检索结果-内蒙古大学图书馆

Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South University School of Artificial Intelligence Guilin University of Electronic Technology The Department of Computer Science Universidad de Chile

In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

来源：评论

学校读者我要写书评

暂无评论

Document-Level Neural Machine Translation With Document Embeddings

引用

IEEE Access 2025年 13卷 87015-87025页

作者： Zhu, Linxing Jiang, Shu Zhao, Hai Li, Zuchao Huang, Jiashuang Ding, Weiping Lu, Bao-Liang Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Wuhan University School of Artificial Intelligence Wuhan430072 China

Standard neural machine translation (NMT) assumes that document-level context information is irrespective. Most existing document-level NMT methods are satisfied with a smattering sense of shallow document-level information, such as using a few context sentences surrounding the source sentence as document-level information. Our work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which can sufficiently model deeper and richer document-level context. The proposed document-level NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. We compress the entire document text with explicit boundaries into a token-size global static document embedding, and the neighboring sentences as a token-size local dynamic document embedding and concatenate with the source tokens. Experiments reveal that the proposed method significantly improves the translation performance over strong baselines and other related studies. © 2013 IEEE.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

A survey on cross-user federated recommendation

引用

science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based method for detection of copy-move forgery in videos

引用

Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Overview of LoRa Localization Technologies

引用

computers, Materials & Continua 2025年第2期82卷 1645-1680页

作者： Huajiang Ruan Panjun Sun Yuanyuan Dong Hamid Tahaei Zhaoxi Fang Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Science and Engineering School of Mechanical and Electrical EngineeringShaoxing UniversityShaoxing312000China

Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.

关键词： LoRa localization IoT TDoA RSSI application hybrid models

来源：评论

学校读者我要写书评

暂无评论

Advancing Alzheimer’s detection: integrative approaches in MRI analysis with traditional and deep learning models

引用

Neural Computing and Applications 2025年第14期37卷 8527-8546页

作者： Vanaja, T. Shanmugavadivel, Kogilavani Subramanian, Malliga Kanimozhiselvi, C.S. Department of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Erode India

Alzheimer’s dementia (AD) poses a significant global health challenge, characterized by progressive cognitive decline, memory impairment, and behavioral changes. The critical need for early detection to enable timely intervention and personalized care is emphasized by the current lack of effective treatments. This study aims to develop precise diagnostic models for AD by employing machine learning and a customized deep-convolutional neural network (cDCNN) with three convolution layers, utilizing Magnetic Resonance Imaging (MRI) data. Methods involve analyzing two distinct datasets—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Kaggle—to explore diverse cohorts and imaging features associated with AD pathology. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied across both datasets. A range of traditional machine learning methods, including support vector machine, k-nearest neighbor, random forest, decision trees, and XGBoost classifier, are evaluated alongside the cDCNN model, which leverages key MRI biomarkers of AD for both datasets. Results show the cDCNN model achieved a specific accuracy of 87% on the ADNI dataset, despite challenges in converting ADNI’s Digital Imaging and Communications in Medicine (DICOM) files to JPEG, impacting image clarity. Conclusions suggest that this research provides critical diagnostic tools for clinicians, offering insights into AD pathology and contributing to the alleviation of AD’s societal impact. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

#REval: A Semantic Evaluation Framework for Hashtag Recommendation

引用

IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3075-3087页

作者： Alsini, Areej Huynh, Du Q. Datta, Amitava Umm Al-Qura University Department of Computer Science and Artificial Intelligence Makkah24382 Saudi Arabia The University of Western Australia Department of Computer Science and Software Engineering Perth6009 Australia

