Applied Intelligence最新文献

Towards user-specific multimodal recommendation via cross-modal attention-enhanced graph convolution network 通过跨模态注意力增强图卷积网络实现针对特定用户的多模态推荐

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-11-18 DOI: 10.1007/s10489-024-06061-1

Ruidong Wang, Chao Li, Zhongying Zhao

{"title":"Towards user-specific multimodal recommendation via cross-modal attention-enhanced graph convolution network","authors":"Ruidong Wang, Chao Li, Zhongying Zhao","doi":"10.1007/s10489-024-06061-1","DOIUrl":"10.1007/s10489-024-06061-1","url":null,"abstract":"<div><p>Multimodal Recommendation (MR) exploits multimodal features of items (e.g., visual or textual features) to provide personalized recommendations for users. Recently, scholars have integrated Graph Convolutional Networks (GCN) into MR to model complicated multimodal relationships, but still with two significant challenges: (1) Most MR methods fail to consider the correlations between different modalities, which significantly affects the modal alignment, resulting in poor performance on MR tasks. (2) Most MR methods leverage multimodal features to enhance item representation learning. However, the connection between multimodal features and user representations remains largely unexplored. To this end, we propose a novel yet effective Cross-modal Attention-enhanced graph convolution network for user-specific Multimodal Recommendation, named CAMR. Specifically, we design a cross-modal attention mechanism to mine the cross-modal correlations. In addition, we devise a modality-aware user feature learning method that uses rich item information to learn user feature representations. Experimental results on four real-world datasets demonstrate the superiority of CAMR compared with several state-of-the-art methods. The codes of this work are available at https://github.com/ZZY-GraphMiningLab/CAMR</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664487","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

Calibrating TabTransformer for financial misstatement detection 校准 TabTransformer 以检测财务错报

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-11-18 DOI: 10.1007/s10489-024-05861-9

Elias Zavitsanos, Dimitrios Kelesis, Georgios Paliouras

引用次数: 0

A robust self-training algorithm based on relative node graph 基于相对节点图的稳健自训练算法

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-11-16 DOI: 10.1007/s10489-024-06062-0

Jikui Wang, Huiyu Duan, Cuihong Zhang, Feiping Nie

引用次数: 0

Short-term load forecasting system based on sliding fuzzy granulation and equilibrium optimizer 基于滑动模糊粒度和均衡优化器的短期负荷预测系统。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-06-07 DOI: 10.1007/s10489-023-04599-0

Shoujiang Li, Jianzhou Wang, Hui Zhang, Yong Liang

{"title":"Short-term load forecasting system based on sliding fuzzy granulation and equilibrium optimizer","authors":"Shoujiang Li, Jianzhou Wang, Hui Zhang, Yong Liang","doi":"10.1007/s10489-023-04599-0","DOIUrl":"10.1007/s10489-023-04599-0","url":null,"abstract":"<div><p>Short-term electricity load forecasting is critical and challenging for scheduling operations and production planning in modern power management systems due to stochastic characteristics of electricity load data. Current forecasting models mainly focus on adapting to various load data to improve the accuracy of the forecasting. However, these models ignore the noise and nonstationarity of the load data, resulting in forecasting uncertainty. To address this issue, a short-term load forecasting system is proposed by combining a modified information processing technique, an advanced meta-heuristics algorithm and deep neural networks. The information processing technique utilizes a sliding fuzzy granulation method to remove noise and obtain uncertainty information from load data. Deep neural networks can capture the nonlinear characteristics of load data to obtain forecasting performance gains due to the powerful mapping capability. A novel meta-heuristics algorithm is used to optimize the weighting coefficients to reduce the contingency and improve the stability of the forecasting. Both point forecasting and interval forecasting are utilized for comprehensive forecasting evaluation of future electricity load. Several experiments demonstrate the superiority, effectiveness and stability of the proposed system by comprehensively considering multiple evaluation metrics.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 19","pages":"21606 - 21640"},"PeriodicalIF":5.3,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04599-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9689364","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

