检索结果-内蒙古大学图书馆

Enhancing wireless sensing via a target-mounted intelligent reflecting surface

National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

Towards Intelligent Attendance Monitoring for Scalable Organ...

引用

International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Srivastava, Akhilesh Kumar Mishra, Chandrahas Mishra, Anurag Srivastava, Atul Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science KIET Group of Institutions Ghaziabad India Amity School of Engineering and Technology AUUP Lucknow India

ISBN: (纸本)9789819706402

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1165-1186页

作者： Ali, Rifaqat Pal, Arup Kumar Kumari, Saru Sangaiah, Arun Kumar Li, Xiong Wu, Fan Dhanbad826004 India Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut250004 India School of Computing Science and Engineering VIT University Vellore Tamil Nadu632014 India School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Xiamen Institute of Technology Xiamen361021 China

With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise information is vital for the proper diagnosis procedure. So, authentication protocol is one of the efficient mechanisms to deal with trustworthy and authentic users. Several authentication protocols have been proposed in WMSNs environment. However, the most of these protocols are so susceptible to security threats and not suitable for practical use. In this article, recently proposed Amin et al.’s authentication scheme is reviewed and some vulnerabilities like off-line password guessing attack, user impersonation attack, known session-key temporary information attack, the revelation of secret parameters, and identity guessing attack are pointed out. To overcome all the above mentioned vulnerabilities, we have proposed an enhanced three-factor based remote user authentication protocol in WMSNs environment. Further, the proposed protocol is validated using Burrows–Abadi–Needham logic and then simulated using Automated Validation of Internet Security Protocols and Applications tool. Moreover, the security analysis ensures that the proposed protocol is well protected from various types of malicious attacks. In addition, the performance evaluation shows better efficiency and suitability of our protocol over other related protocols. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

引用

CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

来源：评论

学校读者我要写书评

暂无评论

Spatial data trusts:an emerging governance framework for sharing spatial data

引用

International Journal of Digital Earth 2023年第1期16卷 1607-1639页

作者： Nenad Radosevic Matt Duckham Mohammad Saiedur Rahaman Serene Ho Katherine Williams Tanzima Hashem Yaguang Tao School of Science RMIT UniversityMelbourneAustralia School of Computing Technologies RMIT UniversityMelbourneAustralia Frontier SI MelbourneAustralia Department of Computer Science and Engineering Bangladesh University of Engineering and TechnologyDhakaBangladesh

Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data *** Trusts combine the technical infrastructure for sharing data with the governance framework of a legal *** concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data *** paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial *** work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data *** conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

关键词： Data governance spatial information location privacy indigenous data sovereignty spatial data quality

来源：评论

学校读者我要写书评

暂无评论

Impact of COVID-19 on People 1

引用

4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Nandal, Neha Tanwar, Rohit Saketh, Meduri Pilania, Urmila Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Technology Manav Rachna University Faridabad India

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

COVID-19 affected each and every sector of human life. The food sector, security, employment, agriculture, etc. converged a lot during the pandemic. The coronavirus (COVID-19) pandemic has had a significant impact on the world. The virus was first identified in Wuhan, China, in December 2019, and has since spread to nearly every country. The World Health Organization declared it a pandemic on March 11, 2020. As of January 2021, the virus has infected over 89 million people and resulted in over 1.9 million deaths globally. The pandemic has had wide-ranging effects on society, including health impacts, economic consequences, and changes to daily life and social interactions. Many countries have implemented measures such as lockdowns, travel restrictions, and mask mandates in an effort to slow the spread of the virus. The impact on the healthcare system was significant due to the overwhelming number of hospitalizations and intensive care needs in some countries. The economy took a hit as well as many businesses closed and job loss became widespread. This review provides insight into the impact of the coronavirus on people and how different measures have been taken up to have a control over this pandemic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer's Classification

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer'...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 2915-2931页

作者： S.Vijayalakshmi S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： Vegetation mapping deep learning machine learning remote sensing data image processing

来源：评论

学校读者我要写书评

暂无评论

Generating animatable 3D cartoon faces from single portraits

引用

虚拟现实与智能硬件（中英文） 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU Yu-Kun LAI Department of Electrical Engineering and Computer Sciences University of California BerkeleyBerkeley CA 94704USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D ***,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being *** study presents a novel framework for generating animatable 3D cartoon faces from a single portrait *** First,we transferred an input real-world portrait to a stylized cartoon image using *** then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed *** two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark ***,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation *** Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity ***,we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

来源：评论

学校读者我要写书评

暂无评论

An effective deep learning based Idrcnn and Bdc-Lstm models for complex word identification and synonym generation

引用

International Journal of Information Technology (Singapore) 2025年第3期17卷 1703-1716页

作者： RajyaLakshmi, Tamma Vinta, Surendra Reddy Department of Computer Science Pondicherry University Puducherry Kalapet 605014 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati 522237 India

Finding words in a document that the average reader could find challenging to understand is usually the first step in identifying complex words in the text. After you've determined which terms are difficult, you might create synonyms or offer substitutes that would be simpler to read to improve the text's accessibility. As a result, inaccurate synonyms unrelated to the context may be created. In this field of study, combining the recognition of difficult words with the creation of synonyms is novel. We provide a unique deep-learning technique and algorithm to address the problems in the prior research's difficult word identification and synonym-generating tasks. This research is evaluated on the Complex Word Identification dataset. Initially, stop word removal, tokenization, punctuation removal, lowercasing, and stemming are performed in the pre-processing phase. After that, utilize the FastText method to perform word embedding on pre-processed input text. Then, the attributes of morphological and linguistics from embedded text are retrieved to enhance the classification performance. Then, recognize the complex words from the given text utilizing the Improved Deep Residual Convolutional Neural Network (IDRCNN) approach and also employ the Single Candidate Optimization Algorithm (SCOA) to enhance the classification performance by tuning and optimizing the parameters of the technique. Finally, utilize the Bi-Directional Convolutional Long Short Term Memory (BDC-LSTM) for synonyms generation for those identified words. The experimental results demonstrate that the proposed approach performs better than the alternative approaches in terms of entity synonym set construction performance. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Complex word improved deep residual convolutional neural network Fastext Feature extraction Identification Stop word removal Synonyms generation task Word embedding

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：