检索结果-内蒙古大学图书馆

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

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

来源：评论

学校读者我要写书评

暂无评论

Multilingual web traffic forecasting for network management using artificial intelligence techniques

引用

Results in engineering 2025年 26卷

作者： Samaan, Sama Salam Korial, Ayad E. Sarra, Raniya R. Humaidi, Amjad J. Computer Engineering Department University of Technology- Iraq Baghdad10001 Iraq Control and Systems Engineering Department University of Technology- Iraq Baghdad10001 Iraq

Web traffic forecasting is critical for ensuring an effective allocation of network resources for websites to avoid potential congestion and maintain service quality. Analyzing traffic forecasts helps network operators plan for future capacity needs to accommodate unexpected traffic loads. Multilingual web platforms present unique challenges due to varying traffic patterns across several languages. In this paper, modular architecture is proposed for multilingual web traffic forecasting using deep learning models. The architecture consists of multiple processes, from collecting the traffic data of the targeted website to preparing the data to be in a suitable form to feed the deep learning models. This study explores time-series forecasting methods using three deep learning models: long short-term memory (LSTM), gated recurrent units (GRU), and convolutional neural networks (CNN). A comprehensive dataset was collected from the BBC World Service websites in three languages—English, Arabic, and Persian—spanning the period from January 2017 to July 2024. The performance of the employed deep learning models is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), relative root mean square error (RRMSE), and R-squared (R²). Results show that the CNN model outperformed the GRU and LSTM models in accuracy and variance explanation across all three language datasets. The CNN model demonstrated superior performance, achieving R² scores of 0.980, 0.989, and 0.994;MAE values of 47,195, 5090, and 6714;RRMSE values of 1.43 %, 1.90 %, and 1.86 %;and RMSE values of 61,372, 6342, and 9376 for the English, Arabic, and Persian websites, respectively. These results highlight the potential of deep learning models in capturing complicated patterns of multilingual web platforms, making them suitable for real-world network management. © 2025

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Rice Leaf Disease Detection Using Deep Learning Models: A Comprehensive Approach

Rice Leaf Disease Detection Using Deep Learning Models: A Co...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Gorli Ramya Valli Kumari Vatsavayi Department Of Computer Science And Systems Engineering Andhra University College of engineering Visakhapatnam India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Rice serves as a primary food source for more than half of the global population. However, a range of diseases brought on by pathogens, including as bacteria, viruses and fungus are posing a growing threat to rice agriculture. These diseases have the potential to seriously reduce crop productivity and threaten food security. Conventional disease detection techniques mostly depend on the labor-intensive, time consuming and frequently prone to human error manual inspection performed by qualified agronomists. We concentrate on two cutting edge architectures: Vision Transformers (ViT) and DenseNet169, which is well-known for its high accuracy in image classification applications and efficiency in feature reuse which make use of self-attention mechanisms to identify complex patterns in images. We developed these models to precisely categorize various rice leaf diseases, including Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Narrow Brown Spot, Neck Blast, Rice Hispa, Sheath Blight, Tungro, and healthy leaves. This was achieved using a comprehensive dataset containing 15,910 annotated images of rice leaves. Our proposed systems improve crop management practices by enabling prompt interventions based on precise predictions but it helps to guarantee ensuring global food security and maximizing overall crop yield. This study highlights the revolutionary potential of artificial intelligence in agriculture, paving the way for further advancements that can enhance sustainability and productivity.

关键词： Productivity computer vision Pathogens Accuracy Food security Transformers Feature extraction Agriculture Sustainable development Diseases

来源：评论

学校读者我要写书评

暂无评论

Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Optimizing Load and Routes in Supply Chain Management with Travelling Salesman Problem and CapsNet Attention 3

Optimizing Load and Routes in Supply Chain Management with T...

引用

3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Rajasekar, P. Begam, M. Shabeena Chattopadhyay, Shouvik Basha, S. Chand Chandre, Shanker Dwivedi, Vijay Kumar Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India Sns College of Technology Department of Mba Coimbatore India Institute of Engineering & Management Department of Mba Kolkata India University of Engineering & Management Kolkata India St Ann's College of Engineering and Technology Department of Business Administration Chirala India School of Computer Science & Artificial Intelligence Sr University Department of Computer Science & Artificial Intelligence Warangal India Vishwavidyalaya Engineering College Department of Mathematics Ambikapur India

ISBN: (纸本)9798331508456

Ever since the industrial revolution, industries have partnered to solve marketing problems and create new channels. Because they don't understand their clients' needs, sales centers often pursue conflicting aims. Lack of information has extended delivery times. Due to supply time, quality, or bulk freight depreciation agreements, facility expenses rise to satisfy demand, resulting in excess inventory. Supply chain management, particularly regional inventory management, has considerably impacted beer manufacturing profitability. The recommended method involves training the model and extracting features. Dynamic and PCA feature extraction are different. While PCA's main purpose is to dissociate correlated from uncorrelated data, dynamic PCA considers auto- and cross-correlation in time-series observations. A revised CapsNet approach trained the models. The Improved CapsNet model outperformed the Attention and baseline models in every test. The model solved brewing supply chain issues with 94.68% accuracy. The most successful advanced machine learning approaches for improving SCM include augmented CapsNet, PCA, and dynamic PCA, according to research. These techniques boost industrial profitability when applied to major issues like excess stock and supply unpredictability. The suggested strategy is more reliable and superior than present solutions, according to results. © 2025 IEEE.

