检索结果-内蒙古大学图书馆

Harnessing Blockchain to Address Plasma Donation Network Challenges

computers, Materials & Continua 2023年第7期76卷 631-646页

作者： Shivani Batra Mohammad Zubair Khan Gatish Priyadarshi Ayman Noor Talal H.Noor Namrata Sukhija Prakash Srivastava Department of Computer Science and Engineering SRM UniversityDelhi-NCR1310029India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia Department of Computer Science and Engineering KIET Group of InstitutionsDelhi-NCRGhaziabad201206India College of Computer Science and Engineering Taibah UniversityMadinahSaudi Arabia College of Computer Science and Engineering Taibah UniversityYanbuMadinahSaudi Arabia Department of Computer Science and Engineering Banasthali VidyapithRajasthan304022India Department of Computer Science and Engineering Graphic Era(Deemed to Be University)Dehradun248002India

Plasma therapy is an extensively used treatment for critically unwell *** this procedure,a legitimate plasma donor who can continue to supply plasma after healing is ***,significant dangers are associated with supply management,such as the ambiguous provenance of plasma and the spread of infected or subpar blood into medicinal ***,from an ideological standpoint,less powerful people may be exploited throughout the contribution ***,there is a danger to the logistics system because there are now just some plasma *** research intends to investigate the blockchain-based solution for blood plasma to facilitate authentic plasma *** parameters,including electronic identification,chain code,and certified ledgers,have the potential to exert a substantial,profound influence on the distribution and implementation process of blood *** understand the practical ramifications of blockchain,the current study provides a proof of concept approach that aims to simulate the procedural code of modern plasma distribution ecosystems using a blockchain-based *** agent-based modeling used in the testing and evaluation mimics the supply chain to assess the blockchain’s feasibility,advantages,and constraints for the plasma.

关键词： Blockchain hyperledger fabric information visibility plasma donation network plasma quality

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

引用

Recent Advances in computer science and Communications 2024年第3期17卷 20-31页

作者： Srivastava, Somya Verma, Seema Das, Nripendra Narayan Sharma, Shraddha Dubey, Gaurav Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Guru Gobind Singh Indraprastha University Delhi India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science Pandit Deendayal Energy University Gujarat India Department of Computer Science KIET Group of Institutions Ghaziabad India

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models. © 2024 Bentham science Publishers.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Data-driven output regulation control for constrained linear systems

引用

science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of VGG16, Inception V4, AlexNet, and ResNet 50 for Plant Disease Identification 15

Comparative analysis of VGG16, Inception V4, AlexNet, and Re...

引用

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

作者： Arora, Utkarsh Mishra, Upendra Singh, Samaira Singh, Vaibhav Kiet Group of Institutions Department of Computer Science & Engineering Ghaziabad India Kiet Group of Institutions Department of Computer Science & Engineering Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350370249

The increasing incidence of leaf diseases in agricultural crops has necessitated the development of efficient and automated detection methods to safeguard crop health and maximize yield. In the field of artificial intelligence, deep learning has emerged as a major computing paradigm with immense potential for solving computer vision problems. CNN is a type of deep learning architecture that is designed to provide accurate results for tasks such as image recognition and object detection. This study utilizes state-of-the-art ResNet-50 model to identify and classify plant leaf diseases combined with transfer learning to enhance its accuracy and efficiency. The proposed methodology involves dataset curation, preprocessing, and finetuning of the ResNet-50 architecture. We have tested and assessed the performance of VGG16, Inception V4, AlexNet, and ResNet-50. Upon examining the results, ResNet50 has demonstrated good outcomes in the identification and categorization of leaf diseases and outperformed alternative architectures with an accuracy of 99.7%. © 2024 IEEE.

关键词： CNN Deep Learning Plant leaf disease ResNet-50

来源：评论

学校读者我要写书评

暂无评论

Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

引用

High-Confidence Computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

来源：评论

学校读者我要写书评

暂无评论

Semantic Segmentation with Attention Dense U-Net for Lung Extraction from X-ray Images 6

