Engineering Applications of Artificial Intelligence最新文献_第6页

Implicit local–global feature extraction for diffusion sequence recommendation 用于扩散序列推荐的隐式局部-全局特征提取

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-28 DOI: 10.1016/j.engappai.2024.109471

{"title":"Implicit local–global feature extraction for diffusion sequence recommendation","authors":"","doi":"10.1016/j.engappai.2024.109471","DOIUrl":"10.1016/j.engappai.2024.109471","url":null,"abstract":"<div><div>The existing research using diffusion model for item distribution modeling is a novel and effective recommendation method. However, the user interaction sequences contain multiple implicit features that reflect user preferences, and how to use implicit features to guide the diffusion process remains to be studied. Therefore, considering the dynamics of user preferences, we conduct fine-grained modeling of diffusion recommendation process. Specifically, we firstly define a sequence feature extraction layer that utilizes multi-scale convolutional neural networks and residual long short-term memory networks to learn local–global implicit features, and obtains implicit features through a weighted fusion strategy. Subsequently, the extracted output features are used as conditional inputs for the diffusion recommendation model to guide the denoising process. Finally, the items that meet user preferences are generated through the sampling and inference process to realize the personalized recommendation task. Through experiments on three publicly available datasets, the results show that the proposed model outperforms the strong baseline model in terms of performance. In addition, we conduct hyperparameter analysis and ablation experiments to verify the impact of model components on overall performance.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GFML: Gravity function for metric learning GFML：用于度量学习的重力函数

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-28 DOI: 10.1016/j.engappai.2024.109463

引用次数: 0

Search task extraction using k-contour based recurrent deep graph clustering 使用基于 k-轮廓的递归深度图聚类提取搜索任务

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-28 DOI: 10.1016/j.engappai.2024.109501

{"title":"Search task extraction using k-contour based recurrent deep graph clustering","authors":"","doi":"10.1016/j.engappai.2024.109501","DOIUrl":"10.1016/j.engappai.2024.109501","url":null,"abstract":"<div><div>Search engines must accurately predict the implicit intent of users to effectively guide their online search experience and assist them in completing their tasks. Users create time-ordered query logs by performing various queries on search engines to access desired information. Search task extraction groups queries with the same intent into unique clusters, whether these queries come from different tasks within the same session or from the same task across different sessions. Accurate identification of user intent improves the performance of search-guiding processes, including query suggestion, personalized search, and advertisement retrieval. Many existing methods focus on creating graphs that show relationships between queries. However, these methods typically cluster the graph using simple threshold-based techniques rather than leveraging graph topological structure features. Recent studies have introduced deep clustering layers to prevent the model size from growing as the number of queries increases. However, these models rely on labeled data and overlook modern embeddings from language models. We propose a novel k-contour-based graph convolutional network connective proximity clustering layer (CoGCN-C-CL) architecture that clusters graphs without requiring labeled data by leveraging graph topological properties. CoGCN-C-CL simultaneously learns query representations and search tasks. The k-contours identify distinct regions of the graph, while the graph convolutional network (GCN) exploits interactions between nodes within these regions. Experimental results demonstrate that CoGCN-C-CL outperforms existing state-of-the-art search task clustering methods on frequently used search task datasets.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-order correlation preserved multi-view unsupervised feature selection 保留高阶相关性的多视角无监督特征选择

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-28 DOI: 10.1016/j.engappai.2024.109507

{"title":"High-order correlation preserved multi-view unsupervised feature selection","authors":"","doi":"10.1016/j.engappai.2024.109507","DOIUrl":"10.1016/j.engappai.2024.109507","url":null,"abstract":"<div><div>Multi-view unsupervised feature selection (MUFS) has attracted considerable attention as an efficient dimensionality reduction technique. Data usually exhibit certain correlations, and in multi-view data there are more complex high-order correlations. However, some MUFS methods neglect to explore the high-order correlations. In addition, existing methods focus only on the high-order correlation between views or between samples. To tackle these shortcomings, this paper proposes a high-order correlation preserved MUFS (HCFS) method, which fully preserves both the high-order correlation between views and between samples. Specifically, HCFS embeds the energy preservation into the self-representation learning for multi-view data, which preserves the global structure while performing feature selection. Meanwhile, HCFS uses the adaptive weighting strategy to fuse the self-representation matrices of each view into a consistent graph, and constructs a hypergraph based on it to maintain the high-order correlation in the consistent information. Furthermore, the high-order correlation between views is preserved by low-rank tensor learning, and the local structure of data is preserved by using the hyper-Laplacian regularization. Extensive experimental results on eight public datasets demonstrate that the proposed method outperforms several existing state-of-the-art methods, which validates the effectiveness of the proposed method.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning to generate and evaluate fact-checking explanations with transformers 学习用转换器生成和评估事实核查说明

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-28 DOI: 10.1016/j.engappai.2024.109492

