检索结果-内蒙古大学图书馆

Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

Tsinghua science and technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based software engineering: progress,challenges, and opportunities

引用

science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

来源：评论

学校读者我要写书评

暂无评论

Analysing Polycystic Ovary Syndrome Using Machine Learning Models 23th

Analysing Polycystic Ovary Syndrome Using Machine Learning M...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Gaur, Yana Chaudhary, Abhay Abraham, Ajith School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031647758

A lot of women of childbearing age have Polycystic Ovary Syndrome, or PCOS. It is a problem with the hormones that control reproduction. PCOS commonly affects women who are able to have children. In addition to irregular menstrual cycles, this condition is characterised by elevated testosterone levels and the formation of small ovarian cysts. People living with polycystic ovarian syndrome (PCOS) sometimes struggle with personality issues. Infertility, obesity, poor insulin resistance, and a high risk of diabetes and cardiovascular disease are all possible outcomes. PCOS, despite being common and having significant impacts on women's health, is still not widely acknowledged or well comprehended by the general population. Frequently, there is a deficiency in knowledge regarding the condition, leading to a delay in diagnosis, insufficient support, and the imposition of social stigma. Educating and raising knowledge about PCOS is important so that people who have it can get the right medical care and mental support right away, which will help them take better care of their general health and well-being. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cell proliferation

来源：评论

学校读者我要写书评

暂无评论

A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification

引用

computers, Materials & Continua 2025年第4期83卷 739-760页

作者： Zhiyong Li Xinlian Zhou School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411100China

Brain tumor classification is crucial for personalized treatment *** deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked during global feature ***,we propose a brain tumor Magnetic Resonance Imaging(MRI)classification model based on a global-local parallel dual-branch *** global branch employs ResNet50 with a Multi-Head Self-Attention(MHSA)to capture global contextual information from whole brain images,while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor *** features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important ***,to address sample imbalance in the dataset,we introduce a category attention block to improve the recognition of minority *** results indicate that our method achieved a classification accuracy of 98.04%and a micro-average Area Under the Curve(AUC)of 0.989 in the classification of three types of brain tumors,surpassing several existing pre-trained Convolutional Neural Network(CNN)***,feature interpretability analysis validated the effectiveness of the proposed *** suggests that the method holds significant potential for brain tumor image classification.

关键词： Deep learning attention mechanism feature fusion dual-branch structure brain tumor MRI classification

来源：评论

学校读者我要写书评

暂无评论

Analyzing topics in social media for improving digital twinning based product development

引用

Digital Communications and Networks 2024年第2期10卷 273-281页

作者： Wenyi Tang Ling Tian Xu Zheng Ke Yan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product *** efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital *** work mines real-world consumer feedbacks through social media topics,which is significant to product *** specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a *** primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset ***,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse *** this end,this work combines deep learning and survival analysis to predict the prevalent time of *** propose a specialized deep survival model which consists of two *** first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network ***,a specific loss function different from regular survival models is proposed to achieve a more reasonable *** experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

关键词： Digital twinning Product development Topic analysis Social media

来源：评论

学校读者我要写书评

暂无评论

Robust video question answering via contrastive cross-modality representation learning

引用

science China(Information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer Science and Information Engineering Hefei University of Technology Institutes of Physical Science and Information Technology Anhui University School of Computer Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Recommendation Systems: A Comparative Analysis of Multimodal Feature Representations 12th

Enhancing Recommendation Systems: A Comparative Analysis of ...

