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Cancellable Multi-Biometric Feature Veins Template Generation Based on SHA-3 Hashing 基于SHA-3哈希的可取消多生物特征静脉模板生成

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2023.030789

Salwa M. Serag Eldin, Ahmed Sedik, Sultan S. Alshamrani, Ahmed M. Ayoup

引用次数: 0

An Optimized Deep Learning Approach for Improving Airline Services 改进航空公司服务的优化深度学习方法

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2023.034399

Shimaa Ouf

引用次数: 0

DSAFF-Net: A Backbone Network Based on Mask R-CNN for Small Object Detection DSAFF-Net:基于掩模R-CNN的小目标检测骨干网络

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2023.027627

Jianqiang Peng, Yifang Zhao, Dengyong Zhang, Feng Li, Arun Kumar Sangaiah

{"title":"DSAFF-Net: A Backbone Network Based on Mask R-CNN for Small Object Detection","authors":"Jianqiang Peng, Yifang Zhao, Dengyong Zhang, Feng Li, Arun Kumar Sangaiah","doi":"10.32604/cmc.2023.027627","DOIUrl":"https://doi.org/10.32604/cmc.2023.027627","url":null,"abstract":"Recently, object detection based on convolutional neural networks (CNNs) has developed rapidly. The backbone networks for basic feature extraction are an important component of the whole detection task. Therefore, we present a new feature extraction strategy in this paper, which name is DSAFF-Net. In this strategy, we design: 1) a sandwich attention feature fusion module (SAFF module). Its purpose is to enhance the semantic information of shallow features and resolution of deep features, which is beneficial to small object detection after feature fusion. 2) to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive field. The method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network (RPN). In the experimental phase, we use the new strategy to extract features. The experiment takes the public dataset of Microsoft Common Objects in Context (MS COCO) object detection and the dataset of Corona Virus Disease 2019 (COVID-19) image classification as the experimental object respectively. The results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%, and small object detection in object detection tasks is improved by 4.0%. © 2023 Tech Science Press. All rights reserved.","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122988547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Power Allocation in NOMA-CR for 5G Enabled IoT Networks 支持5G物联网的NOMA-CR功率分配

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2022.027532

Mohammed Basheri, Mohammad Haseeb Zafar, Imran Khan

{"title":"Power Allocation in NOMA-CR for 5G Enabled IoT Networks","authors":"Mohammed Basheri, Mohammad Haseeb Zafar, Imran Khan","doi":"10.32604/cmc.2022.027532","DOIUrl":"https://doi.org/10.32604/cmc.2022.027532","url":null,"abstract":": In the power domain, non-orthogonal multiple access (NOMA) supports multiple users on the same time-frequency resources, assigns different transmission powers to different users, and differentiates users by user channel gains. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing con-trollable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC) is performed at the receiving end to demodulate the necessary user signals. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the receiver complexity. With the development of microelectronics technology, chip processing capabilities continue to increase, laying the foundation for the practical application of non-orthogonal transmission technology. In NOMA, different users are differentiated by different power levels. Therefore, the power allocation has a considerable impact on the NOMA system performance. To address this issue, the idea of splitting power into two portions, intra-subbands and inter-subbands, is proposed in this study as a useful algorithm. Then, such optimization problems are solved using proportional fair scheduling and water-filling algorithms. Finally, the error propagation was modeled and analyzed for the residual interference. The proposed technique effectively increased the system throughput and performance under various operating settings according to simulation findings. A comparison is performed with existing algorithms for performance evaluation.","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"100 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114123140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports 增强中文投诉报告事实一致性的弱监督抽象摘要

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2023.036178

Ren Tao, Chen Shuang

引用次数: 0

PCATNet: Position-Class Awareness Transformer for Image Captioning PCATNet:用于图像字幕的位置类感知转换器

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2023.037861

Ziwei Tang, Yaohua Yi, Changhui Yu, Aiguo Yin

引用次数: 0

Metaheuristic Optimization for Mobile Robot Navigation Based爋n燩ath燩lanning 基于爋和燩ath燩规划的移动机器人导航元启发式优化

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2022.026672

El-Sayed M. El-kenawy, Zeeshan Shafi Khan, Abdelhameed Ibrahim, Bandar Abdullah Aloyaydi, H. Arafat Ali, Ali E. Takieldeen

引用次数: 0

Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework 基于智能深度学习的多视网膜疾病诊断与分类框架

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2022.023919

Thavavel Vaiyapuri, S. Srinivasan, Mohamed Yacin Sikkandar, T. S. Balaji, Seifedine Kadry, Maytham N. Meqdad, Yun-Seong Nam

引用次数: 4

The Role of Deep Learning in Parking Space Identification and燩rediction燬ystems 深度学习在停车位识别和燩预测燬系统中的作用

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2023.034988

Faizan Rasheed, Y. Saleem, Kok-Lim Alvin Yau, Yung-Wey Chong, Sye Loong Keoh

引用次数: 0

A Blockchain-Based Framework for Secure Storage and Sharing of Resumes 基于区块链的安全存储和共享简历的框架

Computers, Materials & Continua Pub Date : 1900-01-01 DOI: 10.32604/cmc.2022.028284

Huanrong Tang, Changlin Hu, Tianming Liu, Jian-quan Ouyang

引用次数: 0

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