Applied Intelligence最新文献_第8页

Retraction Note: Query-oriented topical influential users detection for top-k trending topics in twitter

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06181-8

Sarmistha Sarna Gomasta, Aditi Dhali, Md Musfique Anwar, Iqbal H. Sarker

引用次数: 0

Proformer: a scalable graph transformer with linear complexity

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06065-x

Zhu Liu, Peng Wang, Cui Ni, Qingling Zhang

{"title":"Proformer: a scalable graph transformer with linear complexity","authors":"Zhu Liu, Peng Wang, Cui Ni, Qingling Zhang","doi":"10.1007/s10489-024-06065-x","DOIUrl":"10.1007/s10489-024-06065-x","url":null,"abstract":"<div><p>Since existing GNN methods use a fixed input graph structure for messages passing, they cannot solve the problems of heterogeneity, over-squashing, long-range dependencies, and graph incompleteness. The all-pair message passing scheme is an effective means to address the above issues. However, owing to the quadratic complexity problem of self-attention used in the all-pair message passing scheme, it is not possible to simultaneously guarantee the scalability and accuracy of the algorithm on large-scale graph datasets. In this paper, we propose Proformer, which uses multilayer dilation convolution to project the key and value in self-attention and uses a focused function to further enhance the model representation and reduce the computational complexity of the all-pair message passing scheme from quadratic to linear. The experimental results show that Proformer performs very well in tasks such as nodes, images, and text. Additionally, when scaled to large-scale graph datasets, it is able to effectively reduce the inference time and GPU memory utilization while guaranteeing the algorithm's accuracy. On OGB-Proteins, it not only improves the ROC-AUC by 3.2% but also conserves 27.8% of the GPU memory.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811259","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

Retraction Note: A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06183-6

Keyu Lu, Huchang Liao

引用次数: 0

Retraction Note: A Cluster-based stratified hybrid decision support model under uncertainty: sustainable healthcare landfill location selection

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06175-6

Erfan Babaee Tirkolaee, Ali Ebadi Torkayesh

引用次数: 0

Retraction Note: A new humanitarian relief logistic network for multi-objective optimization under stochastic programming

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06174-7

Peiman Ghasemi, Fariba Goodarzian, Ajith Abraham

引用次数: 0

A novel hybrid neural network approach incorporating convolution and LSTM with a self-attention mechanism for web attack detection

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-05998-7

Kangqiang Luo, Yindong Chen

{"title":"A novel hybrid neural network approach incorporating convolution and LSTM with a self-attention mechanism for web attack detection","authors":"Kangqiang Luo, Yindong Chen","doi":"10.1007/s10489-024-05998-7","DOIUrl":"10.1007/s10489-024-05998-7","url":null,"abstract":"<div><p>As web attacks have recently increased in number and sophistication, traditional machine learning methods have struggled to defend against well-designed attacks. Therefore, deep learning methods have been widely used in web attack detection, leveraging their ability to discern intricate features within the original payload for precise identification of web application threats. In this study, we propose a novel hybrid neural network model for web attack detection, named hybrid convolutional long short-term memory (HCLSTM). Specifically, the HCLSTM model utilizes two branches to extract features from Hypertext Transfer Protocol (HTTP) request packet: a Deep Feedforward Neural Network (DFNN) branch for extracting word features from Uniform Resource Locator (URL), and a Convolutional Neural Network (CNN) branch for capturing combinatorial and local relationships within payloads. Then, the extracted features from both branches are concatenated and subsequently fed into a Bidirectional Long Short-Term Memory (Bi-LSTM) network integrated with a self-attention mechanism, designed to capture intricate link relationships between URL and payloads. The final classification layer produces the detection results. To evaluate the proposed model, we conducted experiments on CSIC 2010 HTTP dataset. The experimental results reveal that HCLSTM can accurately detect web attacks with a high accuracy of 99.46% and a low false positive rate of 0.02%.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811189","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

