检索结果-内蒙古大学图书馆

In Search of a Suitable Epoch Value for the Optimum Result in Image Captioning 3

In Search of a Suitable Epoch Value for the Optimum Result i...

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dinda, Sourav Pal, Alok Ranjan Pal, Antara College of Engineering and Management Kolaghat Department of Computer Science and Engineering Kolaghat India National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9781665494007

The proposed work is carried out on retrieving the content of an image using machine learning based approach. In order to gain a deeper insight of this topic, the current state-of-the-art image caption generation technique has been considered as the baseline. The goal of this work is to find a suitable Epoch value during the training phase which can maximize the performance of a system. The proposed model is composed of six major components: 1) Data pre-processing unit;2) Convolutional Neural Network (CNN) as an encoder;3) At-tention mechanism;4) Recurrent Neural Network (RNN) as a decoder;5) Beam search to find most optimal caption;and 6) Sentence Generation and evaluation. The model is trained to maximize the likelihood of the description sentence of a given training image. Experiments on the test dataset show the accuracy of the model and the fluency of the language it learns solely from image descriptions. The challenges of this work are mentioned in detail at the last of this paper. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Secure Data Sharing with Confidentiality,Integrity and Access Control in Cloud Environment

引用

computer Systems science & engineering 2022年第2期40卷 779-793页

作者： V.Rajkumar M.Prakash V.Vennila Department of Computer Science and Engineering Krishnasamy College of Engineering and Technology(Affiliated to Anna UniversityChennai)Cuddalore607109India Department of Computer Science and Engineering School of ComputingSRM Institute of Science&TechnologyKattankulathur603203India Department of Computer Science and Engineering K.S.R.College of Engineering(Affiliated to Anna UniversityChennai)Tiruchengode637215India

Cloud storage is an incipient technology in today’s *** of security in cloud environment is one of the primary challenges faced these *** scenario poses new security issues and it forms the crux of the current *** current study proposes Secure Interactional Proof System(SIPS)to address this *** methodology has a few key essential components listed herewith to strengthen the security such as authentication,confidentiality,access control,integrity and the group of components such as AVK Scheme(Access List,Verifier and Key Generator).It is challenging for every user to prove their identity to the verifier who maintains the access *** is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication ***,RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric *** methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS *** proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality,authentication and access *** Timed Petri(STPN)Net evaluation tool was used to verify and prove the formal analysis of SIPS *** evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

关键词： Secure interactional proof system access control multi-step authentication Gulliou-Quisquater protocol

来源：评论

学校读者我要写书评

暂无评论

Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

引用

Tsinghua science and technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

来源：评论

学校读者我要写书评

暂无评论

Bootstrapping Radiography Pre-training via Siamese Masked Vision-Language Modeling with Complementary Self-distillation

Bootstrapping Radiography Pre-training via Siamese Masked Vi...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Wenqiang Luo, Luyang Chen, Hao Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong University of Science and Technology Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Department of Computer Science and Engineering Department of Chemical and Biological Engineering Hong Kong

ISBN: (纸本)9798350386226

Diagnosing thoracic diseases from chest X-rays (CXR) using deep learning faces unique challenges due to the high anatomical similarity across images and the critical nature of minute anomalies. In this paper, we introduce a novel self-supervised learning framework, Siamese Masked Vision-Language Modeling with Complementary Self-distillation (SMVLM), designed to enhance disease diagnosis in CXR by addressing these specific challenges. First, we tackle the problem of high anatomical similarity and subtle variance in CXR by employing a complementary masking strategy in a Siamese network setup, which forces the model to focus on subtle, yet diagnostically relevant features from both global and local perspectives. Secondly, we enrich our model's learning capabilities by integrating a dual masked image-text contrastive loss that aligns radiographic findings with their corresponding text report, harnessing the synergistic potential of multimodal data. In conjunction with this, cross-modal image-text pre-reconstruction with registers is introduced to deepen the contextual understanding of the CXR images and radiology report, ensuring a comprehensive feature representation. Extensive evaluations on benchmark datasets demonstrate that our method significantly outperforms existing approaches, providing a robust solution for the accurate and reliable diagnosis of thoracic diseases from CXR images. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Congestion Control Routing Scheme for WSN using AI Technologies 3

Congestion Control Routing Scheme for WSN using AI Technolog...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Basha, S.K. Mahaboob Gurumoorthi, E. Beulah, E. Mercy Kesavan, R. Kalaiselvan, S.A. Kumar, Lenin Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Cmr College of Engineering & Technology Department of Computer Science and Engineering Telangana India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India

