检索结果-内蒙古大学图书馆

A Generative Model-Based Network Framework for Ecological Data Reconstruction

computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Based Approach for Sugarcane Disease Detection 3

A Deep Learning Based Approach for Sugarcane Disease Detecti...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Gupta, Vishan Kumar Sharma, Garima Vidisha Singh, Mukesh Kumar Amity School of Engineering & Technology Amity University Punjab Mohali India Department of Computer Science & Engineering Uttarakhand Dehradun India Galgotias College of Engineering and Technology Computer Science & Engineering Department Greater Noida India

ISBN: (纸本)9798331518592

The world's largest producers of sugarcane, which is used to make both sugar and bioethanol, are Brazil and India. The crop is primarily grown in tropical and subtropical regions. These nations produce 40% of the world's bioethanol and 80% of its sugar. Sugarcane is susceptible to a wide range of diseases, including bacterial blight, mosaic, red rot, rust, and yellow, due to a multitude of environmental factors. An accurate and dependable automated disease detection system is needed for sugarcane disease diagnosis. In this work, we created the convolution neural network model EfficientNetB0 utilizing data augmentation, evaluated its effectiveness in identifying diseases affecting sugarcane plants using leaf pictures, and compared its performance. The precision, recall, and F1 score values of the EfficientNetB0 model are 0.9812, 0.9821, and 0.9816. This model is a helpful tool that growers may use to prevent sugarcane infections from interfering with the harvesting process, as it has an accuracy rate of 98.12%. © 2024 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Semantic Communications for Digital Signals via Carrier Images

引用

IEEE Wireless Communications Letters 2025年第6期14卷 1816-1820页

作者： Yan, Zhigang Li, Dong Macau University of Science and Technology School of Computer Science and Engineering China

Most of current semantic communication (SemCom) frameworks focus on the image transmission, which, however, do not address the problem on how to deliver digital signals without any semantic features. This paper proposes a novel SemCom approach to transmit digital signals by using the image as the carrier signal. Specifically, the proposed approach encodes the digital signal as a binary stream and maps it to mask locations on an image. This allows binary data to be visually represented, enabling the use of existing model, pre-trained Masked Autoencoders (MAE), which are optimized for masked image reconstruction, as the SemCom encoder and decoder. Since MAE can both process and recover masked images, this approach allows for the joint transmission of digital signals and images without incurring significant communication overheads. In addition, considering the mask tokens transmission encoded by the MAE still faces extra costs, we design a sparse encoding module at the transmitter to encode the mask tokens into a sparse matrix, and it can be recovered at the receiver. Thus, this approach simply needs to transmit the latent representations of the unmasked patches and a sparse matrix, which further reduce the transmission overhead compared with the original MAE encoder. Simulation results show that the approach maintains reliable transmission even in a high mask ratio of images. © 2012 IEEE.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

OCRBench: on the hidden mystery of OCR in large multimodal models

引用

science China(Information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

来源：评论

学校读者我要写书评

暂无评论

Research Progress in Solar Flare Prediction Methods

引用

Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： Sun: activity Sun: flares (Sun:) sunspots Sun: magnetic fields magnetohydrodynamics (MHD)

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving recommendation with coarse-grained spatiotemporal contexts

引用

science China(Information sciences) 2025年第4期68卷 66-81页

作者： Lei CHEN Chen GAO Jiahuan LEI Xiaoyi DU Xinlei SHI Hengliang LUO Depeng JIN Yong LI Meng WANG Department of Electronic Engineering BNRist Tsinghua University Meituan Inc. School of Computer Science and Information Engineering Hefei University of Technology

The behavior of users on online life service platforms like Meituan and Yelp often occurs within specific finegrained spatiotemporal contexts(i.e., when and where). Recommender systems, designed to serve millions of users, typically operate in a fully server-based manner, requiring on-device users to upload their behavioral data, including fine-grained spatiotemporal contexts, to the server, which has sparked public concern regarding privacy. Consequently, user devices only upload coarse-grained spatiotemporal contexts for user privacy protection. However, previous research mostly focuses on modeling fine-grained spatiotemporal contexts using knowledge graph convolutional models, which are not applicable to coarse-grained spatiotemporal contexts in privacy-constrained recommender systems. In this paper, we investigate privacy-preserving recommendation by leveraging coarse-grained spatiotemporal contexts. We propose the coarse-grained spatiotemporal knowledge graph for privacy-preserving recommendation(CSKG), which explicitly models spatiotemporal co-occurrences using common-sense knowledge from coarse-grained contexts. Specifically, we begin by constructing a spatiotemporal knowledge graph tailored to coarse-grained spatiotemporal contexts. Then we employ a learnable metagraph network that integrates common-sense information to filter and extract co-occurrences. CSKG evaluates the impact of coarsegrained spatiotemporal contexts on user behavior through the use of a knowledge graph convolutional network. Finally, we introduce joint learning to effectively learn representations. By conducting experiments on two real large-scale datasets,we achieve an average improvement of about 11.0% on two ranking metrics. The results clearly demonstrate that CSKG outperforms state-of-the-art baselines.

