: (纸本)9798350398267

With the expansion and growth of industries, the two major issues exist that affect both civilization and the environment. technology development has made it more difficult to communicate and transmit data over secure channels. The use of Green Communication (GC) technology and energy-efficient parts can lessen the lack of electricity and energy. The usage of various technologies under one framework is the main topic of this study. In this article, a hardware implementation of the Advanced Encryption Standard (AES) algorithm is shown. Field Programmable Gate Array (FPGA) devices are taken into consideration for hardware implementations. The power calculation of the AES algorithm is computed for several clock speeds of the Spartan-7 FPGA, and the results are examined using VIVADO tool. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity: Threats, Requirements, Deployments, Techniques, System Architecture, and Future Research

Cybersecurity: Threats, Requirements, Deployments, Technique...

引用

Smart Devices (ICSD), International Conference on

作者： Ajay Yadav Vijay Karnatak Amit Kumar Mishra Shalini Verma Pallaw Singh Aswal Neha Tripathi University Institute of Computing Chandigarh University Mohali Punjab India Dept. of Computer Science & Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Doon Group Of Institution Rishikesh India Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Cybersecurity safeguards computer systems, net- works, and digital information from a wide variety of cyber-attacks. Its major purpose is to protect the privacy of users’ data and resources while maintaining their integrity and making them accessible in the digital world. Cybersecurity is an area that receives investment from businesses, individuals, and governments all around the world in order to secure sensitive information, key infrastructure, and digital assets. Protection of Sensitive Information Both individuals and businesses store huge amounts of sensitive information digitally, including personal data, financial records, intellectual property, and trade secrets. This information must be protected in order to prevent unauthorized access or disclosure. The protection provided by cybersecurity prevents this information from being stolen, exposed, or accessed without authorization. Cyberattacks can result in severe financial losses, including theft of funds, fraud, and the expenditures associated with recovering from a cyber incident. It is possible to prevent these types of losses by taking preventative measures. Implementing these strategies helps prevent losses of this nature. In this paper, we propose a system architecture of a generic secure system, which can be deployed for various applications, i.e., smart healthcare, Intelligent Transport Systems (ITS), smart city, etc. We discuss the threats and attacks in cyberspace. Then, we explain the requirements of cybersecurity. Further, we discuss the devastation in the absence of cyber security. We also explain the cybersecurity deployments and techniques under cybersecurity. Then, in the end, some future research directions for cybersecurity are provided.

关键词： Authorization Smart cities Systems architecture Cyberspace Loss measurement Fraud Computer security Protection Smart devices Business

来源：评论

学校读者我要写书评

暂无评论

Usage of specific attention improves change point detection

arXiv

引用

arXiv 2022年

作者： Dmitrienko, Anna Romanenkova, Evgenia Zaytsev, Alexey Institute for Information and Transmission Problems Moscow Russia Skolkovo Institute of Science and Technology Moscow Russia Moscow Institute of Physics and Technology Moscow Russia Institution of Russian Academy of Sciences Dorodnicyn Computing Centre Moscow Russia

The change point is a moment of an abrupt alteration in the data distribution. Current methods for change point detection are based on recurrent neural methods suitable for sequential data. However, recent works show that transformers based on attention mechanisms perform better than standard recurrent models for many tasks. The most benefit is noticeable in the case of longer sequences. In this paper, we investigate different attentions for the change point detection task and proposed specific form of attention related to the task at hand. We show that using a special form of attention outperforms state-of-the-art results. © 2022, CC0.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LinNet: linear network for efficient point cloud representation learning 24

LinNet: linear network for efficient point cloud representat...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hao Deng Kunlei Jing Shengmei Cheng Cheng Liu Jiawei Ru Jiang Bo Lin Wang State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University and Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality School of Software Engineering Xi'an Jiaotong University and Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9798331314385

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of down-sampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Neural Network Classifier Interfaced Skin Cancer Identification for Medical Diagnosis System

Machine Learning Neural Network Classifier Interfaced Skin C...

