Green roofs are an important part of the ecological system in high-density cities and a crucial component of community public green spaces. Analyzing preferences of residents during spatial planning can help improve t...
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Green roofs are an important part of the ecological system in high-density cities and a crucial component of community public green spaces. Analyzing preferences of residents during spatial planning can help improve their satisfaction and efficiency in using community rooftop spaces. This study uses k-modesalgorithm to perform cluster analysis on questionnaires from 699 residents, and summarizes user profiles with significant characteristics by combing the comprehensive ratings of residents regarding different roof space functions. The study shows that differences in preferences among people are not only related to demographic characteristics but also to their interests and environmental perceptions. For example, low-to-middle-income groups, being price-sensitive, tend to reduce energy consumption expenditure. The highly educated population, driven by social needs, shows a clear preference for activity spaces and sports facilities. The elderly population, emphasizing healthy eating and rural memories, gives higher ratings to agricultural spaces. Finally, this study explores the correlation between certain resident characteristics and their preferences for green roof space functions, and proposes a "1+X" spatial configuration strategy dominated by Landscape Leisure Space, with Ecological Low-carbon Space, Agricultural Production Space, and Activity Gathering Space as options, in order to optimize community green roof spaces guided by resident needs.
The improved k-modes clustering algorithm for classification attribute data is proposed, in which the classification attribute data of seafood traceable data are taken as an example and the k-modesalgorithm is improv...
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The improved k-modes clustering algorithm for classification attribute data is proposed, in which the classification attribute data of seafood traceable data are taken as an example and the k-modesalgorithm is improved by improving clustering accuracy and process. The improved k-modes clustering algorithm combines density and distance to select the initial center point of cluster, which ensures the effectiveness of the initial cluster center and avoids falling into local extreme points so that clustering process can be simplified. The improved k-modes clustering algorithm redefines the clustering mode and comprehensively considers the representative of all attribute values in sample attributes to clustering category so that the distance measure will be improved and that the clustering effect can be optimized. Experiments show that the improved k-modesclustering method has good clustering effect on standard data sets, which is universal to classification attribute data and worthy of being more popular and further improving in practice.
With the popularity of social media, mobile devices and the Internet, a large amount of multimodal data (e.g, text, image, audio, video, etc.) is increasingly being outsourced to cloud to save local computing and stor...
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With the popularity of social media, mobile devices and the Internet, a large amount of multimodal data (e.g, text, image, audio, video, etc.) is increasingly being outsourced to cloud to save local computing and storage costs. To search through encrypted multimodal data in the cloud, privacy-preserving cross-modal retrieval (PPCMR) techniques have attracted extensive attention. However, most of the existing PPCMR schemes lack the ability to resist quantum attacks and have low search efficiency on large-scale datasets. To solve above problems, we first propose a basic PPCMR scheme FECMR using the enhanced Single-key Function-hiding Inner Product Functional Encryption for Binary strings (SFB-IPFE) and cross-modal hashing technology, which achieves the measurement of similarity over encrypted multimodal data while resisting quantum attacks. Then, we design an efficient index kM-tree utilizing the k-modes clustering algorithm. On this basis, we propose an improved scheme FECMR+, which achieves sub-linear search complexity. Finally, formal security analysis proves that our schemes are secure against quantum attacks, and extensive experiments prove that our schemes are efficient and feasible for practical application.
The implementation of an efficient adaptive e-learning system requires the construction of an effective student model that represents the student’s characteristics, among those characteristics, there is the learning ...
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The implementation of an efficient adaptive e-learning system requires the construction of an effective student model that represents the student’s characteristics, among those characteristics, there is the learning style that refers to the way in which a student prefers to learn. knowing learning styles helps adaptive E-learning systems to improve the learning process by providing customized materials to students. In this work, we have proposed an approach to identify the learning style automatically based on the existing learners’ behaviors and using web usage mining techniques and machine learning algorithms. The web usage mining techniques were used to pre-process the log file extracted from the E-learning environment and capture the learners’ sequences. The captured learners’ sequences were given as an input to the k-modes clustering algorithm to group them into 16 learning style combinations based on the Felder and Silverman learning style model. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system’s log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.
Data clustering is an important unsupervised technique in data mining which aims to extract the natural partitions in a dataset without a priori class information. Unfortunately, every clustering model is very sensiti...
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ISBN:
(纸本)9789897582189
Data clustering is an important unsupervised technique in data mining which aims to extract the natural partitions in a dataset without a priori class information. Unfortunately, every clustering model is very sensitive to the set of randomly initialized centers, since such initial clusters directly influence the formation of final clusters. Thus, determining the initial cluster centers is an important issue in clustering models. Previous work has shown that using multiple clustering validity indices in a multiobjective clustering model (e.g., MODEk-modes model) yields more accurate results than using a single validity index. In this study, we enhance the performance of MODEk-modes model by introducing two new initialization methods. The two proposed methods are the k-modes initialization method and the entropy initialization method. The two proposed methods are tested using ten benchmark real life datasets obtained from the UCI Machine Learning Repository. Experimental results show that the two initialization methods achieve significant improvement in the clustering performance compared to other existing initialization methods.
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