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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

共 3023 条记录，以下是441-450 订阅

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Enhancing Cross-Border data Sharing in Blockchain Networks: A Compliance-Centric Approach Ensuring Anonymity and Traceability 3

Enhancing Cross-Border Data Sharing in Blockchain Networks: ...

引用

3rd international conference on computer science and Blockchain, CCSB 2023

作者： Peng, Sheng Sun, Di Zhu, Linkai Zhou, Hongtu Zhang, Xueshuai Cui, Chenchang Guangdong University of Science and Technology Academy of Management Dongguan China Shijiazhuang College of Applied Technology Department of Information Engineering Shijiazhuang China Information Technology School Hebei University of Economics and Business Shijiazhuang China International Education School Hebei University of Economics and Business Shijiazhuang China

ISBN: (纸本)9798350359077

The rise of digital globalization necessitates robust cross-border data sharing mechanisms, presenting challenges in security, privacy, and regulatory compliance. This paper introduces a novel blockchain-based framework to address these challenges, facilitating secure and efficient data exchange while meeting diverse international compliance requirements. Our approach combines advanced cryptographic techniques with smart contracts to create a dual-layered blockchain architecture. The first layer provides participant anonymity through zero-knowledge proofs, and the second ensures traceable, compliant data transactions. This solution streamlines compliance with varied data protection laws, offering a transparent and privacy-preserving platform for global data sharing initiatives. It promises significant benefits for entities engaged in multinational operations, enhancing security, simplifying regulatory adherence, and protecting user privacy. © 2023 ieee.

关键词： Blockchain

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Research on Fine-Grained Image Classification with Residual Network and Feature Fusion 3

Research on Fine-Grained Image Classification with Residual ...

引用

3rd international conference on Electronic Information Engineering and computer science, EIECS 2023

作者： Xin, Haitao Dou, Ruili Harbin University of Commerce School of Computer and Information Engineering Heilongjiang Harbin China Harbin Business University School of Computer and Information Engineering Heilongjiang Harbin China

ISBN: (纸本)9798350316674

With the development of deep learning, deep residual networks have been emerging. Deep residual networks are widely used in image classification by virtue of the advantages of mitigating the gradient vanishing problem and solving the degradation of the depth performance of traditional deep convolutional increasing networks. Fine-grained image classification is a subcategory of image classification that classifies subcategories of the same category. In this paper, we first use Mixup, a data augmentation approach, to increase the diversity of training samples and thus improve the generalization ability of the model, then we propose to solve the challenges posed by the fine-grained classification problem with a deep residual network, ResNet50, and finally, the vectors that have been processed through the last three layers of ResNet50 are spliced in order to achieve multilayer feature fusion. Comparison with the traditional method on the CUB-200-2011 dataset shows that our method can achieve better classification results. © 2023 ieee.

关键词： data enhancement deep residual networks feature fusion fine-grained image classification

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Research on the application of Deep Learning Methods in Carbon Emission Prediction 3

Research on the Application of Deep Learning Methods in Carb...

引用

3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Tan, Ning Liu, Jun Fan, Shuaizuo Shenyang Jianzhu University Liaoning Shenyang China Huazhong Agricultural University Wuhan Hubei China University of Queensland Brisbane Australia

ISBN: (纸本)9798350357707

This paper aims to explore techniques for forecasting carbon emissions using gated cyclic unit (GRU) neural networks. Firstly, historical carbon emissions data from various countries were collected and data preprocessing was carried out;then, a GRU neural network-based prediction model was developed to learn the characteristics of time series data;In order to enhance the precision of the model, adjustments and optimizations to the model's parameters were made continually;finally, the performance of the model was assessed through simulation experiments and compared to other frequently employed prediction methods. The experimental results show that the GRU-based neural network model exhibits excellent performance in the carbon emissions prediction task with higher prediction accuracy and stability. The method provides an effective and feasible method for carbon emissions prediction, which helps the government and enterprises to make more scientific decisions in environmental protection and carbon emissions control. Future research can further explore how to combine data from other fields, such as economic data and weather data, to further improve the accuracy of carbon emissions prediction. © 2023 ieee.

