检索结果-内蒙古大学图书馆

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

来源：评论

学校读者我要写书评

暂无评论

Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

A Novel Secure Scan Design Based on Delayed Physical Unclonable Function

引用

computers, Materials & Continua 2023年第3期74卷 6605-6622页

作者： Weizheng Wang Xingxing Gong Xiangqi Wang Gwang-jun Kim Fayez Alqahtani Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Information Science and Engineering Hunan Women’s UniversityChangsha410138China College of Mathematics and Computational Science Hunan First Normal UniversityChangsha410138China Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.

关键词： Cryptographic chips scan testing scan-based attacks hardware security PUF

来源：评论

学校读者我要写书评

暂无评论

A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

An asynchronous discontinuous Galerkin method for combustion simulations

arXiv

引用

arXiv 2025年

作者： Arumugam, Aswin Kumar Aditya, Konduri Department of Computational and Data Sciences Indian Institute of Science KA Bengaluru560012 India

The discontinuous Galerkin (DG) method has been widely considered in recent years to develop scalable flow solvers for its ability to handle discontinuities, such as shocks and detonations, with greater accuracy and high arithmetic intensity. However, its scalability is severely affected by communication bottlenecks that arise from data movement and synchronization at extreme scales. Recently, an asynchronous discontinuous Galerkin (ADG) method was proposed to reduce communication overhead by either relaxing synchronization or avoiding communication between processing elements (PEs). The numerical properties of the ADG method were verified by solving simple one-dimensional partial differential equations. In this study, the application of the ADG method is extended to complex chemically reacting flows, particularly to evaluate its efficacy in capturing flame fronts and detonations. New asynchrony-tolerant weighted essentially non-oscillatory (AT-WENO) limiters are derived to accurately capture flow discontinuities with communication delays at the PE boundaries. The numerical accuracy of the ADG method is verified on the premixed spontaneous ignition of a fuel-lean mixture with detailed chemistry. The progression of the flame front is accurately represented by the ADG method, and the numerical errors incurred at the PE boundaries turn out to be insignificant. The efficacy of the AT-WENO limiters in capturing discontinuities is demonstrated by the propagation of a detonation wave. The findings of this study serve as a basis for the development of highly scalable discontinuous Galerkin-based solvers for combustion simulations on massively parallel supercomputers. © 2025, CC BY.

关键词： Detonation

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-agency Collaboration Medical Images Analysis and Classification System based on Federated Learning 24

Research on Multi-agency Collaboration Medical Images Analys...

引用

2024 International Conference on Biomedicine and Intelligent Technology, ICBIT 2024

作者： Huang, Shijia Diao, Su Wan, Yajie Song, Chaoda Fu Foundation School of Engineering and Applied Science Columbia University United States Department of Industrial & Systems Engineering Auburn University United States Department of Computer Science Brown University United States School of Engineering Department of Computer and Data Sciences United States

ISBN: (纸本)9798400710063

In the field of medical image analysis, with the growth of data volume and the increasing demand for privacy protection, traditional centralized learning methods are facing the problems of data silos and privacy leakage. Therefore, federated learning, as an emerging distributed machine learning framework, provides a solution to protect data privacy while effectively utilizing decentralized data. The purpose of this study is to develop a federated learning-based medical image analysis and classification system to solve the data isolation and privacy problems existing in traditional methods. This study uses a distributed federated learning framework that allows multiple healthcare organizations to share updates to their models instead of raw data. data privacy protection is further enhanced through the use of differential privacy and secure multi-party computation techniques. In addition, a deep convolutional neural network (CNN) is used to improve the accuracy of feature extraction and classification of medical images. Experimental analysis shows that the system can significantly improve the classification accuracy of medical images. Specifically, our system achieved a 15% improvement in classification accuracy compared to traditional centralized methods. Additionally, the implementation of differential privacy and secure multi-party computation resulted in a privacy leakage reduction of 25%. These results verify the application potential of federated learning in the field of medical image processing, demonstrating superior performance and enhanced privacy protection compared to traditional centralized learning methods. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Impact of Cloud Computing in Digital Healthcare 5

Impact of Cloud Computing in Digital Healthcare

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Shreya Sharma, Simran Sharma, Arvind K. Kaur, Rajinder Anju Shoolini University Yogananda School of AI Computers and Data Science Himachal Pradesh Solan India Trinity College Department of Computer Sciences Punjab Jalandhar India

