检索结果-内蒙古大学图书馆

A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

computers, Materials & Continua 2023年第5期75卷 2741-2757页

作者： Muhammad Usman Sana Zhanli Li Tayybah Kiren Hannan Bin Liaqat Shahid Naseem Atif Saeed College of Computer Science and Technology Xi’an University of Science and TechnologyXi’anShaanxi710054China Department of Computer Science(RCET Campus) University of Engineering and TechnologyLahore39161Pakistan Department of Information Sciences Division of Science&TechnologyUniversity of EducationLahore54700Pakistan Department of Computer Science COMSATS UniversityIslamabadLahore54700Pakistan

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being *** cloud computation,data processing,storage,and transmission can be done through laptops andmobile *** Storing in cloud facilities is expanding each day and data is the most significant asset of *** important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s *** have to be dependent on cloud service providers for assurance of the platform’s *** security and privacy issues reduce the progression of cloud computing and add ***;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured *** this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of *** implementation of a modified RSA algorithm results guaranteed the security of data in the cloud *** enhance the user data security level,a neural network is used for user authentication and ***;the proposed technique develops the performance of detection as a loss function of the bounding *** Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.

关键词： Cloud computing data security RSA algorithm Faster R-CNN

来源：评论

学校读者我要写书评

暂无评论

Analysis of Watermarked Video Optimization and Training Based on Classification Using Deep Learning Techniques

引用

SN computer science 2024年第2期5卷 220页

作者： Muthulakshmi, K. Valarmathi, K. Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

The exchange of multimedia data is secured using watermarking. Typically, embedding a watermark involves optimising scheme parameters, frequently by using meta-heuristic optimization methods. Despite the fact that DNN have achieved significant advancements in field of multimedia representation, training neural methods takes a lot of time and data. This research propose novel method in analysing watermarked video optimization and training based on DL. Here input is collected as watermarked input video and optimize and train the watermarked input video data with enhanced security using swarm optimization-based clustering fuzzy greedy trust analysis. then the optimized video has been trained using variational temporal-based u-net convolutional encoder neural networks (VTU-net ENN). Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for various watermarked dataset. Accuracy attained by proposed technique is 92%, precision attained by proposed technique is 75%, recall attained is 62%, F-1 score attained 55%, RMSE obtained is 53%, NSE attained is 48% by proposed technique. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Optimization and training Securing multimedia Watermarked video

来源：评论

学校读者我要写书评

暂无评论

A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

引用

computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

来源：评论

学校读者我要写书评

暂无评论

AnyComputing: A Novel Architecture for Orchestrating Various Computing Paradigms for Adaptive Workload Management 21

AnyComputing: A Novel Architecture for Orchestrating Various...

引用

21st International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2024

作者： Taghizadeh, Seyedreza Yousefi, Sara Engineering and Information Technology Shiraz University Department of Computer Science Shiraz Iran

ISBN: (纸本)9798350377545

The proliferation of physical objects and intelligent systems, along with their inherent differences, such as levels of device mobility, generated data volume, required storage memory, and complexity in data processing, presents a significant challenge in achieving real-Time and optimal responsiveness to the Massive volume of sent requests from these systems. Depending on the nature of each request and its requirements, effective processing must be by available and suitable computational platforms. This situation necessitates the urgent development of novel ideas and frameworks in request orchestration and processing by suitable computational platforms to enhance system performance and efficiently utilize available computational resources. In this article, we propose an integrated computational architecture spanning from edge devices of users and intelligent things to cloud servers. This design encompasses the reception, processing, and response to various computational and storage requests, considering specific requirements, through computational units deployed within this span. Our objective in devising this architecture is to establish mechanisms for categorizing diverse computational requests from users and things, effectively allocating resources to them, thereby reducing response time, optimizing bandwidth utilization and other available resources, managing latency for real-Time requests, and minimizing energy consumption in user devices and intelligent objects. © 2024 IEEE.