关键词： Time series

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Detecting phishing attacks using Convolutional Neural Network and LSTM 3

Detecting phishing attacks using Convolutional Neural Networ...

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jesmithaa, S. Eliyas, Sherin Hindustan Institute of Science and Technology Department of Computer Application Chennai India

ISBN: (纸本)9798350399264

Cybersecurity threats are increasing immensely due to the evolution of digitization. Due to this evolution, many security threats are happening around the world. One serious threat is phishing. It is a fraudulent activity that aims to steal a victim's private data using fraudulent emails and websites. Sensitive data such as login information, passwords, and bank account details are obtained and misused. In these types of attacks, the attackers appear to be legitimate. It is very hard to differentiate between what is real and phishing. A lot of research has been carried out to classify URLs as legitimate or related to phishing. However, a more efficient detection approach is needed to accurately predict and distinguish genuine sites from phishing sites. This issue can be solved by using deep learning algorithms such as convolutional neural networks (CNN) and long-term short-term memory (LSTM). This paper proposes a hybrid (CNN-LSTM) approach that provides maximum accuracy. © 2023 ieee.

关键词： Cybersecurity

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DenseNet-SALD: A Lightweight Diseases and Pests Spatial Attention Classification Model 3

DenseNet-SALD: A Lightweight Diseases and Pests Spatial Atte...

引用

3rd ieee international conference on Software Engineering and Artificial Intelligence, SEAI 2023

作者： Qin, Zhanghao Chen, Longqiu Wang, Yanan Jiang Nan University College of Artificial Intelligence and Computer Science Wuxi China Tianjin Agricultural University College of Computer and Information Engineering Tianjin China Tianjin University of Commerce College of Science Tianjin China

ISBN: (纸本)9798350337952

Leaf diseases and pests significantly impact crop yields, making it crucial to rapidly and accurately identify plant foliar diseases and implement appropriate control measures. We present a novel DenseNet-SALD-based model for identifying maize diseases and pests to improve classification performance. The proposed model was trained on a balanced dataset, incorporating an attention mechanism, Leaky ReLU activation function, and Dropout regularization to enhance its classification accuracy. The experimental results demonstrate that the proposed model outperforms conventional convolutional neural network models, such as DenseNet and AlexNet, achieving an accuracy of 99.0% and 85.03% for diseases and pests testing, respectively, meeting the primary pests recognition needs. This research provides valuable insights into the application of attention mechanisms in deep learning models for image recognition tasks, which could have potential implications for advancing the classification of corn leaf diseases and pests images. © 2023 ieee.

关键词： Image recognition

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Ship Target Detection Algorithm Based on Improved YOLOv5 3

Ship Target Detection Algorithm Based on Improved YOLOv5

引用

3rd international conference on computer science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Louyu Bi, Zhenbo School of Information Engineering Zhejiang Ocean University Zhoushan China Zhejiang Ocean University Department of Physics Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9798350372052

In view of the low accuracy and low efficiency of traditional ship target detection methods, this paper introduces an improved ship target detection method based on YOLOv5. Firstly, we preprocess the ship target data set, which includes graph denoising, graph enhancement and so on. Secondly, based on the benchmark YOLOv5 object detection algorithm, SimAM attention mechanism module is introduced. Then, a pyramid feature fusion strategy is added to filter conflicting information in airspace to suppress inconsistent features and improve the network's feature fusion capability for targets of different scales. Finally, the trained model is tested to achieve accurate evaluation of ship target detection. Experimental results show that the proposed ship target detection method is compared with YOLOv5s in accuracy rate, recall rate, mAP@0.5 and mAP@0.5: 0.95 increased by 1.9, 3.8, 2.6 and 7.3 percentage points. Respectively, which can meet the requirements of detection speed and obtain better detection accuracy, effectively realizing high-speed and high-precision ship detection. © 2023 ieee.

