检索结果-内蒙古大学图书馆

4th International Conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Liu, Tianju Liu, Yuan Li, Ningyi Shi, Hui Liaoning Normal University School of Computer Science and Artificial Intelligence Liaoning Dalian China

ISBN: (纸本)9798331534622

As deepfake detection technology progresses and application scenarios broaden, it becomes challenging for detection models within a single domain or single scale to handle the complex application environments, leading to limited accuracy and generalization capabilities of the detection models. To address the above problems, we propose a Multi-domain Multi-scale DeepFake Detection model. First, we employ the RetinaFace framework to preprocess and augment the image data, which directs the model's attention to the relevant regions of the image. Next, we utilize the EfficientNet-B0 network for rapid coarse-grained feature extraction. Building on these coarse-grained features, we design two modules to capture forgery traces at multiple levels: a spatial-domain fine-grained feature extraction module based on multi-scale analysis and a frequency-domain fine-grained feature extraction module leveraging the Fast Fourier Transform. Finally, the dual-domain features are fused using a multi-channel attention mechanism, enabling the model to comprehensively analyze images from various perspectives. Additionally, we implement a Particle Swarm Optimization (PSO) strategy to optimize the hyper-parameters of the network model. The proposed model demonstrates significant advantages over other algorithms, particularly in cross-dataset detection, exhibiting strong generalization, robustness, and adaptability. When trained on the FF++ dataset and tested on the Celeb-DF dataset, it achieves an AUC of 91.7%. This represents a 14.8% improvement in cross-dataset performance compared to the best existing model. © 2024 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

引用

Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

来源：评论

学校读者我要写书评

暂无评论

IoT Meets computer Vision: An Improved Detection of Tomato Pests and Diseases 17

IoT Meets Computer Vision: An Improved Detection of Tomato P...

引用

IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart data, Smartdata 2024

作者： Wang, Junling Wu, Dongyang Wang, Yupeng Nanjing University of Science and Technology ZiJin College School of Computer and Artificial Intelligence Nanjing China Nanjing Forestry University College of Information Science and Technology Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Accurate detection of tomato pests and diseases is essential for efficient agriculture as global food demand continues to grow. Traditional methods often struggle with accurate localization and detection, highlighting the need for advanced solutions. Leveraging the Internet of Things (IoT), which facilitates interconnected devices for data acquisition and transmission, this study introduces the TPDDM algorithm to significantly enhance detection capabilities in tomato farming. To address existing challenges, we employ innovative techniques within TPDDM. data augmentation and mosaic optimization methods are utilized to enrich the dataset and enhance image information, improving model robustness. Integration of a pre-trained Vision Transformer (ViT) into the YOLOX model boosts feature extraction and enriches the model's ability to detect subtle patterns. Additionally, the design of the Adaptive Multi-scale Feature Pyramid Network (AMFPN) enables effective fusion of features across different scales, optimizing target detection accuracy. Evaluation using comprehensive performance metrics demonstrates TPDDM's superiority over traditional approaches and other contemporary models. Real-world application of TPDDM on diverse datasets validates its high accuracy and practical utility in tomato pest and disease detection. This research underscores the transformative potential of IoT-driven technologies in advancing precision agriculture, offering scalable solutions to meet the demands of modern farming practices. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Fault Diagnosis for CAN Bus 6

Deep Learning-Based Fault Diagnosis for CAN Bus

引用

6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Peng, Xiong Ge, Junfeng He, Dingxin Liu, Zhiwei School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350353594

The Controller Area Network (CAN) bus, as a widely adopted communication protocol in real-time control systems, has played a crucial role in automotive electronic control systems, industrial automation systems, and even medical devices, owing to its outstanding real-time performance, high reliability, and excellent multi-node data exchange capabilities. However, in the event of a fault occurring in any node on the CAN bus, all interconnected devices on the bus may potentially be affected. Therefore, effective fault diagnosis for the CAN bus is of significant importance. Addressing this challenge, this paper proposes a technical solution that combines One-Dimensional Convolutional Neural Network (1DCNN) and Long Short-Term Memory Network (LSTM). The proposed 1DCNN-LSTM model achieves precise fault diagnosis by real-time monitoring of voltage changes in the CAN bus. Experimental results have demonstrated the effectiveness of this approach, showing a higher fault diagnosis accuracy compared to traditional methods relying solely on 1DCNN. © 2024 IEEE.

关键词： Buses

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis for Determining Key Influencing Factors on Carbon Credit Prices 20

A Comprehensive Analysis for Determining Key Influencing Fac...

引用

20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Alshatri, Najlaa Saberi, Morteza Hussain, Farookh Khadeer College of Computer Science and Engineering University of Jeddah Department of Computer Science and Artificial Intelligence Saudi Arabia School of Computer Science University of Technology Sydney SydneyNSW2127 Australia

ISBN: (纸本)9798350365856

Carbon credits are digital assets used in the fight against climate change. Understanding the complex interplay of factors that affect the carbon trading market is essential to determining the critical influences on carbon credit prices. In this study, three methods are employed to find the key factors that affect the price of carbon credits: correlation analysis, the Maximum Information Coefficient (MIC) and the proposed Carbon Credit Multi-Factor Identification (CCMFI) model. The CCMFI integrates Random Forest regression, SHAP values, and robust ranking mechanisms, providing a powerful tool to identify key price drivers. The results reveal that energy markets (crude oil and coal) and environmental factors (CO2 and GHG emissions) significantly influence carbon credit prices. Non-ferrous metal product manufacturing (NONF), which represents product prices, further highlights the critical role of economic factors. Patents on environmental technologies (PT) are also critical drivers, highlighting the importance of green technological ad-vancements in determining carbon credit prices. European Union Allowances (EUAs) also exerted a strong influence, reflecting the impact of international carbon markets. The performance evaluation of the proposed CCMFI model resulted in an MSE of 1.44, RMSE of 1.20, MAE of 0.44, and an R2 of 0.98, which confirms its effectiveness. Our results indicate that the key factors vary depending on the method applied. Therefore, we recommend using multiple explanation techniques for more reliable results. © 2024 IEEE.

