Knowledge-Based Systems最新文献_第2页

Cross-domain recommender system with embedding- and mapping-based knowledge correlation 基于嵌入和映射知识关联的跨域推荐系统

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-13 DOI: 10.1016/j.knosys.2024.112514

{"title":"Cross-domain recommender system with embedding- and mapping-based knowledge correlation","authors":"","doi":"10.1016/j.knosys.2024.112514","DOIUrl":"10.1016/j.knosys.2024.112514","url":null,"abstract":"<div><p>A knowledge transfer-based cross-domain recommender system is currently a research hotspot. Existing research has reached a high level of maturity in mining potential knowledge and establishing transfer mechanisms. However, most of them ignore the impact of the dissimilarity of potential knowledge on the transfer performance. Herein, a cross-domain recommender system based on knowledge correlation-induced the embedding and mapping approach is proposed, denoted by KCEM-CDRS. First, we propose a knowledge correlation measure, which captures the consistency of knowledge between the target and source domains to build the bridge for knowledge transfer. Second, we construct a joint matrix triple factorization model to solve the data sparsity in the target domain while introducing graph regularization to solve the problem of negative knowledge transfer. Results of extensive experiments on real Amazon metadata indicate that compared with three existing cross-domain recommendation methods, KCEM-CDRS shows performance improvements of 0.05–9.55 % and 0.02–2.63 % on mean absolute error and root mean square error, respectively. Additionally, the results of the ablation experiments indicate that consideration of the knowledge correlation between domains is beneficial for knowledge transfer when the density of the source domain is rich.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 reliable Bayesian regularization neural network approach to solve the global stability of infectious disease model 解决传染病模型全局稳定性的可靠贝叶斯正则化神经网络方法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-13 DOI: 10.1016/j.knosys.2024.112481

{"title":"A reliable Bayesian regularization neural network approach to solve the global stability of infectious disease model","authors":"","doi":"10.1016/j.knosys.2024.112481","DOIUrl":"10.1016/j.knosys.2024.112481","url":null,"abstract":"<div><p>The purpose of this study is to perform the numerical results of the global stability of infectious disease mathematical model by using the stochastic computing scheme. The design of proposed solver is presented by one of the efficient and reliable schemes named as Bayesian regularization neural network (BRNN). The global stability of infectious disease mathematical nonlinear model is categorized into susceptible, infected, recovered and vaccinated. The construction of dataset is performed through the Runge-Kutta scheme in order to lessen the mean square error (MSE) by dividing the statics as training 74 %, while 13 % for both testing and endorsement. The proposed stochastic process contains log-sigmoid merit function, twenty neurons and optimization through RBNN for the numerical solutions of the global stability of infectious disease mathematical system. The best training values for each model's case are performed around 10<sup>–11</sup>. The scheme's correctness is performed by the matching of the results and the minor calculated absolute error performances. Moreover, the regression, state transmission, error histogram and MSE indicate the trustworthiness of the designed solver.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Prediction of air compressor faults with feature fusion and machine learning 利用特征融合和机器学习预测空气压缩机故障

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112519

{"title":"Prediction of air compressor faults with feature fusion and machine learning","authors":"","doi":"10.1016/j.knosys.2024.112519","DOIUrl":"10.1016/j.knosys.2024.112519","url":null,"abstract":"<div><p>Air compressors are critical for many industries, but early detection of faults is crucial for keeping them running smoothly and minimizing maintenance costs. This contribution investigates the use of predictive machine learning models and feature fusion to diagnose faults in single-acting, single-stage reciprocating air compressors. Vibration signals acquired under healthy and different faulty conditions (inlet valve fluttering, outlet valve fluttering, inlet-outlet valve fluttering, and check valve fault) serve as the study’s input data. Diverse features including statistical attributes, histogram data, and auto-regressive moving average (ARMA) coefficients are extracted from the vibration signals. To identify the most relevant features, the J48 decision tree algorithm is employed. Five lazy classifiers viz. k-nearest neighbor (kNN), K-star, local kNN, locally weighted learning (LWL), and random subspace ensemble K-nearest neighbors (RseslibKnn) are then used for fault classification, each applied to the individual feature sets. The classifiers achieve commendable accuracy, ranging from 85.33% (K-star and local kNN) to 96.00% (RseslibKnn) for individual features. However, the true innovation lies in feature fusion. Combining the three feature types, statistical, histogram, and ARMA, significantly improves accuracy. When local kNN is used with fused features, the model achieves a remarkable 100% classification accuracy, demonstrating the effectiveness of this approach for reliable fault diagnosis in air compressors.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950705124011535/pdfft?md5=eaa6a9ef6367d7e60620086f5b5b6da1&pid=1-s2.0-S0950705124011535-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Interpretable neuro-cognitive diagnostic approach incorporating multidimensional features 包含多维特征的可解释神经认知诊断方法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112432