关键词： Object detection

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A Machine Learning based Insect Bite Classification 3

A Machine Learning based Insect Bite Classification

引用

3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Akshaykrishnan, V. Sharanya, C. Abhinav, K. Aparna, C.K. Bindu, P.V. Government College of Engineering Department of Computer Science and Engineering Kerala Kannur India

ISBN: (纸本)9781665464871

Identifying insects by their bite marks can assist doctors in diagnosing victims and providing appropriate treatment. In recent years, researches using Machine Learning have been actively conducted and have produced excellent results in fields such as object detection, behaviour recognition, voice recognition, and cancer detection in medical field. This study has developed a classification application that can be used on mobile phones to solve the insect classification problems. Experiments were carried out on five insect species chosen for being the most common biting insects. Detailed study was conducted on different images with the help of Random Forest and Support Vector Machine models. These models need different insect bite marks images to classify them. Random forests achieve a better performance and are usually much faster than Support Vector Machines. © 2023 ieee.

关键词： Support vector machines

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Ethereum Decentralized peer-to-peer Contracts Mechanism 3

Ethereum Decentralized peer-to-peer Contracts Mechanism

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Singh, Rajender Bansal, Naveen Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

With its dedicated money Ether, Ethereum offers regionalized Ethernet simulated computers to manage as an open-source public blockchain, peer-to-peer agreementsability to perform clever contracts. And since Ethereum is the second largest blockchain, transaction data volumes are growing rapidly. However, at this point, most analyses of Ethereum data are focused on the statistical characteristics of Ethereum nodes and do not examine how transactions between them behave. In this research, leverage transaction data from existing blocks to categorize users and smart contracts into groups and Applies machine learning to Ethereum analytics for the first time. The identification data of the accessible Ethereum users and smart contracts is used to examine the clustering findings. © 2023 ieee.

关键词： Ethereum

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Snake-Stega: A snake game-based steganography scheme 3

Snake-Stega: A snake game-based steganography scheme

引用

3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Mahato, Susmita School of Computer Science and Engineering NIST Institute of Science & Technology Odisha Brahmapur761008 India

ISBN: (纸本)9798350300710

When two people want to communicate secretly, they can use steganography, which entails hiding the message within a seemingly innocuous medium. This research proposes a novel approach to encrypt hidden data in the famous Snake game, which is available online. The method builds a stego-snake game resembling online snake games with several information-concealing features. This paper primarily focuses on hiding messages in the snakes' food grid, followed by a simulation of a stego-snake game with an embedded message. © 2023 ieee.

关键词： Steganography

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Developing an Exam Assessment Website Based application for Assurance of Learning at One of Universities in Indonesia 3

Developing an Exam Assessment Website Based Application for ...

引用

3rd international conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Sari, Azani Cempaka Deodat, Christopher Susanto, Jasmine Tanti Amartya, Natashya Nurhakim, Muhammad Ihsan Arief Maryani Bina Nusantara University School of Computer Science Computer Science Department Tangerang Indonesia Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia

ISBN: (纸本)9781509062805

Assessment is essential in the education system as it helps determining students' performance as well as evaluation. There are different types of assessment and one of them is exam. The creation of exams may differ from one education institute to another. Observation and research were done at one of Indonesian universities to find out that the process of creating exams is still being done manually up to this date. This research uses waterfall development method and gathers data through literature research, questionnaires, and interviews. Laravel framework and MySQL database are used in the development of the application. The Eight Golden Rules, Five Human Factors Evaluation, Black Box Testing, and Paired T-Test Testing are used to evaluate the application developed. The result indicates that the application has a significant impact in simplifying the process of creating exams. © 2023 ieee.

关键词： Websites

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