ISBN: (纸本)9798350369083

An essential part of wireless sensor networks (WSN) is managing congestion, which occurs when the amount of traffic exceeds the combined or individual capacities of the underlying channel. As a result, developing more advanced methods to prevent, detect, and alleviate congestion requires particular considerations. In order to obtain the highest throughput, these strategies must take into account the limited resources of the WSN. Dedicated congestion control strategies and routing protocols that help in congestion detection as well as management are two of the many methods that have been created in recent years. As a means of avoiding congestion, the sink node in the previous designs would reset the topology and reduce bulk traffic. In this article, we lay the groundwork for WSNs and AI, the latter of which will try to prevent congestion in WSNs from the get-go and the former of which will try to minimize the spread of impending congestion by rerouting traffic. An integral part of modern life, Artificial Intelligence (AI) allows us to create self-governing systems. It is possible to enhance performance and reliability by making use of several sophisticated AI approaches. Better network management and routing in WSN may be the outcome of studying the AI algorithm used by WSN, leading to a more dependable network. Here we'll take a look at several AI algorithms that have found their way into WSNs, and we'll talk about how these algorithms might help WSNs overcome their challenges and become more reliable and efficient. Additionally, artificial intelligence has often been disregarded in these networks because of limitations on data processing as well as power consumptions. Thanks to AI, WSN is now lightning fast, error-free, as well as effective in almost every way. Using AI methods in nodes of wireless sensors for congestion aware routes is the goal of this work. © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of a Decentralized Voting System Using Volta Blockchain 5

Design and Implementation of a Decentralized Voting System U...

引用

5th IEEE International Women in technology Conference, WINTECHCON 2024

作者： Arthi, M. Suganthi Evangeline, C. Bharadwaj, Rohit Avrit, M. Jaswant, Gogineni Siranjeevi, G.P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350374070

The drive for secure, open, and easily accessible voting systems has spurred the advance of decentralized blockchain-powered voting systems. This paper outlines the design and implementation of a decentralized voting system that utilizes the Hardhat Ether for testing and deployment, the Volta blockchain, Solidity smart contracts, and MetaMask for user interaction. Our approach ensures the confidentiality and integrity of votes by harnessing the irreversible and transparent properties of blockchain technology, enabling participation from any location with internet connectivity. Users can safely cast their ballots straight from their web browsers thanks to the inclusion of MetaMask, improving accessibility and usability. Furthermore, the application of Hardhat Ether simplifies the development process, enabling reliable deployment and testing protocols. This paper discusses the architecture, functionality, and security considerations of our decentralized voting system, highlighting its potential to revolutionize democratic processes worldwide. © 2024 IEEE.

关键词： Voting machines

来源：评论

学校读者我要写书评

暂无评论

Normalized Sampling Strategical Random Oversampling towards Improving Classifier Performance of Chemical Composition based Ceramic Sample Classification 14

Normalized Sampling Strategical Random Oversampling towards ...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Devi, M. Shyamala Kujani, T. Maithili, K. Rao, Kota Venkateswara Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Computer Science & Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada India

Clay, earthen elements, powders, and water mixtures are typically manipulated into desired shapes to create ceramics. The ceramic is then dispersed in a blast furnace, which is a high-temperature oven, after it has been molded. Varnishes are paint-like materials that are used to decorate ceramics and are resistant to water. Manually identifying ceramic samples can lead to the discovery of flaws. The chemical composition of the finished ceramic tiles is not visible. Exact identification of ceramic samples is still a difficult task for the user, as it necessitates more hands-on experience. With this overview, this paper focus on designing an effective workflow for classifying the ceramic samples based on chemical composition. The ceramic chemical sample dataset from KAGGLE with the dataset size of 88 with 19 features having 5 classes of ceramic as 0-"FLQ Ceramic", 1- "DYBS Ceramic", 2-"DYNS Ceramic", 3-"DYY Ceramic", 4- "DYMQC Ceramic". The data is preprocessed and normalized for splitting the training and testing dataset. The normalized dataset is fitted to Anova test analysis to extract the significant features. The insignificant features from Anova test is deleted from the original database dataset and anova normalized dataset is fitted to Exhaustive feature selection using KNN, Naive Bayes, Logistic regression, Decision Tree and Random Forest classifier. The exhaustive feature selected dataset is applied to the above-mentioned classifier algorithms to examine the efficiency with the presence and absence of feature scaling. The exhaustive feature selected dataset is again applied to random oversampling in order to improve the classifier performance. The random oversampled exhaustive feature selected dataset is applied to the above mentioned classifier algorithms to compare the performance metrics like precision, accuracy, fscore, recall and run time. Experimental results shows that the decision tree exhibits 49% accuracy with exhaustive feature selected dataset. Th

