检索结果-内蒙古大学图书馆

DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

来源：评论

学校读者我要写书评

暂无评论

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

引用

CAAI Transactions on Intelligence technology 2024年第2期9卷 452-466页

作者： Xiaoming Wang Yilong Yang Weiru Wang Yuanhua Zhou Yongfeng Yin Zhiguo Gong Department of Computer and Information Science University of MacaoMacaoChina School of Software Beihang UniversityBeijingChina Department of Computer Science and Technology Faculty of Information TechnologyBeijing University of TechnologyBeijingChina School of Foreign Languages Guangzhou Huashang CollegeGuangzhouChina

Robot calligraphy visually reflects the motion capability of robotic *** traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy *** key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data *** this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot *** robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with *** the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is *** calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

关键词： calligraphy synthesis generative adversarial networks Motion learning robot writing

来源：评论

学校读者我要写书评

暂无评论

Adversarial-Learning-Based Taguchi Convolutional Fuzzy Neural Classifier for Images of Lung Cancer

引用

IEEE Access 2024年 12卷 72766-72776页

作者： Lin, Cheng-Jian Lin, Xue-Qian Jhang, Jyun-Yu National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung40401 Taiwan

Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled data, configuring network parameters, and handling excessive network parameters. To address these challenges, in this study, we developed an adversarial-learning-based Taguchi convolutional fuzzy neural classifier (AL-TCFNC) for classifying malignant and benign lung tumors displayed in computed tomography images. In the framework of the developed AL-TCFNC, a fuzzy neural classifier replaces a conventional fully connected network, thereby reducing the number of network parameters and the training duration. To reduce experimental cost and training time, the Taguchi method was used. This method helps to identify the optimal combination of model parameters through a small number of experiments. The transfer learning of models across databases often results in subpar performance because of the paucity of labeled samples. To resolve this problem, we used a combination of maximum mean discrepancy and cross-entropy for adversarial learning with the proposed model. Two data sets, namely the SPIE-AAPM Lung CT Challenge data set and LIDC-IDRI Lung Imaging Research data set, were used to validate the AL-TCFNC model. When the AL-TCFNC model was used for transfer learning, it exhibited an accuracy rate of 89.55% and outperformed other deep learning models in terms of classification performance. © 2013 IEEE.

关键词： Fuzzy neural networks

来源：评论

学校读者我要写书评

暂无评论

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

引用

computers, Materials & Continua 2023年第1期74卷 399-414页

作者： Nora Abdullah Alkhaldi Hanan T.Halawani Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityP.O.Box 400AlAhsa31982Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Automated segmentation of blood vessels in retinal fundus images is essential for medical image *** segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal *** article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)*** proposed GOFED-RBVSC model initially employs contrast enhancement ***,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership *** ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature ***,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the *** performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

关键词： Edge detection blood vessel segmentation retinal fundus images image classification deep learning

来源：评论

学校读者我要写书评

暂无评论

A selective LVQ algorithm for improving instance reduction techniques and its application for text classification

引用

Journal of Intelligent and Fuzzy Systems 2024年第5-6期46卷 11353-11366页

作者： Hayel, Rafa Hindi, Khalil El Hosny, Manar Alharbi, Rawan Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Instance-Based Learning, such as the k Nearest Neighbor (kNN), offers a straightforward and effective solution for text classification. However, as a lazy learner, kNN’s performance heavily relies on the quality and quantity of training instances, often leading to time and space inefficiencies. This challenge has spurred the development of instance-reduction techniques aimed at retaining essential instances and discarding redundant ones. While such trimming optimizes computational demands, it might adversely affect classification accuracy. This study introduces the novel Selective Learning Vector Quantization (SLVQ) algorithm, specifically designed to enhance the performance of datasets reduced through such techniques. Unlike traditional LVQ algorithms that employ random vector weights (codebook vectors), SLVQ utilizes instances selected by the reduction algorithm as the initial weight vectors. Importantly, as these instances often contain nominal values, SLVQ modifies the distances between these nominal values, rather than modifying the values themselves, aiming to improve their representation of the training set. This approach is crucial because nominal attributes are common in real-world datasets and require effective distance measures, such as the Value Difference Measure (VDM), to handle them properly. Therefore, SLVQ adjusts the VDM distances between nominal values, instead of altering the attribute values of the codebook vectors. Hence, the innovation of the SLVQ approach lies in its integration of instance reduction techniques for selecting initial codebook vectors and its effective handling of nominal attributes. Our experiments, conducted on 17 text classification datasets with four different instance reduction algorithms, confirm SLVQ’s effectiveness. It significantly enhances the kNN’s classification accuracy of reduced datasets. In our empirical study, the SLVQ method improved the performance of these datasets, achieving average classification accuracie

