检索结果-内蒙古大学图书馆

Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

computer Systems Science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

来源：评论

学校读者我要写书评

暂无评论

Privacy enhanced course recommendations through deep learning in Federated Learning environments

引用

International Journal of information technology (Singapore) 2025年第1期17卷 629-635页

作者： Kolli, Chandra Sekhar Seelamanthula, Sreenivasu Reddy V, Venkata Krishna Babu, Padamata Ramesh Reddy, Mule Rama Krishna Gumpina, Babu Rao Department of Information Technology Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta India Department of Compute Science GITAM School of Science GITAM (Deemed to be University) Andhra Pradesh Visakhapatnam India

The increasing concerns around data security and privacy among users have significantly pushed the interest of the research community towards developing privacy-preserving recommendation systems. Amidst this backdrop, our study introduces a novel course recommendation methodology leveraging Federated Learning (FL) coupled with advanced Deep Learning techniques. This method executes the recommendation process across local nodes through several stages, including agglomerative matrix formulation, course clustering, bi-level matching, identification of learner-preferred courses, and ultimately, course recommendation. Notably, course clustering is achieved through Deep Fuzzy Clustering (DFC), while Deep Convolutional Neural Networks (DCNN) are employed for the recommendation phase. The efficacy of our DFC-DCNN-FL approach is rigorously evaluated based on several metrics: accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP). The results demonstrate remarkable performance with scores of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Course recommendation Deep fuzzy clustering Deep learning Federated learning Privacy preservation

来源：评论

学校读者我要写书评

暂无评论

Designing an Attribute-Based Encryption Scheme with an Enhanced Anonymity Model for Privacy Protection in E-Health

引用

SN computer Science 2024年第2期5卷 203页

作者： Zala, Kirtirajsinh Thakkar, Hiren Kumar Dholakia, Neel Shukla, Madhu Thumar, Deep Department of Information Technology Marwadi University Gujarat Rajkot 360006 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar 382007 India Department of Computer Engineering Marwadi University Gujarat Rajkot 360006 India Faculty of Engineering Marwadi Education Foundations Gujarat Rajkot 360006 India

The medical field and the health care industry generate large volumes of data that are ultimately beneficial to society. Yet, the exposure of sensitive aspects might jeopardize this data generation. Exposure to Healthcare information made available via the internet with the intention of benefiting healthcare professionals presents a challenge for researchers in terms of privacy and security concerns. This access is intended to benefit the medical community. With growing technology, medical data on the cloud are subject to unanticipated dangers, and the threat landscape appears resilient with sensitive qualities. Organizations fail to keep their reputations and are unable to maintain public trust in our modern day. The severity of advanced security threats compromises patient data privacy and healthcare unit security. Many studies and practitioners’ fruitful approaches gave up healing resolutions, but the requirement for a perfect solution remains unsatisfactory. In this research, we provide a solution for dealing with security challenges in healthcare administration. We present a hybrid system that combines sensitive attribute access primitives with enhanced attribute-based encryption and anonymity methods. The proposed approach electronic health records design model is closely linked with the proposed approach and system scenario, forming a comprehensive blueprint for our healthcare technology solution. In terms of completion time when user instance is upto 500, the practical flexible ABE instances method outperforms the other approaches such as real-time operational data base extraction–transformation–loading, and long short-term memory to recurrent neural network by a significant margin. This method offers the quick encryption (7.5 s), decryption (5.4 s), and reduced memory usage (5361 s). A cloud sim simulator is used to evaluate the proposed mechanism’s performance, encryption, decryption, and memory usage. The latest model with better hardware and software opt

关键词： Anonymity Attribute-based encryption (ABE) Cloud computing E-health Privacy Secure hash algorithm (SHA-1)

来源：评论

学校读者我要写书评

暂无评论

Designing the Prediction Protocol based on Sparrow Search Optimization Algorithm with Vibrational Auto Encoder in E-Healthcare 5

Designing the Prediction Protocol based on Sparrow Search Op...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Saraswathi, R. Pavithra, D. Gokuldev, S. Jebakumar, Immanuel D. Tharageswari, K. Sns College of Engineering Department of Computer Science and Technology Coimbatore India Dr.N.G.P. Institute of Technology Department of Information Technology Coimbatore India INurture Education Solutions Zonal Head South-Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665474672

Since knowing the likelihood of contracting an illness is crucial to maintaining a healthy lifestyle, disease prediction systems have made significant contributions to people's daily lives. New data mining methods have allowed for the development of illness forecasting models. In particular, Single-Layer Perceptron may be used to extract useful information for building a disease prediction system from the mountain of medical data created daily. Many obstacles, such as information security and prediction efficiency, may restrict the practical application of the illness prediction system, despite its considerable promise. Therefore, determining the illness and safeguarding the data are the primary goals of the study. Many forms of preprocessing, including resizing, augmenting, and min-max based normalisation, are used to the raw data acquired by IoT devices. This process culminates in the use of a classifier based on the sparrow search optimization algorithm (SSOA) and the vibrational auto encoder (VAE) to distinguish between unhealthy and healthy occurrences. Finally, the Smartphone notifies the staff and administration in the event of a sickness incident. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Advanced Source Privacy: DQ-NN and Phantom Routing in IoT-Integrated Multiple-Source and Destination Wireless Sensor Networks 15

Advanced Source Privacy: DQ-NN and Phantom Routing in IoT-In...