关键词： Carbon capture and utilization

来源：评论

学校读者我要写书评

暂无评论

Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

来源：评论

学校读者我要写书评

暂无评论

Topo-Loss: A Novel Loss Function for Learning Multiple Manifold Structures in Image Classification with Deep Neural Networks 5

Topo-Loss: A Novel Loss Function for Learning Multiple Manif...

引用

5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Ran, Likai Cai, Chao Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350373820

abstract-The real data distribution of a specific category tends to approach a low-dimensional manifold in the Riemannian space, and the manifolds of different categories do not intersect in high-dimensional space. However, existing loss functions used for image classification ignore the inherent manifold structure between samples, resulting in limited network performance. In this paper, we reveal that maintaining the manifold structure between samples while extracting features can significantly improve the efficiency of neural networks in image classification tasks and propose a novel loss function called Topo-Loss. Topo-Loss is calculated by a dual-branch structure network. One of the branches is used to calculate the cross-entropy loss, and the other branch is used to get the cosine loss between the local geodesic distance of samples in the feature space and it in the original space. The optimization goal of the network is to minimize the weighted sum loss of the two branches, thus allows the neural network to maintain the manifold structure between samples during the feature extraction. The visualization experiments show that the DNN gains the ability to learn multiple manifolds with Topo-Loss simultaneously, significantly improving the network's performance in image classification. Meanwhile, our work outperforms other similar works. © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Multi-level Teaching Text Classification based on the Fusion of Deep Learning Models

Multi-level Teaching Text Classification based on the Fusion...

引用

2023 International Conference on Information Education and artificial intelligence, ICIEAI 2023

作者： Jin, Guanghao Zhao, Junhua Wang, Yuqing Wang, Jieying Du, Hui Song, Qingzeng School of Artificial Intelligence Beijing Polytechnic China School of Computer Science and Technology Tiangong University China

ISBN: (纸本)9798400716157

Deep learning is widely used in text classification, which can be help the collection of teaching text samples from multiple datasets. On the other side, the variety of texts causes the difficulty of the classification. To solve this problem, we design a multi-level text classification system that fuses multiple models to increase the accuracy of the classification. In more details, we train the deep learning models on some public datasets. Then, we organize these to construct multi-level sets of models. On a text sample, we firstly detect if the dataset classification is needed. Then, we send this sample to the proper level of model set. Finally, we continuously optimize the system based on the collected samples. As the experimental results show, our method can achieve higher accuracy than the existing ones. © 2023 ACM.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Farmland Assessment and Developing an Automatic Crop Recommendation System Using GCN 5

Deep Learning for Farmland Assessment and Developing an Auto...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Srilatha, A. Praveen, P. School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331518097

Agricultural productivity is significantly influenced by soil health and crop suitability. Conventional techniques of soil analysis are physically taxing, taking up significant time, and typically involve specialized knowledge. This research proposes an innovative method employing deep learning techniques to classify soil images for farmland assessment. Furthermore, the study extends to developing an automatic crop recommendation system based on the classified soil types. By Utilizing deep convolutional networks for visual data classification and integrating data-driven recommendations, this system aims to enhance decision-making processes in agriculture, promoting sustainable farming practices and optimized crop yields. The GCN model, having achieved almost perfect score of 98% the performance set here is beyond any doubt one of the best that can be routinely which has been complemented by great results in several essential indicators starting with F1-score, recall, specificity, sensitivity and precision. Besides this comprehensive study also provides verifiable evidence of the fact that in each of the five categories, the GCN model stands out as the superior performer emphasizes its possibility to revolutionize the terrain and farming decision-making. The GCN mode distinctive approach is this ability to use topologies from the graph to deconstruct equations in agriculture provides a multiple perspective unmatched by its competitors. © 2024 IEEE.

关键词： ARIMA Models Production Function Time Series Analysis Vector Error Correction Models

来源：评论

学校读者我要写书评

暂无评论

AVF-Lip: High-Fidelity Talking Face Generation via Audio-Visual Fusion 7

AVF-Lip: High-Fidelity Talking Face Generation via Audio-Vis...

引用

7th International Conference on Pattern Recognition and artificial intelligence, PRAI 2024

作者： Wu, Mengduo Zhao, Sirui Wu, Tong Xu, Yifan Xu, Tong Chen, Enhong University of Science and Technology of China School of Computer Science and Technology Hefei China University of Science and Technology of China School of Data Science Hefei China

ISBN: (纸本)9798350350890

Talking face generation aims to create realistic facial videos with lips precisely synchronized to the input audio, finding broad applications in human-computer interaction, virtual avatars, and video conferencing. Despite significant advancements in previous works, existing methods continue to face challenges in achieving accurate lip synchronization and high visual quality. To address these challenges, we propose a talking face generation framework named AVF-Lip that supports higher resolution and accurate lip synchronization. Specifically, an audio-visual fusion module (AVFM) is first customized to improve lip synchronization performance, and this is further enhanced by introducing a dynamic and fine-grained face mask. Subsequently, to address the problem of lip distortion in high-resolution facial images, we design a set of loss functions based on face sketches to enhance the generation of the lip areas. Extensive experiments demonstrate that our AVF-Lip outperforms state-of-the-art methods in lip synchronization and delivers competitive visual quality, positioning it as a promising solution for high-resolution and high-fidelity talking face generation. Project page: https://***/ © 2024 IEEE.

关键词： Video conferencing

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：