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

引用

computers, Materials & Continua 2024年第3期78卷 3413-3429页

作者： Rabia Abid Muhammad Rizwan Abdulatif Alabdulatif Abdullah Alnajim Meznah Alamro Mourade Azrour Department of Computer Science Kinnaird College for WomenLahorePakistan College of Science and Engineering University of DerbyDerbyDE221GBUK Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia STI Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsMeknesMorocco

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

关键词： Artificial intelligence data privacy federated machine learning healthcare system security

来源：评论

学校读者我要写书评

暂无评论

Robust tube-based MPC with smooth computation for dexterous robot manipulation

引用

science China(information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

来源：评论

学校读者我要写书评

暂无评论

Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

引用

computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

来源：评论

学校读者我要写书评

暂无评论

Hybrid DF and SIR Forwarding Strategy in Conventional and Distributed Alamouti Space-Time Coded Cooperative Networks

引用

computer Modeling in Engineering & sciences 2025年第2期142卷 1933-1954页

作者： Slim Chaoui Omar Alruwaili Faeiz Alserhani Haifa Harrouch Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Computer Science Department Applied CollegeUniversity of HailHail55424Saudi Arabia

In this paper,we propose a hybrid decode-and-forward and soft information relaying(HDFSIR)strategy to mitigate error propagation in coded cooperative *** the HDFSIR approach,the relay operates in decode-and-forward(DF)mode when it successfully decodes the received message;otherwise,it switches to soft information relaying(SIR)*** benefits of the DF and SIR forwarding strategies are combined to achieve better performance than deploying the DF or SIR strategy ***-form expressions for the outage probability and symbol error rate(SER)are derived for coded cooperative communication with HDFSIR and energy-harvesting ***,we introduce a novel normalized log-likelihood-ratio based soft estimation symbol(NL-SES)mapping technique,which enhances soft symbol accuracy for higher-order modulation,and propose a model characterizing the relationship between the estimated complex soft symbol and the actual high-order modulated ***-more,the hybrid DF-SIR strategy is extended to a distributed Alamouti space-time-coded cooperative *** evaluate the~performance of the proposed HDFSIR strategy,we implement extensive Monte Carlo simulations under varying channel *** demonstrate significant improvements with the hybrid technique outperforming individual DF and SIR strategies in both conventional and distributed Alamouti space-time coded cooperative ***,at a SER of 10^(-3),the proposed NL-SES mapping demonstrated a 3.5 dB performance gain over the conventional averaging one,highlighting its superior accuracy in estimating soft symbols for quadrature phase-shift keying modulation.

关键词： Cooperative communication soft information relaying soft symbols modeling cooperative diversity gain distributed Alamouti space-time code

来源：评论

学校读者我要写书评

暂无评论

An Energy-efficient Routing Protocol Based on Elephant Herding Optimization in MANET

引用

Recent Advances in computer science and Communications 2024年第8期17卷 105-115页

作者： Dinesh, A. Vignesh, B. P. Sreejith Department of Computer Science Sri Krishna Adithya College of Arts and Science Coimbatore India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore India

Background: A mobile ad hoc network (MANET) is a collection of self-organizing mobile nodes creating an ad hoc network without fixed infrastructure. Routing is a major issue in mobile networks that may reduce network performance due to frequent network topology changes. Routing protocols are so important in dynamic multi-hop networks that many studies have focused on the routing problem in MANETs. Methods: In this research work, a proposed ad hoc on-demand distance vector (AODV) with an elephant herding optimization (EHO) called AODVEHO is used as an optimal path selection method that creates a set of best paths between destination and source. Results: To validate the performance of proposed AODVEHO, AntHocNet, AODV, and des-ignation sequence distance vector (DSDV). Various performance measures are considered to validate our proposed research work, such as packet delivery ratio (PDR), end-to-end delay (E2ED), and average energy consumption (AEC). Conclusion: Experimental results are indicated that the AODVEHO achieved a higher routing overhead (73 %), PDR (89.87 %), low E2ED rate (95.22 %), AEC (75.60 %), and dead nodes (75%) when compared to other routing schemes. © 2024 Bentham science Publishers.

关键词： Routing protocols

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：