{"title":"Learning to generate and evaluate fact-checking explanations with transformers","authors":"","doi":"10.1016/j.engappai.2024.109492","DOIUrl":"10.1016/j.engappai.2024.109492","url":null,"abstract":"<div><div>In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing information veracity. Our research contributes to the field of Explainable Artificial Antelligence (XAI) by developing transformer-based fact-checking models that contextualise and justify their decisions by generating human-accessible explanations. Importantly, we also develop models for automatic evaluation of explanations for fact-checking verdicts across different dimensions such as <span>(self)-contradiction</span>, <span>hallucination</span>, <span>convincingness</span> and <span>overall quality</span>. By introducing human-centred evaluation methods and developing specialised datasets, we emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements. This approach not only advances theoretical knowledge in XAI but also holds practical implications by enhancing the transparency, reliability and users’ trust in AI-driven fact-checking systems. Furthermore, the development of our metric learning models is a first step towards potentially increasing efficiency and reducing reliance on extensive manual assessment. Based on experimental results, our best performing generative model achieved a Recall-Oriented Understudy for Gisting Evaluation-1 (<span>ROUGE-1</span>) score of 47.77 demonstrating superior performance in generating fact-checking explanations, particularly when provided with high-quality evidence. Additionally, the best performing metric learning model showed a moderately strong correlation with human judgements on objective dimensions such as <span>(self)-contradiction</span> and <span>hallucination</span>, achieving a Matthews Correlation Coefficient (MCC) of around 0.7.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reference-based image super-resolution of hyperspectral and red-green-blue image for determination of wheat kernel quality using deep learning networks 利用深度学习网络对高光谱和红绿蓝图像进行基于参考的图像超分辨率处理，以确定小麦籽粒质量

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-25 DOI: 10.1016/j.engappai.2024.109513

{"title":"Reference-based image super-resolution of hyperspectral and red-green-blue image for determination of wheat kernel quality using deep learning networks","authors":"","doi":"10.1016/j.engappai.2024.109513","DOIUrl":"10.1016/j.engappai.2024.109513","url":null,"abstract":"<div><div>In the process of cultivation and harvest, wheat kernel quality is highly susceptible to various factors, such as disease, mildew, atrophy and impurities, and detection of kernel quality is essential to avoid hazard proliferation, facilitate product grading, and ensure food safety. Possessing abundant image and spectral characteristics, hyperspectral imaging (HSI) has gained impressive achievements in kernel quality analysis, but its low spatial resolution limits its detection accuracy. In this study, reference-based image super-resolution (RefSR) of HSI and Red-Green-Blue image was adopted to improve resolution to determine wheat kernel quality using deep learning networks. Firstly, RefSR was conducted by the improved transformer network with dual-branch feature extraction and weighted fusion operation and achieved excellent RefSR with significant resolution improvement, peak signal to noise ratio of 35.521 and structural similarity index of 0.97, outweighing the existing state-of-the-art networks. Then, the reflectance images (RIs) of effective wavelengths (EWs) from generated HSI images were combined with the residual network with a spatial, channel attention and multi-scale residual to determine wheat kernel quality. Precise analysis was achieved with the accuracy in calibration, validation and prediction sets of 100.00%, 95.26% and 92.78%. RefSR provides a novel and efficient approach for obtaining HSI images of high spatial resolution and facilitates the application of HSI in analysis of crop kernels. RIs of several sporadic EWs can be easily acquired and processed, achieving field and rapid kernel detection. Therefore, the proposed method furnishes the efficient, accurate and applicable determination of wheat kernel quality.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consistency-guided Multi-Source-Free Domain Adaptation 一致性指导的多源自由领域适应性

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-25 DOI: 10.1016/j.engappai.2024.109497

{"title":"Consistency-guided Multi-Source-Free Domain Adaptation","authors":"","doi":"10.1016/j.engappai.2024.109497","DOIUrl":"10.1016/j.engappai.2024.109497","url":null,"abstract":"<div><div>Deep neural networks suffer from severe performance degradation when facing a distribution shift between the labeled source domain and unlabeled target domain. Domain adaptation addresses this issue by aligning the feature distributions of both domains. Conventional methods assume that the labeled source samples are drawn from a single data distribution (domain) and can be fully accessed during training. However, in real applications, multiple source domains with different distributions often exist, and source samples may be unavailable due to privacy and storage constraints. To address multi-source and data-free challenges, Multi-Source-Free Domain Adaptation (MSFDA) uses only diverse pre-trained source models without requiring any source data. Most existing MSFDA methods adapt each source model to the target domain individually, making them ineffective in leveraging the complementary transferable knowledge from different source models. In this paper, we propose a novel COnsistency-guided multi-source-free Domain Adaptation (CODA) method, which leverages the label consistency criterion as a bridge to facilitate the cooperation among source models. CODA applies consistency regularization on the soft labels of weakly- and strongly-augmented target samples from each pair of source models, allowing them to supervise each other. To achieve high-quality pseudo-labels, CODA also performs a consistency-based denoising to unify the pseudo-labels from different source models. Finally, CODA optimally combines different source models by maximizing the mutual information of the predictions of the resulting target model. Extensive experiments on four benchmark datasets demonstrate the effectiveness of CODA compared to the state-of-the-art methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interactive streaming feature selection based on neighborhood rough sets 基于邻域粗糙集的交互式流媒体特征选择