关键词： Memory management units

来源：评论

学校读者我要写书评

暂无评论

Conditional Generative Adversarial Network Approach for Autism Prediction

引用

computer Systems science & engineering 2023年第1期44卷 741-755页

作者： K.Chola Raja S.Kannimuthu Department of Computer Science and Engineering Sri Eshwar College of EngineeringCoimbatore641202TamilnaduIndia Department of Information Technology Karpagam College of EngineeringCoimbatore641032TamilnaduIndia

Autism Spectrum Disorder(ASD)requires a precise diagnosis in order to be managed and ***-invasive neuroimaging methods are disease markers that can be used to help diagnose *** majority of available techniques in the literature use functional magnetic resonance imaging(fMRI)to detect ASD with a small dataset,resulting in high accuracy but low *** supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text,images,and videos,but their performance and robustness are restricted by the size of the accompanying training *** learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering *** takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural *** modelling,also known as Generative Adversarial Networks(GANs),is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original *** are an exciting and rapidly changingfield that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains,most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the *** this paper,we present a novel conditional generative adversarial network,or cGAN for short,which is a form of GAN that uses a generator model to conditionally generate *** terms of prediction and accuracy,they outperform the standard *** pro-posed model is 74%more accurate than the traditional methods and takes only around 10 min for training even with a huge dat

关键词： Autism classification attributes imaging adversarial fMRI functional graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

来源：评论

学校读者我要写书评

暂无评论

Prediction of banana leaf disease by intelligent algorithm through digital image using neural network technique

引用

Multimedia Tools and Applications 2025年 1-17页

作者： Nagu, Bharathiraja Kaur, Gaganpreet Shanmugavelu, Murugesan M.R.M, Veeramanickam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Information Technology Tagore Engineering College Tamil Nadu Chennai India Apex Institute of Technology-Computer Science and Engineering Chandigarh University Punjab India

A lot of research shows that there could be several reasons why the duality of agricultural products has been reduced. Plant diseases make up one of the most important components of this quality. Therefore, the reduction of plant diseases allows for much higher product quality. The automated diagnosis of plant diseases using CNN techniques is presented in this article. The simple approach alters the 25 color channels and 24H histograms. The Banana Leaf Disease Detection Technique (BLDDT), which uses two attention mechanisms to assess, identify and categorize traits, was also suggested in this research. In the analysis phase, the testing carried out using the reference information reveals that the disease, detection system may be reasonably more appropriate than the current techniques because it both identifies and diagnoses the diseased/infected areas. The researchers were able to identify Esca, black decay, and isariopsis ––––with a 99.93% accuracy level using the suggested disease detector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

引用

computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Targeted Influence Maximization Using Centrality and Diminishing Weight

Targeted Influence Maximization Using Centrality and Diminis...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Baruah, Khanjan Changmai Tripathi, Rohit Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350350821

Social Networks are an integral part of our daily life. These networks are tools for sharing information among different users. In such a network, the most influential users become the source for disseminating information over the network. Hence, social network analysis was done to understand and identify the importance of a node in information diffusion. These influential nodes help to achieve maximum influence throughout the network. However, in certain cases, only a subset of nodes are needed to be influenced. This is called targeted influence maximization. In this paper, we use different centrality measures and diminishing weights to identify influential nodes and study the influence of these nodes on the target nodes. Our simulation shows that the seed selection scheme using centrality and diminishing weights activates more target nodes in a network than the existing schemes. © 2024 IEEE.

关键词： Centrality Measures Independent Cascade Model Social Network Analysis Target Influence Maximization

来源：评论

学校读者我要写书评

暂无评论

An Efficient Image Contrast Enhancement using Segmentation in Genetic Algorithm for Remote Sensing Applications

An Efficient Image Contrast Enhancement using Segmentation i...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Yadav, Gyan Singh Nehra, Ajay Vashistha, Aman Gupta, Adarsh Deshmukh, Omkar Indian Institute of Information Technology Kota Department of Computer Science and Engineering India

ISBN: (纸本)9798350376876

This paper proposes a novel optimized histogram equalization technique using a genetic algorithm (GA) for brightness-preserved contrast enhancement. This method optimizes the histogram equalization technique to extract detailed data from distorted images. The principal objective is to enhance histogram equalization by fixing a trade-off between contrast improvement and mean-brightness preservation. A genetic algorithm is used to generate the new target histogram. The optimized histogram is used in the histogram equalization (HE) process to generate the final enhanced image. The performance of the proposed method is checked and validated with qualitative and quantitative assessments against well-known state-of-the-art techniques. The result section depicts that the proposed method significantly enhances the visual effect of distorted images based on human subjective and objective evaluation indices. 3-5 percent enhancement can be observed if we compare the standard Histogram Equalization method to the proposed method. © 2024 IEEE.

关键词： Genetic algorithms

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：