Knowledge interaction graph guided prompting for event causality identification

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06095-5

Ruijuan Hu, Jian Li, Haiyan Liu, Guilin Qi, Yuxin Zhang

引用次数: 0

Constructing explainable health indicators for aircraft engines by developing an interpretable neural network with discretized weights

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-05981-2

Morteza Moradi, Panagiotis Komninos, Dimitrios Zarouchas

{"title":"Constructing explainable health indicators for aircraft engines by developing an interpretable neural network with discretized weights","authors":"Morteza Moradi, Panagiotis Komninos, Dimitrios Zarouchas","doi":"10.1007/s10489-024-05981-2","DOIUrl":"10.1007/s10489-024-05981-2","url":null,"abstract":"<p>Remaining useful life predictions depend on the quality of health indicators (HIs) generated from condition monitoring sensors, evaluated by predefined prognostic metrics such as monotonicity, prognosability, and trendability. Constructing these HIs requires effective models capable of automatically selecting and fusing features from pertinent measurements, given the inherent noise in sensory data. While deep learning approaches have the potential to automatically extract features without the need for significant specialist knowledge, these features lack a clear (physical) interpretation. Furthermore, the evaluation metrics for HIs are nondifferentiable, limiting the application of supervised networks. This research aims to develop an intrinsically interpretable ANN, targeting qualified HIs with significantly lower complexity. A semi-supervised paradigm is employed, simulating labels inspired by the physics of progressive damage. This approach implicitly incorporates nondifferentiable criteria into the learning process. The architecture comprises additive and newly modified multiplicative layers that combine features to better represent the system’s characteristics. The developed multiplicative neurons are not restricted to pairwise actions, and they can also handle both division and multiplication. To extract a compact HI equation, making the model mathematically interpretable, the number of parameters is further reduced by discretizing the weights via a ternary set. This weight discretization simplifies the extracted equation while gently controlling the number of weights that should be overlooked. The developed methodology is specifically tailored to construct interpretable HIs for commercial turbofan engines, showcasing that the generated HIs are of high quality and interpretable.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810842","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

Retraction Note: Feature selection techniques in the context of big data: taxonomy and analysis

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-13 DOI: 10.1007/s10489-024-06182-7

Hudhaifa Mohammed Abdulwahab, S. Ajitha, Mufeed Ahmed Naji Saif

引用次数: 0

Adaptive robust control without initial stabilizing for constrained-states nonlinear multiplayer mixed zero-sum game systems with matched input disturbances

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-12-12 DOI: 10.1007/s10489-024-05980-3

Xiaopeng Qiao, Chunbin Qin, Jinguang Wang, Zhongwei Zhang, Ziyang Shang

{"title":"Adaptive robust control without initial stabilizing for constrained-states nonlinear multiplayer mixed zero-sum game systems with matched input disturbances","authors":"Xiaopeng Qiao, Chunbin Qin, Jinguang Wang, Zhongwei Zhang, Ziyang Shang","doi":"10.1007/s10489-024-05980-3","DOIUrl":"10.1007/s10489-024-05980-3","url":null,"abstract":"<div><p>In this paper, for the multiplayer mixed zero-sum game (MZSG) problem of the constrained-states nonlinear systems with matched input disturbances, an adaptive robust control method without initial stabilizing is presented on account of barrier function (BF) transformation. Firstly, the original system with state constraints is converted to a transformed system without state constraints by barrier function transformation. Secondly, to overcome the influence of matched input disturbances, considering the nominal system related to the transformation system, the cost function corresponding to each player is appropriately selected, and the robust regulation scheme with matched input disturbances is converted to the optimal regulation scheme. In addition, a novel weight tuning law is designed for the critic neural network (NN) by combining the experience replay (ER) mechanism and the index function. Then, the corresponding cost function of each player is approximated by the critic NN without requiring initial stabilizing control. Utilizing the Lyapunov stability theory, under the influence of state constraints and matched input disturbances, the critic NN weights and states within the multiplayer system are ensured to be uniformly ultimately bounded (UUB). Ultimately, the validity of the proposed method is verified by two simulation examples.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810836","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