关键词： Supply chain management

来源：评论

学校读者我要写书评

暂无评论

A Dimensional Difference-Based Population Size Adjustment Framework for Gannet Optimization Algorithm 16th

A Dimensional Difference-Based Population Size Adjustment Fr...

引用

16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Pan, Jeng-Shyang Han, Kunpeng Chu, Shu-Chuan Li, Zhi Zhang, Li College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Artificial Intelligence Nanjing University of Information Science and Engineering Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan School of Information and Control Engineering Qingdao University of Technology Qingdao266590 China

ISBN: (纸本)9789819615346

In this paper, an improved Gannet optimization algorithm (DDPSA-GOA) is presented. The Gannet optimization algorithm (GOA) was formed by mathematically studying the various foraging behaviors of Gannet. GOA has had better results in the past. A framework DDPSA to improve GOA is cited in this paper. DDPSA aims to adjust population size and replace stagnant individuals with different strategies by determining the stage of the population. The framework divides the population into two stages, premature integration and evolutionary stagnation, which are two population states with stagnant individual replacement mechanisms. Optimal elimination replacement or elite replacement is adopted for different stages. The GOA algorithm in the DDPSA framework was tested against the initial GOA and other comparative algorithms in the CEC 2013 test set. Experimental results show that DDPSA-GOA outperforms other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution Supplementary Material

引用

IEEE Transactions on Image Processing 2025年 34卷 3013-3024页

作者： Yuan, Jiang Ma, Ji Wang, Bo Hu, Weiming North China Electric Power University School of Control and Computer Engineering Beijing102206 China CASIA State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://***/Fieldhunter/CdCL. © 1992-2012 IEEE.

关键词： Depression Anxiety disorders Face recognition Schizophrenia Visualization Training Data collection Uncertainty Facial features Emotion recognition

来源：评论

学校读者我要写书评

暂无评论

The Role of Generative AI in Deepfake Detection: A Systematic Literature Review

The Role of Generative AI in Deepfake Detection: A Systemati...

引用

International Conference on Smart Business and technology, ICSBT 2024

作者： Acim, Btissam Kharmoum, Nassim Lagmiri, Souad Najoua Ziti, Soumia Department of Computer Science Intelligent Processing & Security of Systems Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco IT Networking Security and Management Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Intelligence (AI) does expand possibilities and give an opportunity to achieve terrific outcomes and has innumerable potential uses. This systematic literature review focuses on the role of generative AI in the improvement of deepfake detection technologies, providing a comparison between different methods and assessing their effectiveness. By analyzing recent studies, it depicts the techniques employed most frequently in deepfake detection, such as the ones concentrating on the intricacies between the visual and auditory elements, the temporal domains, as well as any minor signs of alteration. Due to the fact that generative AI is rapidly improving, the methods that are used to detect its use are often inadequate, which calls for more studies on the subject matter. Besides, by combining audio and visual elements when performing the analysis, calling it as a method that can be useful in focusing the detection growth potential with diverse and unstructured data. Also, this study evaluates the advantages and limitations of the techniques existing today and demonstrates their relative effectiveness in controlled and real conditions. Clarifying that measures aimed at detecting AI generated content should be preoccupied with a technique that is able to keep pace with changes in generative AI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing transparency in buyer-driven commodity chains for complex products: A blockchain-based traceability framework demonstrated through an apparel supply chain simulation 6

Enhancing transparency in buyer-driven commodity chains for ...

引用

6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Takkar, Ritwik Birman, Ken Gao, H. Oliver Department of Systems Engineering Cornell University IthacaNY14853 United States Department of Computer Science Cornell University IthacaNY14853 United States School of Civil and Environmental Engineering Cornell University IthacaNY14853 United States

Buyer-driven commodity chains are characterized by commercial relationships between buyers and sellers that may obscure accountability due to complexity, thereby undermining sustainability efforts. Conventional methods to trace production, including ineffective human-led audits, risk reorienting global corporate governance towards the interests of private business and away from social benefit by limiting the role of objective data in the process. This study examines the relevant features of private, permissioned blockchain towards harnessing the transparency challenge by demonstrating the efficacy of our proposed framework against a simulation of a real-world multi-tier apparel supply chain. The simulation integrates a set of functional and operational requirements achieved through a combination of programmable smart contracts and underlying blockchain architecture. We then evaluate the framework both qualitatively and quantitatively before discussing the limitations of our work. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

MOAT: towards safe BPF kernel extension 24

MOAT: towards safe BPF kernel extension

引用

Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Hongyi Lu Shuai Wang Yechang Wu Wanning He Fengwei Zhang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology and Department of Computer Science and Engineering Southern University of Science and Technology and Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology and Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

ISBN: (纸本)9781939133441

The Linux kernel extensively uses the Berkeley Packet Filter (BPF) to allow user-written BPF applications to execute in the kernel space. The BPF employs a verifier to check the security of user-supplied BPF code statically. Recent attacks show that BPF programs can evade security checks and gain unauthorized access to kernel memory, indicating that the verification process is not flawless. In this paper, we present MOAT, a system that isolates potentially malicious BPF programs using Intel Memory Protection Keys (MPK). Enforcing BPF program isolation with MPK is not straightforward; MOAT is designed to alleviate technical obstacles, such as limited hardware keys and the need to protect a wide variety of BPF helper functions. We implement MOAT on Linux (ver. 6.1.38), and our evaluation shows that MOAT delivers low-cost isolation of BPF programs under mainstream use cases, such as isolating a BPF packet filter with only 3% throughput loss.

关键词：

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：