{"title":"Interpretable neuro-cognitive diagnostic approach incorporating multidimensional features","authors":"","doi":"10.1016/j.knosys.2024.112432","DOIUrl":"10.1016/j.knosys.2024.112432","url":null,"abstract":"<div><p>Cognitive diagnostics is a pivotal area within educational data mining, focusing on deciphering students’ cognitive status via their academic performance. Traditionally, cognitive diagnostic models (CDMs) have evolved from manually designed probabilistic graphical models to sophisticated automated learning models employing neural networks. Despite their enhanced fitting capabilities, contemporary neuro-cognitive diagnostic models frequently overlook critical process information from students and suffer from reduced interpretability. To address these limitations, this paper introduces a neuro-cognitive diagnostic model that integrates multidimensional features (MFNCD) by incorporating students’ response time as process information. This approach facilitates the simultaneous modeling of students’ response accuracy and response speed using neural networks, thereby enhancing both the fitting capability and precision of the method. Furthermore, a multi-channel attention mechanism is employed to effectively capture the complex interactions between students and exercise characteristics, simulating the process of students answering questions and thereby improving the model's interpretability. Validated on four diverse datasets, MFNCD model demonstrates superior accuracy compared to other state-of-the-art (SOAT) baseline models. Additionally, our experiments confirm significant correlations between cognitive attributes, revealing interesting educational patterns, such as a positive correlation between speed and ability, and between ability and accuracy. These findings provide deeper insights into learning patterns that incorporate multidimensional features and suggest potential pathways for targeted educational interventions.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 lightweight intrusion detection algorithm for IoT based on data purification and a separable convolution improved CNN 基于数据净化和可分离卷积改进型 CNN 的物联网轻量级入侵检测算法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112473

{"title":"A lightweight intrusion detection algorithm for IoT based on data purification and a separable convolution improved CNN","authors":"","doi":"10.1016/j.knosys.2024.112473","DOIUrl":"10.1016/j.knosys.2024.112473","url":null,"abstract":"<div><p>With the rapid development of the IoT (Internet of Things), the network data present the characteristics of large volume and high dimension. Convolutional neural networks (CNNs) have become one of the most important intrusion detection methods due to their advantages in processing high-dimensional data. The conventional intrusion detection model based on CNN lacks an effective data purification means in the process of converting unstructured data into image data, and too many parameters are generated due to the complex structure of the model in the training process, leading to the problems of high time complexity and low detection rate of the model, which limits the application of the CNN in intrusion detection of the IoT. First, based on the principle of liquid molecular distillation, a data purification algorithm (DPA) for unstructured data is proposed in this paper, which reduces the \"redundant\" data generated in the process of converting unstructured data to image data. Second, based on the rigid-motion convolution principle of a separable wavelet, separable convolution is used to improve the CNN structure, and then a lightweight detection algorithm LSCNN (lightweight CNN based on separable convolution) is developed to reduce the number of parameters in the network structure and improve the time efficiency and accuracy of the algorithm. The experimental results on real intrusion detection datasets show that the LSCNN trained on DPA purified data has higher time efficiency and detection accuracy than the conventional CNN, and compared with the conventional machine learning algorithm, it has higher accuracy.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals N-BodyPat：利用脑电信号检测痴呆症和阿尔茨海默病的研究