引用

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

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru560001 India Manipal Institute of Technology Department of Information Technology Bangalore India Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350370249

Internet of Things (IoT) and Wireless Sensor Networks (WSNs) have brought about revolutionary opportunities for many areas, they have also raised serious security issues, especially with relation to source node location privacy in IoT-enabled WSNs. In response, we put out a brand-new hybrid Deep Q-learning Neural Network (DQ-NN) strategy designed specifically for Source Location Privacy (SLP) in Internet of Things (IoT)-enabled WSNs that use phantom routing. Through the strategic selection of phantom nodes, our method establishes various routing paths from source to sink nodes via these phantoms, taking into account factors such as neighbour lists, energy levels, distances, and trust heterogeneity. The use of DQ-NN, a combination of Deep Q-learning Network (DQN) and Deep Neural Network (DNN), is essential to our approach since it guarantees source node location privacy protection in addition to effective and reliable data transfer through IoT-enabled WSNs, strengthening the privacy and security environment of IoT networks and facilitating easy integration with everyday life. The suggested DQ-NN performs better than other current methods. © 2024 IEEE.

关键词： deep q learning neural network deep q neural networks Internet of things phantom routing source location privacy

来源：评论

学校读者我要写书评

暂无评论

2SWUNet:small window SWin UNet based on tansformer for building extraction from high-resolution remote sensing images

引用

Optoelectronics Letters 2024年第10期20卷 599-606页

作者： YU Jiamin CHAN Sixian LEI Yanjing WU Wei WANG Yuan ZHOU Xiaolong College of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Key Laboratory of Meteorological Disaster(KLME) Ministry of Education&Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China College of Geographic Information Modern Industry Zhejiang University of TechnologyHangzhou310023China College of Electrical and Information Engineering Quzhou UniversityQuzhou324000China

Models dedicated to building long-range dependencies often exhibit degraded performance when transferred to remote sensing *** transformer(Vi T)is a new paradigm in computer vision that uses multi-head self-attention(MSA)rather than convolution as the main computational module,with global modeling ***,its performance on small datasets is usually far inferior to that of convolutional neural networks(CNNs).In this work,we propose a small window SWin UNet(2SWUNet)for building extraction from high-resolution remote sensing ***,the 2SWUNet is trained based on swin transformer by designing a fully symmetric encoder-decoder U-shaped ***,to construct a reasonable U-shaped architecture for building extraction from high-resolution remote sensing images,different forms of patch expansion are explored to simulate up-sampling operations and recover feature map ***,the small window-based multi-head self-attention(W-MSA)is designed to reduce the computational and memory burden,which is more appropriate for the features of remote sensing ***,the pre-training mechanism is advanced to make up for the lack of decoder ***,comparison experiments with other mainstream CNNs and Vi Ts validate the superiority of the proposed model.

关键词： Net computer resolution

来源：评论

学校读者我要写书评

暂无评论

Investigating Innovative User Interfaces for Data Visualization and Analysis 2

Investigating Innovative User Interfaces for Data Visualizat...

引用

2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Kumar, V. Mathan Balakumar, P. Katikar, Shilpa M. Singh, Satendra Yadav, Rakesh Kumar Kamalraj, R. Academy of Higher Education Department of Computer Science Karpagam Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350308846

This paper will explore current studies and developments in consumer interfaces for records visualization and analysis. Mainly that specialize in how innovative consumer interfaces can make choices better than present-day ones. The studies will look at various consumer interfaces, discover and explore their strengths and weaknesses, and discuss opportunities for further studies and innovation in this location. Examples of revolutionary consumer interfaces discussed within the paper might be evaluated for their capability to grow a person's information and engagement with records, lessen the cognitive load, improve enjoyment, and better combine documents with meaningful visualizations. The consequences of this research could be priceless in terms of assisting in designing effective and person-pleasant document visualization consumer interfaces on multiple systems, including desktop, mobile devices, and virtual pills. © 2023 IEEE.

关键词： Consumer Devices Mobile Pleasant Visualization

来源：评论

学校读者我要写书评

暂无评论

Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

引用

computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential Benefits of Machine Learning for Optimizing Real-Time Data Analysis 3

Exploring the Potential Benefits of Machine Learning for Opt...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Ravindra Sakhare, Nitin N. Thangarasu, N. Senthilkumar, K.K. Wawage, Pawan S. Rengarajan, A. Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms. © 2023 IEEE.

关键词： Machine 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：