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Lung Cancer Detection Using Deep-Learning Models 6th

Lung Cancer Detection Using Deep-Learning Models

引用

6th International Conference on Soft Computing and its engineering Applications, icSoftComp 2024

作者： Semwal, Prabhakar Saini, Rashmi Singh, Suraj Mittal, Mudit Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology Uttarakhand Tehri India Computer Science and Engineering G. B. Pant Institute of Engineering and Technology Pauri Garhwal Ghurdauri 246196 India

ISBN: (纸本)9783031880384

Histopathological images are essential for diagnosing medical conditions, especially when assessing a variety of illnesses, such as cancer. This research applied deep learning models to developed an automated lung cancer detection system. In this study we acquired dataset from kaggle, dataset consisting of 3000 images categorized into three different classes: adenocarcinoma, squamous carcinoma, and benign. Seventy percent (2100) images were used for training, while the remaining 15% (450) images each were allotted for testing and validation. After preprocessing, the initial image size of 768 × 768 was reduced to 224 × 224. The system achieved an impressive 98% testing accuracy using the VGG19 model, indicating its suitability for automated lung cancer finding and diagnosis. The VGG16 model reached with 95.33% accuracy, NasNetLarge demonstrated stable performance across 50 epochs with 91.77% accuracy, and InceptionV3 stabilized after 35 epochs, achieving 96.22% accuracy. The evaluation included precision, F1-score, and recall metrics for each class, providing a complete understanding of model performance. In overall summary, this analysis demonstrates the effectiveness of deep learning models in classifying lung cancer histopathology images for disease identification, VGG19 can be effective tools for automated identification of lung cancer, with high accuracy and excellent result. The difference between the highest accuracy achieved by VGG19 98% and the lowest accuracy achieved by NasNetLarge 91.77% is 6.23%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Learning Histopathological image InceptionV3 lung cancer VGG16 VGG19

来源：评论

学校读者我要写书评

暂无评论

Big Data Analytics in Cloud Computing 6

Big Data Analytics in Cloud Computing

引用

6th International Conference on Image Information Processing, ICIIP 2021

作者： Suhasini, Nittala Swapna Puli, Srilatha Sreyas Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665433617

In this paper we will study the two unlike but related technologies-Big Data and cloud computing-and also examines the benefits and outcomes of using cloud computing for Big Data analytics. As information is being produced at a phenomenal scale and it is originating from every direction, such a monstrous measure of information makes enormous or complex informational indexes. These informational collections are known as Big Data. The blend of cloud and big data can be attributed to fresh IT (Information technology) wave that is causing remarkable progress across IT departments of various industries. In this new environment of big data and cloud there are challenges associated with data storage as data are accumulating *** rapidly expansion of Big data requires government agencies to extract relevant data and make sense of it in order to make evidence-based policy decisions, with a focus on translating data into information and subsequently information into knowledge. The way to do this is to employ so-called big data analytics, which involves analyzing numerous data sets in order to reveal information such as specific patterns, correlations, and trends, among other *** data analytics places rigorous needs on networks, storage, and servers. This is why few businesses use the cloud for this. Big data and cloud are combining to create new business prospects that support big data research while also overcoming numerous architectural *** these mutually exclusive principles to coexist a solution architecture is required to truly exploit. Future breakthroughs and research difficulties in cloud computing that support Big Data analytics are presented in this review. © 2021 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Online Training and Placement System

Online Training and Placement System

引用

2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Jewani, Gunjan Sahare, Swati Kamble, Trupti Kathalkar, Ritu Unhale, Ashwini Jhulelal Institute of Technology Computer Science and Engineering Nagpur India Rashtrasant Tukdoji Maharaj University Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350398748

This technique offers students a very effective method of placement. With this method, the registration process for students is comparatively simple, and the placement officer may easily get student information. The system has quick access to the students who qualify. The system states that it will inform the kids. It gives us data on the number of students who are placed, which is displayed in the various graphs. The technology aids the college in keeping track of both current and former students. The system provides college with documentation for alumni gatherings. The timetables of all the placement-related activities can be created by the placement officer. Students who meet the requirements are automatically notified by the company. The TnP personnel and students. Since it is accessible around-the-clock, it lessens the communication gap between students and TnP coordinators and TnPs. This solution aids students and TnPs in maintaining their data at a secure portal and makes it possible to use it for additional training and employment activities. By preserving all data on a secure site that can be utilized in the future, it minimizes paper effort. It also offers a single default form that incorporates company counts and information to assist TnPs with future placement operations and retain records of all referral companies. © 2023 IEEE.

关键词： Students

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：