A multi-robot deep Q-learning framework for priority-based sanitization of railway stations 用于火车站基于优先级的消毒的多机器人深度Q学习框架。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-04-18 DOI: 10.1007/s10489-023-04529-0

Riccardo Caccavale, Mirko Ermini, Eugenio Fedeli, Alberto Finzi, Vincenzo Lippiello, Fabrizio Tavano

{"title":"A multi-robot deep Q-learning framework for priority-based sanitization of railway stations","authors":"Riccardo Caccavale, Mirko Ermini, Eugenio Fedeli, Alberto Finzi, Vincenzo Lippiello, Fabrizio Tavano","doi":"10.1007/s10489-023-04529-0","DOIUrl":"10.1007/s10489-023-04529-0","url":null,"abstract":"<div><p>Sanitizing railway stations is a relevant issue, primarily due to the recent evolution of the Covid-19 pandemic. In this work, we propose a multi-robot approach to sanitize railway stations based on a distributed Deep Q-Learning technique. The proposed framework relies on anonymous data from existing WiFi networks to dynamically estimate crowded areas within the station and to develop a heatmap of prioritized areas to be sanitized. Such heatmap is then provided to a team of cleaning robots - each endowed with a robot-specific convolutional neural network - that learn how to effectively cooperate and sanitize the station’s areas according to the associated priorities. The proposed approach is evaluated in a realistic simulation scenario provided by the Italian largest railways station: Roma Termini. In this setting, we consider different case studies to assess how the approach scales with the number of robots and how the trained system performs with a real dataset retrieved from a one-day data recording of the station’s WiFi network.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 17","pages":"20595 - 20613"},"PeriodicalIF":5.3,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04529-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10094498","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

Deep reinforcement learning-based approach for rumor influence minimization in social networks 基于深度强化学习的社交网络谣言影响最小化方法。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-04-04 DOI: 10.1007/s10489-023-04555-y

Jiajian Jiang, Xiaoliang Chen, Zexia Huang, Xianyong Li, Yajun Du

{"title":"Deep reinforcement learning-based approach for rumor influence minimization in social networks","authors":"Jiajian Jiang, Xiaoliang Chen, Zexia Huang, Xianyong Li, Yajun Du","doi":"10.1007/s10489-023-04555-y","DOIUrl":"10.1007/s10489-023-04555-y","url":null,"abstract":"<div><p>Spreading malicious rumors on social networks such as Facebook, Twitter, and WeChat can trigger political conflicts, sway public opinion, and cause social disruption. A rumor can spread rapidly across a network and can be difficult to control once it has gained traction.Rumor influence minimization (RIM) is a central problem in information diffusion and network theory that involves finding ways to minimize rumor spread within a social network. Existing research on the RIM problem has focused on blocking the actions of influential users who can drive rumor propagation. These traditional static solutions do not adequately capture the dynamics and characteristics of rumor evolution from a global perspective. A deep reinforcement learning strategy that takes into account a wide range of factors may be an effective way of addressing the RIM challenge. This study introduces the dynamic rumor influence minimization (DRIM) problem, a step-by-step discrete time optimization method for controlling rumors. In addition, we provide a dynamic rumor-blocking approach, namely RLDB, based on deep reinforcement learning. First, a static rumor propagation model (SRPM) and a dynamic rumor propagation model (DRPM) based on of independent cascade patterns are presented. The primary benefit of the DPRM is that it can dynamically adjust the probability matrix according to the number of individuals affected by rumors in a social network, thereby improving the accuracy of rumor propagation simulation. Second, the RLDB strategy identifies the users to block in order to minimize rumor influence by observing the dynamics of user states and social network architectures. Finally, we assess the blocking model using four real-world datasets with different sizes. The experimental results demonstrate the superiority of the proposed approach on heuristics such as out-degree(OD), betweenness centrality(BC), and PageRank(PR).</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 17","pages":"20293 - 20310"},"PeriodicalIF":5.3,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04555-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10075908","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: 2

Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis 用于医学图像分析的基于三核门控注意力的多实例学习与对比学习。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-04-04 DOI: 10.1007/s10489-023-04458-y

Huafeng Hu, Ruijie Ye, Jeyan Thiyagalingam, Frans Coenen, Jionglong Su

{"title":"Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis","authors":"Huafeng Hu, Ruijie Ye, Jeyan Thiyagalingam, Frans Coenen, Jionglong Su","doi":"10.1007/s10489-023-04458-y","DOIUrl":"10.1007/s10489-023-04458-y","url":null,"abstract":"<div><p>In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a “bag” as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 17","pages":"20311 - 20326"},"PeriodicalIF":5.3,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04458-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10075909","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

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews 基于博弈论和MCDM的餐厅评论无监督情绪分析。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-03-31 DOI: 10.1007/s10489-023-04471-1

Neha Punetha, Goonjan Jain

{"title":"Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews","authors":"Neha Punetha, Goonjan Jain","doi":"10.1007/s10489-023-04471-1","DOIUrl":"10.1007/s10489-023-04471-1","url":null,"abstract":"<div><p>Sentiment Analysis is a method to identify, extract, and quantify people’s feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers’ opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review’s positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review’s context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer’s satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 17","pages":"20152 - 20173"},"PeriodicalIF":5.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04471-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10076448","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: 5

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction LASSO和注意力TCN：室内颗粒物预测的并行方法。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-03-29 DOI: 10.1007/s10489-023-04507-6

Ting Shi, Wu Yang, Ailin Qi, Pengyu Li, Junfei Qiao

{"title":"LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction","authors":"Ting Shi, Wu Yang, Ailin Qi, Pengyu Li, Junfei Qiao","doi":"10.1007/s10489-023-04507-6","DOIUrl":"10.1007/s10489-023-04507-6","url":null,"abstract":"<div><p>Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 17","pages":"20076 - 20090"},"PeriodicalIF":5.3,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04507-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10075910","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

Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset 在基于多组分小数据集的边界改进的帮助下，甲状腺的自动分割。

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-03-15 DOI: 10.1007/s10489-023-04540-5

Yifei Chen, Xin Zhang, Dandan Li, HyunWook Park, Xinran Li, Peng Liu, Jing Jin, Yi Shen

{"title":"Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset","authors":"Yifei Chen, Xin Zhang, Dandan Li, HyunWook Park, Xinran Li, Peng Liu, Jing Jin, Yi Shen","doi":"10.1007/s10489-023-04540-5","DOIUrl":"10.1007/s10489-023-04540-5","url":null,"abstract":"<div><p>Deep learning has been widely considered in medical image segmentation. However, the difficulty of acquiring medical images and labels can affect the accuracy of the segmentation results for deep learning methods. In this paper, an automatic segmentation method is proposed by devising a multicomponent neighborhood extreme learning machine to improve the boundary attention region of the preliminary segmentation results. The neighborhood features are acquired by training U-Nets with the multicomponent small dataset, which consists of original thyroid ultrasound images, Sobel edge images and superpixel images. Afterward, the neighborhood features are selected by min-redundancy and max-relevance filter in the designed extreme learning machine, and the selected features are used to train the extreme learning machine to obtain supplementary segmentation results. Finally, the accuracy of the segmentation results is improved by adjusting the boundary attention region of the preliminary segmentation results with the supplementary segmentation results. This method combines the advantages of deep learning and traditional machine learning, boosting the accuracy of thyroid segmentation accuracy with a small dataset in a multigroup test.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 16","pages":"19708 - 19723"},"PeriodicalIF":5.3,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-023-04540-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10075911","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