Semantic Segmentation with Attention Dense U-Net for Lung Ex...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Mahmud Auvy, Akib Al Zannah, Rafiatul Mahbub-E-Elahi Sharif, Shezhan Mahmud, Washik Al Noor, Jannatun Research Group Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Deep learning and digital image processing are crucial in medical imaging research. Lung segmentation is particularly challenging, demanding accurate differentiation of complex structures, sophisticated algorithms, and deep learning models for reliable results. In our research, We used the Shen-zhen chest X-ray dataset, comprising 566 frontal chest X-rays focused on pulmonary tuberculosis. This dataset, compiled by the Guangdong Medical College and Shenzhen No. 3 People's Hospital, was released by the United States National Library of Medicine. Images, captured using a Philips DR Digital Diagnostic system, were resized from $3000 \times 3000$ pixels to $512 \times 512$ pixels for computer-aided diagnosis research. We initially used the Attention Dense U-Net to extract lungs from chest X-rays, leveraging attention mechanisms to focus on relevant features and dense blocks to improve feature reuse. Our goal is to assess its performance for chest X-ray segmentation, comparing it with other deep learning models due to limited research in this area. So, the model is compared with four other variants of U-Net architectures to segment lung pixels from an X-ray image. After implementing the approach using a benchmark dataset and comparing it to other existing architectures, we present that Attention Dense U-Net gives the best accuracy for all given parameters, with a result of accuracy: 97.48%, Dice coefficient: 94.87%, and IoU: 93.87%. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Bi-GAE:A Bidirectional Generative Auto-Encoder

引用

Journal of computer science & Technology 2023年第3期38卷 626-643页

作者：华勤胡瀚文钱诗友杨定裕曹健 Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Alibaba Group Shanghai 201210China

Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and simultaneously optimize the bidirectional mapping between the encoder and the decoder/generator while ensuing convergence. Most existing auto-encoders cannot automatically trade off bidirectional mapping. In this work, we propose Bi-GAE, an unsupervised bidirectional generative auto-encoder based on bidirectional generative adversarial network (BiGAN). First, we introduce two terms that enhance information expansion in decoding to follow human visual models and to improve semantic-relevant feature representation capability in encoding. Furthermore, we embed a generative adversarial network (GAN) to improve representation while ensuring convergence. The experimental results show that Bi-GAE achieves competitive results in both generation and representation with stable convergence. Compared with its counterparts, the representational power of Bi-GAE improves the classification accuracy of high-resolution images by about 8.09%. In addition, Bi-GAE increases structural similarity index measure (SSIM) by 0.045, and decreases Fréchet inception distance (FID) by in the reconstruction of 512*512 images.

关键词： auto-encoder adversarial network image reconstruction and generation feature representation

来源：评论

学校读者我要写书评

暂无评论

IoT Cryptography: A Secure Communication Framework for the Internet of Things

IoT Cryptography: A Secure Communication Framework for the I...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rishi, Midhvana Dubey, Vaibhav Bhardwaj, Mukesh Kumar Singh, Mahesh Kumar Dronacharya Group of Institutions Department of Computer Science Engineering India

ISBN: (纸本)9798350375237

IoT is a lightning-fast technology that uses clever objects or stuff that speak uncomplicatedly to the work of our daily lives. Smart homes, wearables, connected automobiles, vibrant urban communities;savvy retail, agribusiness, healthcare, and other areas are some of the most well-known applications of IoT. This document provides an overview of IoT Crypto a safe platform Internet of Things communication. Typically, they have elements from Internet of Things (IoT), such as the device functionality they need, the requirement to restrict the amount of information sent, and the ability to connect to the Internet. With real business relationships in mind, Innovative lightweight authentication setup and trust concepts are provided by IoT-Crypto. Include a secure communication protocol that only makes use of one DTLS connection that is encrypted. This article discusses and presents the unique components and nuanced execution details of IoT crypto in terms of essentially equivalent arrangements. Post-implementation testing and analysis of the IoT encrypted network have proven its accuracy and safety. Additionally, a test network was conducted to determine if the BLE IPSP profile and coding standards are suitable for IoT. Based on these results, the significance of future research was discussed. The IoT paradigm and communication protocols are introduced at the outset of this work and then compare countermeasures for a few security vulnerabilities at each tier of the IoT model to protect such a large and diverse company. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

引用

Recent Advances in computer science and Communications 2024年第5期17卷 12-23页

作者： Kumar, Dharmendra Solanki, Anil Kumar Ahlawat, Anil Kumar Department of Computer Science & Engineering KIET Group of Institutions NCR Delhi Ghaziabad India Department of Computer Science & Engineering Bundelkhand Institute of Engineering and Technology Jhansi India Affiliated to Dr. APJ Abdul Kalam Technical University Lucknow India

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field. © 2024 Bentham science Publishers.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai201804 China Ant Group Shanghai200122 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Fudan University School of Computer Science Shanghai200438 China Tongji University Department of Control Science and Engineering Shanghai201804 China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne3122 Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this article, we propose a hypergraph learning framework named learning from heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterward, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experimental results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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：