Automatic evaluation of hashtag recommendation models is a fundamental task in Twitter. In the traditional evaluation methods, the recommended hashtags from an algorithm are first compared with the ground truth hashtags for exact correspondences. The number of exact matches is then used to calculate the hit rate, hit ratio, precision, recall, or F1-score. This way of evaluating hashtag similarities is inadequate as it ignores the semantic correlation between the recommended and ground truth hashtags. To tackle this problem, we propose a novel semantic evaluation framework for hashtag recommendation, called #REval. This framework includes an internal module referred to as BERTag, which automatically learns the hashtag embeddings. We investigate on how the #REval framework performs under different word embedding methods and different numbers of synonyms and hashtags in the recommendation using our proposed #REval-hit-ratio measure. Our experiments of the proposed framework on three large datasets show that #REval gave more meaningful hashtag synonyms for hashtag recommendation evaluation. Our analysis also highlights the sensitivity of the framework to the word embedding technique, with #REval based on BERTag more superior over #REval based on Word2Vec, FastText, and GloVe. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Legal Query RAG

引用

IEEE Access 2025年 13卷 36978-36994页

作者： Wahidur, Rahman S. M. Kim, Sumin Choi, Haeung Bhatti, David S. Lee, Heung-No Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of Gwangju Institute of Science and Technology Artificial Intelligence Graduate School Gwangju61005 Korea Republic of

Recently, legal practice has seen a significant rise in the adoption of artificial intelligence (AI) for various core tasks. However, these technologies remain in their early stages and face challenges such as understanding complex legal reasoning, managing biased data, ensuring transparency, and avoiding misleading responses, commonly referred to as hallucinations. To address these limitations, this paper introduces Legal Query RAG (LQ-RAG), a novel Retrieval-Augmented Generation framework with a recursive feedback mechanism specifically designed to overcome the critical shortcomings of standard RAG implementations in legal applications. The proposed framework incorporates four key components: a custom evaluation agent, a specialized response generation model, a prompt engineering agent, and a fine-tuned legal embedding LLM. Together, these components effectively minimize hallucinations, improve domain-specific accuracy, and deliver precise, high-quality responses for complex queries. Experimental results demonstrate that the fine-tuned embedding LLM achieves a 13% improvement in Hit Rate and a 15% improvement in Mean Reciprocal Rank (MRR). Comparisons with general LLMs reveal a 24% performance gain when using the Hybrid Fine-Tuned Generative LLM (HFM), the specialized response generation model integrated into the LQ-RAG framework. Furthermore, LQ-RAG achieves a 23% improvement in relevance score over naive configurations and a 14% improvement over RAG with Fine-Tuned LLMs (FTM). These findings underscore the potential of domain-specific fine-tuned LLMs, combined with advanced RAG modules and feedback mechanisms, to significantly enhance the reliability and performance of AI in legal practice. The reliance of this study on a proprietary model as the evaluation agent, combined with the lack of feedback from human experts, highlights the need for improvement. Future efforts should focus on developing a specialized legal evaluation agent and enhancing its performance

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization

引用

Evolving Systems 2025年第2期16卷 1-17页

作者： Hassan, Esraa Saber, Abeer El-Sappagh, Shaker El-Rashidy, Nora Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt

Breast cancer (BC) is one of the most significant threats to women’s health worldwide, affecting one in eight women and causing over 42,250 deaths in 2024. Early detection plays a crucial role in improving patient outcomes, with mammography being the primary screening method most health organizations recommend. This paper highlights the potential of deep learning (DL) in enhancing diagnostic Accuracy and speed. However, despite the promising results of DL models, the increasing complexity of breast cancer data presents a significant challenge in selecting the most relevant features. We propose a robust DL model incorporating transfer learning (TL) and grey wolf optimization (GWO) to enhance BC diagnosis. The architecture is developed by leveraging multiple deep neural networks, including ResNet and Inception, based on convolutional neural networks (CNNs). These networks are fine-tuned using a mammographic image dataset to optimize model weights and parameters for higher classification accuracy. Additionally, the Wisconsin Breast Cancer Dataset (WBCD) is utilized with GWO to refine feature selection further. Experimental results demonstrate that the proposed ensemble method improves robustness, generalization, and detection rates while minimizing false positives and negatives. Large-scale dataset evaluation yielded a precision of 0.942, sensitivity of 0.982, Accuracy of 0.965, and an area under the curve (AUC) value of 0.971. These findings suggest that the proposed framework could significantly enhance patient care and improve healthcare service organization and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Multi-feature Fusion Deep Network for Skin Disease Diagnosis

引用

Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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：