A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern.

Enhancing data pipelines for forecasting student performance: integrating feature selection with cross-validation

Educators seek to harness knowledge from educational corpora to improve student performance outcomes. Although prior studies have compared the efficacy of data mining methods (DMMs) in pipelines for forecasting student success, less work has focused on identifying a set of relevant features prior to model development and quantifying the stability of feature selection techniques. Pinpointing a subset of pertinent features can (1) reduce the number of variables that need to be managed by stakeholders, (2) make “black-box” algorithms more interpretable, and (3) provide greater guidance for faculty to implement targeted interventions. To that end, we introduce a methodology integrating feature selection with cross-validation and rank each feature on subsets of the training corpus. This modified pipeline was applied to forecast the performance of 3225 students in a baccalaureate science course using a set of 57 features, four DMMs, and four filter feature selection techniques. Correlation Attribute Evaluation (CAE) and Fisher’s Scoring Algorithm (FSA) achieved significantly higher Area Under the Curve (AUC) values for logistic regression (LR) and elastic net regression (GLMNET), compared to when this pipeline step was omitted. Relief Attribute Evaluation (RAE) was highly unstable and produced models with the poorest prediction performance. Borda’s method identified grade point average, number of credits taken, and performance on concept inventory assessments as the primary factors impacting predictions of student performance. We discuss the benefits of this approach when developing data pipelines for predictive modeling in undergraduate settings that are more interpretable and actionable for faculty and stakeholders.

Fuzzy Evaluation of Kansei Attributes Using Convolutional Neural Networks

The emotional needs for products have increased significantly with the recent improvements in living standards. Attribute evaluation forms the core of Kansei engineering in emotion-oriented products, and is practically quite subjective in nature. Essentially, attribute evaluation is a fuzzy classification task, whose quantitative results change slightly with statistical time and statistical objects, making it difficult to accurately describe using standard mathematical models. In this paper, we propose a novel deep-learning-assisted fuzzy attribute-evaluation (DLFAE) method, which could generate quantitative evaluation results. In comparison to existing methods, the proposed method combines subjective evaluation with convolutional neural networks, which facilitates the generation of quantitative evaluation results. Additionally, this strategy has better transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency.

Adjustable Fuzzy Rough Reduction: A Nested Strategy

As a crucial extension of Pawlak's rough set, a fuzzy rough set has been successfully applied in real-valued attribute reduction. Nevertheless, the traditional fuzzy rough set is not provided with adjustable ability due to the maximal and minimal operators. It follows that the associated measure for attribute evaluation is not always appropriate. To alleviate such problems, a novel adjustable fuzzy rough set model is presented and further introduced into the parameterized attribute reduction. Additionally, the inner relationship between the appointed parameter and the reduct result is discovered, and thereby a nested mechanism is adopted to accelerate the searching procedure of reduct. Experiments demonstrate that the proposed heuristic algorithm can offer us more stable reducts with higher computational efficiency as compared with the traditional approaches.

An Exploratory Analysis of Museum Attributes from the Perspective of Tourists and Residents: The Case of Thyssen-Bornemisza National Museum, Madrid, Spain

Attribute evaluation provides an understanding of the perceived quality and subjective value of the museum visitor experience. The principal contribution of this paper is to analyze the attributes perceived by tourists and the local community (Madrid residents) of the Thyssen-Bornemisza National Museum (Madrid, Spain), utilizing the results from choice experiment and willingness-to-pay questionnaires. To analyze in depth the assessment regarding the museum attributes and the visitor perceptions of them, the relevance-determination model was applied. Data collection was achieved with a questionnaire using a convenience sample of international tourists and the local community, providing a total of 775 valid surveys. The results of the application of the relevance-determination analysis (RDA) show that there are two types of attributes: higher-impact core and lower-importance attributes. The attributes with the highest subjective value perceived by interviewed tourists and interviewed residents are the location, the building, and the permanent collection. These results show that there are substantial differences between the perception and appreciation of these attributes by interviewed residents and interviewed tourists. The results provide valuable information that can be applied in practice to devise strategies for economic and socio-cultural sustainability aimed at improving decision-making in museum management.