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112510

{"title":"N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals","authors":"","doi":"10.1016/j.knosys.2024.112510","DOIUrl":"10.1016/j.knosys.2024.112510","url":null,"abstract":"<div><p>The N-body problem is a remarkable research topic in physics. We propose a new feature extraction model inspired by the N-body trajectory and test its feature extraction capability. In the first part of the research, an open-access electroencephalogram (EEG) dataset is used to test the proposed method. This dataset has three classes, namely (i) Alzheimer's Disorder (AD), (ii) frontal dementia (FD), and (iii) control groups. In the second step of the study, the EEG signals were divided into segments of 15 s in length, which resulted in 4,661 EEG signals. In the third part of the study, the proposed new self-organized feature engineering (SOFE) model is used to classify the EEG signals automatically. For this SOFE, two novel methods were presented: (i) a dynamic feature extraction function using a graph of the N-Body orbital, termed N-BodyPat, and (ii) an attention pooling function. A multileveled and combinational feature extraction method was proposed by deploying both methods. A feature selection function using ReliefF and Neighborhood Component Analysis (RFNCA) was used to choose the most informative features. An ensemble k-nearest neighbors (EkNN) classifier was employed in the classification phase. Our proposed N-BodyPat generates seven feature vectors for each channel, and the utilized EEG signal dataset contains 19 channels. In this aspect,133 (=19 × 7) EkNN-based outcomes were created. To attain higher classification performance by employing these 133 EkNN-based outcomes, an iterative majority voting (IMV)-based information fusion method was applied, and the most accurate outcomes were selected automatically. The recommended N-BodyPat-based SOFE achieved a classification accuracy of 99.64 %.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Enhancing visual reinforcement learning with State–Action Representation 用状态-动作表示法强化视觉强化学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112487

{"title":"Enhancing visual reinforcement learning with State–Action Representation","authors":"","doi":"10.1016/j.knosys.2024.112487","DOIUrl":"10.1016/j.knosys.2024.112487","url":null,"abstract":"<div><p>Despite the remarkable progress made in visual reinforcement learning (RL) in recent years, sample inefficiency remains a major challenge. Many existing approaches attempt to address this by extracting better representations from raw images using techniques like data augmentation or introducing some auxiliary tasks. However, these methods overlook the environmental dynamic information embedded in the collected transitions, which can be crucial for efficient control. In this paper, we present STAR: <strong>St</strong>ate-<strong>A</strong>ction <strong>R</strong>epresentation Learning, a simple yet effective approach for visual continuous control. STAR learns a joint state–action representation by modeling the dynamics of the environment in the latent space. By incorporating the learned joint state–action representation into the critic, STAR enhances the value estimation with latent dynamics information. We theoretically show that the value function can still converge to the optima when involving additional representation inputs. On various challenging visual continuous control tasks from DeepMind Control Suite, STAR achieves significant improvements in sample efficiency compared to strong baseline algorithms.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

DCMSL: Dual influenced community strength-boosted multi-scale graph contrastive learning DCMSL：双重影响社区强度增强多尺度图对比学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112472