ISBN: (纸本)9798350383522

The continuous and sustainable development in healthcare is taking place from decades and with prompt technological advancement in engineering nowadays the healthcare can't be imagined without the advent tools like Cloud Computing, Internet of Medical Things etc., for quality medical practices, electronic medical records management, patient control on private health information, tracking/tracing of pharmaceutical shipments etc. The eminent cloud-computing not only spreading its feet but also exploring new dimensions in healthcare to make the digital-health possible with its remarkable fundamental benefits in quality healthcare application development. The paper is focused especially on cloud-computing, its applications and services more specifically in healthcare towards best medical practices. © 2024 IEEE.

关键词： Cloud Computing Digital Health Electronic Health Records Healthcare Privacy Telemedicine Wearable Device

来源：评论

学校读者我要写书评

暂无评论

Depth Separation in Norm-Bounded Infinite-Width Neural Networks 37

Depth Separation in Norm-Bounded Infinite-Width Neural Netwo...

引用

37th Annual Conference on Learning Theory, COLT 2024

作者： Parkinson, Suzanna Ongie, Greg Willett, Rebecca Shamir, Ohad Srebro, Nathan Committee on Computational and Applied Mathematics University of Chicago United States Department of Mathematical and Statistical Sciences Marquette University United States Department of Statistics Department of Computer Science University of Chicago United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Israel Toyota Technological Institute Chicago United States

We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared 2-norm of the weights (sum of squares of all weights in the network). Whereas previous depth separation results focused on separation in terms of width, such results do not give insight into whether depth determines if it is possible to learn a network that generalizes well even when the network width is unbounded. Here, we study separation in terms of the sample complexity required for learnability. Specifically, we show that there are functions that are learnable with sample complexity polynomial in the input dimension by norm-controlled depth-3 ReLU networks, yet are not learnable with sub-exponential sample complexity by norm-controlled depth-2 ReLU networks (with any value for the norm). We also show that a similar statement in the reverse direction is not possible: any function learnable with polynomial sample complexity by a norm-controlled depth-2 ReLU network with infinite width is also learnable with polynomial sample complexity by a norm-controlled depth-3 ReLU network. © 2024 S. Parkinson, G. Ongie, R. Willett, O. Shamir & N. Srebro.

关键词： Source separation

来源：评论

学校读者我要写书评

暂无评论

MediGuard:A Survey on Security Attacks in Blockchain-IoT Ecosystems for e-Healthcare Applications

引用

computers, Materials & Continua 2025年第6期83卷 3975-4029页

作者： Shrabani Sutradhar Rajesh Bose Sudipta Majumder Arfat Ahmad Khan Sandip Roy Fasee Ullah Deepak Prashar Institute of Engineering and Technology Dibrugarh UniversityDibrugarh786004AssamIndia Departments of Computational Sciences Brainware UniversityKolkata700125West BengalIndia Department of Computer Science&Engineering JIS UniversityKolkata700109West BengalIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610Perak Darul RidzuanMalaysia Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411PunjabIndia Jadara University Research Center Jadara UniversityIrbid21110Jordan

Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare *** digitization has major advantages,mainly enhancing the security barriers of the green tree *** this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system *** addition,we considered several solutions proposed by thousands of researchers *** results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and ***,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power *** propose a multi-layered architecture for the future healthcare ***,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these *** thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional *** conducted in-depth comparative studies on different interoperability parameters in the blockchain *** research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and *** the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.

关键词： Blockchain internet of medical things cloud infrastructure cyber-attacks privacy issues

来源：评论

学校读者我要写书评

暂无评论

Human brain computing and brain-inspired intelligence

引用

National science Review 2024年第5期11卷 8-9页

作者： Jianfeng Feng Viktor Jirsa Wenlian Lu Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of Education Shanghai Center for Mathematical Sciences Fudan University Department of Computer Science University of Warwick Zhangjiang Fudan International Innovation Center Institut de Neurosciences des Systèmes Faculté de Medécine

The relationship between structure, neural activity dynamics and intelligent human brain function is complex and multifaceted with deep roots in several academic disciplines. Its understanding is a fundamental goal of brain research [1] and may indicate the way towards the next generation of artificial intelligence [2]. There are major challenges on the way, several of which are addressed in this Special Topic of National science Review.

关键词： intelligence human brain-inspired computing

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：