关键词： privacy-preserveing coarse-grained spatiotemporal contexts recommender systems

来源：评论

学校读者我要写书评

暂无评论

State space representation and phase analysis of gradient descent optimizers

引用

science China(Information sciences) 2023年第4期66卷 140-154页

作者： Biyuan YAO Guiqing LI Wei WU School of Computer Science and Engineering South China University of Technology School of Computer Wuhan University

Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this work, the optimizers of dynamical system models are established,and the influence of parameter adjustments on the dynamic performance of the system is proposed. This is a useful supplement to the theoretical control models of optimizers. First, the system control model is derived based on the iterative formula of the optimizer, the optimizer model is expressed by differential equations, and the control equation of the optimizer is established. Second, based on the system control model of the optimizer, the phase trajectory process of the optimizer model and the influence of different hyperparameters on the system performance of the learning model are analyzed. Finally, controllers with different optimizers and different hyperparameters are used to classify the MNIST and CIFAR-10 datasets to verify the effects of different optimizers on the model learning performance and compare them with related methods. Experimental results show that selecting appropriate optimizers can accelerate the convergence speed of the model and improve the accuracy of model recognition. Furthermore, the convergence speed and performance of the stochastic gradient descent(SGD) optimizer are better than those of the stochastic gradient descent-momentum(SGD-M) and Nesterov accelerated gradient(NAG) optimizers.

关键词： optimizer control model phase trajectory parameter adjustment classification dynamic performance

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approach to Emotion Recognition by Facial Expressions: A Review Paper 5

Deep Learning Approach to Emotion Recognition by Facial Expr...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Shubhanshi Shukla, Shipra Amity School Of Engineering And Technology Computer Science And Engineering Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330861

Emotion recognition by facial expression is a challenging task that has gotten much attention in recent years. Deep neural networks are used to extract pertinent information from facial photographs and categorise them into various emotional categories in deep learning approaches, which have proven their potential in this area The article examines the most recent advances in deep learning for recognising emotions from facial expressions. The model architecture and training methods are explored, and their effectiveness on benchmark datasets is assessed. Also discuss the challenges and prospects for this field, including the need for large and diverse datasets, the interpretability of models, and the requirement to eliminate any bias in training data. Overall, deep learning algorithms for emotion recognition using facial expressions have shown considerable promise and can be applied in a variety of applications, such as virtual assistants, entertainment, and healthcare. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Techniques for Fake News Detection: A Comprehensive Analysis Using the LIAR Dataset 3rd

Advanced Machine Learning Techniques for Fake News Detectio...

引用

3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Paul, Subrata Ghosh, Shivnath Mitra, Anirban Bhattacharya, Pronaya Department of CSE-AI Brainware University West Bengal Barasat India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India

ISBN: (纸本)9789819624676

In today’s digital world, fake news presents a serious challenge to social cohesiveness, trust among individuals, and the functioning of democracy. Overcoming this issue demands novel solutions that make efficient use of machine learning (ML) for identifying and battle disinformation. This paper examines the important problem of fake news, including its meaning, its consequences, and the significance of social media in its rapid spread. The study emphasises the complexities of identifying fake news, focusing on sophisticated techniques like AI-generated content and the widespread dissemination of misinformation. In order to tackle such obstacles, the paper uses ML approaches, particularly the LIAR dataset and the XGBoost model, to create a reliable fake news detection system. The outcomes show that these methods have been successful at successfully recognising false information, highlighting ML’s prospective in reducing misinformation. This research contributes to the broader discourse on media literacy and the need for reliable information in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Customer Segmentation of E-commerce data using K-means Clustering Algorithm 13

Customer Segmentation of E-commerce data using K-means Clust...

引用

13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Rajput, Lucky Singh, Shailendra Narayan Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

Several data mining techniques, such as classification, clustering, regression, etc., are used to determine the purchasing behaviour of customers to create value for money in businesses. In this paper, clustering is implemented on a real-time data set of an e-commerce firm that aims to decide whether to focus on its website or mobile application. The K-means clustering algorithm is used to segment and cluster the users for the same purpose because of the scattered nature of the data and to find hidden patterns in the data set. To define the number of clusters, the elbow method is used, and customers are grouped with respect to different attributes. Based on the analysis, a decision is made about which set of customers to target. © 2023 IEEE.

关键词： K-means clustering

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：