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-25 DOI: 10.1016/j.engappai.2024.109479

{"title":"Interactive streaming feature selection based on neighborhood rough sets","authors":"","doi":"10.1016/j.engappai.2024.109479","DOIUrl":"10.1016/j.engappai.2024.109479","url":null,"abstract":"<div><div>Feature streams refer to features that arrive continuously over time without changing the number of samples. Such data is commonly encountered in various practical application scenarios. Stream feature selection is a technique designed to select relevant features from high-dimensional stream data, thereby reducing its overall size. Feature interaction plays a crucial role in influencing the results of feature selection. Most existing methods address stream feature selection primarily by focusing on irrelevance and redundancy, often overlooking the important interactions between features. Additionally, these methods typically assume that all samples and features are known, which contradicts the fundamental nature of streaming data. This study introduces an interactive feature selection approach for stream feature selection, utilizing the neighborhood rough set. First, we provide a basic explanation of multi-neighbor entropy, which measures the amount of information related to neighborhood classes. It is used to measure how the amount of information about neighborhood classes. Next, we propose a feature evaluation method based on correlation, redundancy, and interaction analysis. Finally, we elaborate on functions for feature evaluation criteria, aiming to design streaming feature selection algorithms that integrate correlation, redundancy, and interactivity. The proposed algorithm is compared with six other representative feature selection algorithms across 14 public datasets. Experimental results demonstrate the validity of our proposed solution.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dual-branch convolutional neural network with domain-informed attention for arrhythmia classification of 12-lead electrocardiograms 用于 12 导联心电图心律失常分类的具有领域信息关注的双分支卷积神经网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-25 DOI: 10.1016/j.engappai.2024.109480

{"title":"A dual-branch convolutional neural network with domain-informed attention for arrhythmia classification of 12-lead electrocardiograms","authors":"","doi":"10.1016/j.engappai.2024.109480","DOIUrl":"10.1016/j.engappai.2024.109480","url":null,"abstract":"<div><div>The automatic classification of arrhythmia is an important task in the intelligent auxiliary diagnosis of an electrocardiogram. Its efficiency and accuracy are vital for practical deployment and applications in the medical field. For the 12-lead electrocardiogram, we know that the comprehensive utilization of lead characteristics is key to enhancing diagnostic accuracy. However, existing classification methods (1) neglect the similarities and differences between the limb lead group and the precordial lead group; (2) the commonly adopted attention mechanisms struggle to capture the domain characteristics in an electrocardiogram. To address these issues, we propose a new dual-branch convolutional neural network with domain-informed attention, which is novel in two ways. First, it adopts a dual-branch network to extract intra-group similarities and inter-group differences of limb and precordial leads. Second, it proposes a domain-informed attention mechanism to embed the critical domain knowledge of electrocardiogram, multiple RR (R wave to R wave) intervals, into coordinated attention to adaptively assign attention weights to key segments, thereby effectively capturing the characteristics of the electrocardiogram domain. Experimental results show that our method achieves an F1-score of 0.905 and a macro area under the curve of 0.936 on two widely used large-scale datasets, respectively. Compared to state-of-the-art methods, our method shows significant performance improvements with a drastic reduction in model parameters.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSD-DETR: A lightweight real-time detection transformer of traffic sign detection for long-range perception of autonomous driving TSD-DETR：用于自动驾驶远距离感知的轻量级交通标志检测实时检测变换器

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-25 DOI: 10.1016/j.engappai.2024.109536

{"title":"TSD-DETR: A lightweight real-time detection transformer of traffic sign detection for long-range perception of autonomous driving","authors":"","doi":"10.1016/j.engappai.2024.109536","DOIUrl":"10.1016/j.engappai.2024.109536","url":null,"abstract":"<div><div>The key to accurate perception and efficient decision making of autonomous driving is the long-range detection of traffic signs. Long-range detection of traffic signs has the problems of small traffic sign size and complex background. In order to solve these problems, this paper proposes a lightweight model for traffic sign detection based on real-time detection transformer (TSD-DETR). Firstly, the feature extraction module is constructed using multiple types of convolutional modules. The model extracts multi-scale features of different levels to enhance feature extraction ability. Then, small object detection module and detection head are designed to extract and detect shallow features. It can improve the detection of small traffic signs. Finally, Efficient Multi-Scale Attention is introduced to adjust the channel weights. It aggregates the output features of three parallel branches interactively. TSD-DETR achieves a mean average precision (mAp) of 96.8% on Tsinghua-Tencent 100K dataset. It is improved by 2.5% compared with real-time detection transformer. In small object detection, mAp improved by 9%. TSD-DETR achieves 99.4% mAp on the Changsha University of Science and Technology Chinese Traffic Sign Detection Benchmark dataset, with an improvement of 0.6%. The experimental results show that TSD-DETR reduces the number of parameters by 9.06M by optimizing the model structure. On the premise of ensuring the real-time performance of the model, the detection accuracy of the model is improved greatly. The results of ablation experiments show that the feature extraction module and small object detection module proposed in this paper are conducive to improving the detection accuracy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0