引用

Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： Chempavathy B Kavitha Veerappan Jose P Shaji. K. A. Theodore R. Rajasree S. Gnana Prasanna Dept. of CSE New Horizon College of Engineering Bangalore Dept. of ECE Kongu Engineering College Erode India Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India College of Computing and Information Science University of Technology and Applied Sciences Sultanate of Oman Dept. of ECE Chennai Institution of Technology Chennai India Dept. of CSE St. Martins Engineering College Secunderabad Telangana

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Skin cancer, which lethal, is one of the top three tumours caused by DNA damage. This damaged DNA causes cells to grow uncontrolled, and they are currently growing swiftly. Numerous trainings performed on the automatic diagnosis of cancer in images of skin lesions. Analysis of these images is rather challenging, though, because of some disruptive factor like light observations from the skin's surface, differences in color enlightenment, and different forms and dimensions of the lesions. The accuracy and skill of analyzers in the early stages improved by machine learning (ML) based autonomous skin cancer diagnosis. A Deep Convolutional Neural Network (DCNN) model for identifying malignant and benign skin lesions is presented in this research. Applying a bilateral filter as the initial step in preprocessing eliminates noise and artefacts. The second phase involves utilizing U-Net to segment the input images and GLCM to extract features that assist with correct categorization. Data classification, the third phase, increases the quantity of images and improves classification precision. This work implements a skin cancer detection model using the ISIC dataset, which contains a large collection of medical images for training and evaluating ML algorithms. Proposed DCNN model is more dependable and resilient, according to the results. The training accuracy is 95% and the training loss is 0.01 after 35 epochs.

关键词： Training Accuracy Image color analysis DNA Machine learning Feature extraction Skin Lesions Medical diagnostic imaging Skin cancer

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Personalized Precision Medicine for Cancer Treatment

A Deep Learning Approach for Personalized Precision Medicine...

引用

Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Kamal Upreti Sambhajiraje Patil Gaurav Agrawal Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science CHRIST (Deemed to be University) Ghaziabad India Centre for Online Learning Dr. D. Y. Patil Vidyapeeth Pune India Department of Computer Science Engineering (AI) KIET Group of Institution Ghaziabad India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Current research combines multiple machine learning models to provide comprehensive insights into tailor-made medicine for lung cancer. The study identified micropatterns of malignant disorders using a dataset of 3222 CT and MRI scans using VGG16 architecture, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). A comprehensive understanding of the characteristics of pulmonary arteries is obtained. After extensive evaluation, the models exhibit exceptional accuracy and precision, with VGG16 performing particularly well. Confusion matrices provide comprehensive insight into model predictions. This work provides important new information in the field of personalized precision medicine through the potential of standardized methods for lung cancer diagnosis and treatment. Future research should focus on sample design they will be enhanced, and their application will be extended to multiple datasets.

关键词： Support vector machines Accuracy Precision medicine Magnetic resonance imaging Computed tomography Confusion matrices Lung cancer Nearest neighbor methods Feature extraction Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Cloud computing based Smart Traffic Management System with Priority Switching for Health Care Services

Cloud Computing based Smart Traffic Management System with P...

引用

Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： X.S. Asha Shiny D. Ravikumar A. Chinnasamy S. Hemavathi Department of Computer Science and Engineering Nalla Malla Reddy Engineering College (Autonomous Institution) Hyderabad Telangana India Department of Electronics and Communication Engineering Kings Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

Regular cities can be transformed into intelligent structures by leveraging information and communication technologies. Innovative city development could be significantly impacted by the Internet of Things paradigm, commonly called cloud computing. All urban locations have a lot of traffic. In this study, Internet-of- Things (IoT) based system is proposed for health care services to organize and to establish the traffic signaling and pick up a route under which road congestion can be administrated. Vehicle accident contraventions are identified by the traffic officers using an online service that is hierarchically carefully monitored or constrained. However, the suggested approach is generic and may be utilized in any major metropolis without losing its generality. Traffic lights linked to cameras in metropolitan areas can be upgraded by connecting to IoT. During a pandemic, this approach is precious. The police can easily regulate traffic from their homes using their cell phones and identify defaulters. The suggested technique aids in the separation of ambulance and rescue engines from ordinary traffic.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated Defect Detection in Electronic Components using Convolutional Neural Networks

Automated Defect Detection in Electronic Components using Co...

引用

International Conference on science technology Engineering and Management (ICONSTEM)

作者： A. Shobanadevi Sreekanth Kottu K R Senthil Kumar K. Amudha K. Praveena R. Venkatesh Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science & Engineering Mallareddy University Hyderabad Telangana India Department of Mechanical Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Science and Humanities-Physics R.M.D. Engineering College (An Autonomous Institution) Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Andhra Pradesh India Department of Physics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence.

关键词： Training Productivity Process control Quality control Streaming media Mathematics Manufacturing

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：