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Wasid, Mohammed Mathur, Kush Anmol, Kumar School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India

ISBN: (纸本)9789819789450

Recent studies have shown that multimodal feature representations improve recommendation accuracy and help address the cold-start problem. These representations are achieved through deep learning-based fusion of various modalities or knowledge graph-based text encoding. However, training multimodal recommendation models still relies heavily on labelled data, which is labour-intensive and costly to obtain. In this paper, we conduct a comparative analysis of multidimensional recommender systems, exploring different combinations of modalities. Researchers are increasingly focusing on multimodal information to enhance recommendation systems. By leveraging rich contextual data such as textual descriptions, metadata, audio, and visual content, multimodal recommendation models can overcome the limitations of traditional collaborative filtering techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Secure Cloud-Based Diabetes Classification with Machine Learning

Secure Cloud-Based Diabetes Classification with Machine Lear...

引用

2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Anas, Mohd Hussain, Shahadat Ahmad, Shahnawaz School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350379716

With the continuous advancement of medical devices, a substantial amount of data is generated within the healthcare environment daily. Consequently, cloud computing technology is being employed to securely store, process, and manage these vast volumes of data, safeguarding them from potential attacks. This paper focuses on diabetes classification using machine learning (ML) algorithms with a secured cloud computing environment by combining the Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES) method to ensure authentication and data integrity. Additionally, we performed a comparative analysis of diabetes prediction classification using five diverse ML algorithms. The results show that the random forest (RF) classification method achieves the best results among K-Nearest Neighbors (KNN), Naive Bayes (NB), Diction tree (DT), and support vector machine (SVM) classifiers, with an accuracy of 90.4%, as well as the evaluation of security in the cloud involves analyzing the encryption and decryption times for records of different file sizes, ranging from 20 KB to 50 KB. The results obtained demonstrate that the proposed system surpasses other state-of-the-art systems in terms of performance. Therefore, the proposed model successfully establishes an efficient remote health monitoring system that relies on secure data storage and management on cloud-based platforms. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Smart Market Insights: A Deep Learning Approach to Trend Analysis 13

Smart Market Insights: A Deep Learning Approach to Trend Ana...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Ahmad, Shahnawaz Ansari, Mohd. Aquib Mair, Aakriti Hussain, Shahadat School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350380583

Stock prices are associated with the dynamic nature of the market;hence it is excruciating to predict the numbers. It has always been tough to invest in a set of assets, the oddity of the financial market does not allow traditional models to predict tomorrow's asset values with higher accuracy. Moreover, traditional models lack in finding interdependency among different sectors, overfitting of historical data, and other nonlinear factors. In this review, we examine machine learning (ML) techniques that are applied to stock market prediction. Making computers accomplish activities that ordinarily need human intellect is known as ML, and it is now the most popular trend in scientific study. According to our results, we have concluded that our Long-Short Term Memory model (LSTM) model has predicted prices with higher accuracy as compared to simpler models. The focus of this article is to develop a model for predicting future stock market values using recurrent neural networks (RNN), specifically the LSTM. Moreover, it would also highlight the key features of deep learning and why ML models are preferred over linear models for forecasting in the markets. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

computer-Assisted Classification of Pneumonic Patients with Chest X-ray Using Deep Networks 6th

Computer-Assisted Classification of Pneumonic Patients with ...

引用

6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Shahi, Anurag Singh, Chetan Ahmad, Farid Negi, Prachi Saxena, Pranshu School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819778614

The project aims to develop an accurate and efficient pneumonia detection system utilizing chest X-ray images, addressing the critical need for early diagnosis and treatment of pneumonia, particularly in resource-constrained healthcare settings, to improve patient outcomes and reduce healthcare costs. The approach used to identify pneumonia using chest X-rays has several stages. A large dataset of chest x-ray images from Kaggle is used to train and test the model. The images are preprocessed to assure quality and consistency. The model is trained on Convolutional Neural Networks (CNNs) and ResNet50 via transfer learning approaches. To improve model performance, data augmentation and fine-tuning will be used. For the CNN model, the accuracy, precision and F1-score are 95.13%, 96.57% and 95.38% respectively on the other hand for the ResNet50 model, the accuracy, precision and F1-score are 91.38%, 91.56% and 88.96% respectively. The proposed model can be used in computer-aided faster detection of pneumonia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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：