{"title":"DCMSL: Dual influenced community strength-boosted multi-scale graph contrastive learning","authors":"","doi":"10.1016/j.knosys.2024.112472","DOIUrl":"10.1016/j.knosys.2024.112472","url":null,"abstract":"<div><p>Graph Contrastive Learning (GCL) effectively mitigates label dependency, defining positive and negative pairs for node embeddings. Nevertheless, most GCL methods, including those considering communities, overlooking the simultaneous influence of community and node—a crucial factor for accurate embeddings. In this paper, we propose <strong>D</strong>ual influenced <strong>C</strong>ommunity Strength-boosted <strong>M</strong>ulti-<strong>S</strong>cale Graph Contrastive <strong>L</strong>earning (DCMSL), concurrently considering community and node influence for comprehensive contrastive learning. Firstly, we define dual influenced community strength which can be adaptable to diverse datasets. Based on it, we define node cruciality to differentiate node importance. Secondly, two graph data augmentation methods, NCNAM and NCED, respectively, are put forward based on node cruciality, guiding graph augmentation to preserve more influential semantic information. Thirdly, a joint multi-scale graph contrastive scheme is raised to guide the graph encoder to learn data semantic information at two scales: (1) Propulsive force node-level graph contrastive learning—a node-level graph contrastive loss defining the force to push negative pairs in GCL farther away. (2) Community-level graph contrastive learning—enabling the graph encoder to learn from data on the community level, improving model performance. DCMSL achieves state-of-the-art results, demonstrating its effectiveness and versatility in two node-level tasks: node classification and node clustering and one edge-level task: link prediction. Our code is available at: <span><span>https://github.com/HanChen-HUST/DCMSL</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

HCUKE: A Hierarchical Context-aware approach for Unsupervised Keyphrase Extraction HCUKE：用于无监督关键词提取的层次化语境感知方法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112511

{"title":"HCUKE: A Hierarchical Context-aware approach for Unsupervised Keyphrase Extraction","authors":"","doi":"10.1016/j.knosys.2024.112511","DOIUrl":"10.1016/j.knosys.2024.112511","url":null,"abstract":"<div><p>Keyphrase Extraction (KE) aims to identify a concise set of words or phrases that effectively summarizes the core ideas of a document. Recent embedding-based models have achieved state-of-the-art performance by jointly modeling local and global contexts in Unsupervised Keyphrase Extraction (UKE). However, these models often ignore either sentence- or document-level contexts, leading directly to weak or incorrect global significance. Furthermore, they rely heavily on local significance, making them vulnerable to noisy data, particularly in long documents, resulting in unstable and suboptimal performance. Intuitively, hierarchical contexts enable a more accurate understanding of the candidates, thereby enhancing their global relevance. Inspired by this, we propose a novel Hierarchical Context-aware Unsupervised Keyphrase Extraction method called <strong>HCUKE</strong>. Specifically, HCUKE comprises three core modules: (i) a hierarchical context-based global significance measure module that incrementally learns global semantic information from a three-level hierarchical structure; (ii) a phrase-level local significance measure module that captures local semantic information by modeling the context interaction among candidates; and (iii) a candidate ranking module that integrates the measure scores with positional weights to compute a final ranking score. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Locally differentially private graph learning on decentralized social graph 去中心化社交图谱上的局部差异化私有图谱学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-09-12 DOI: 10.1016/j.knosys.2024.112488

{"title":"Locally differentially private graph learning on decentralized social graph","authors":"","doi":"10.1016/j.knosys.2024.112488","DOIUrl":"10.1016/j.knosys.2024.112488","url":null,"abstract":"<div><p>In recent years, decentralized social networks have gained increasing attention, where each client maintains a local view of a social graph. To provide services based on graph learning in such networks, the server commonly needs to collect the local views of the graph structure, which raises privacy issues. In this paper, we focus on learning graph neural networks (GNNs) on decentralized social graphs while satisfying local differential privacy (LDP). Most existing methods collect high-dimensional local views under LDP through Randomized Response, which introduces a large amount of noise and significantly decreases the usability of the collected graph structure for training GNNs. To address this problem, we present Structure Learning-based Locally Private Graph Learning (SL-LPGL). Its main idea is to first collect low-dimensional encoded structural information called cluster degree vectors to reduce the amount of LDP noise, then learn a high-dimensional graph structure from the cluster degree vectors via graph structure learning (GSL) to train GNNs. In SL-LPGL, we propose a Homophily-aware Graph StructurE Initialization (HAGEI) method to provide a low-noise initial graph structure as learning guidance for GSL. We then introduce an Estimated Average Degree Vector Enhanced Graph Structure Learning (EADEGSL) method to further mitigate the negative impact of LDP noise in GSL. We conduct experiments on four real-world graph datasets. The experimental results demonstrate that SL-LPGL outperforms the baselines.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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