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Analysis of college students' physical education learning effect and quality based on text data fusion

ADVANCES IN CONTINUOUS AND DISCRETE MODELS 2025年第1期2025卷 1-15页

作者： Dai, Zhiqing Hohai Univ Business Sch Nanjing 210000 Peoples R China

Improving the effectiveness of education requires progress on both sides of the learning equation: students need to enhance their learning capabilities, and teachers need to refine their teaching strategies. The proposed approach introduces a sophisticated algorithm for improving education, enhancing students' learning capabilities, and refining teachers' instructional strategies. A multi-objective educational text clustering algorithm is introduced, combining the K-means algorithm's simplicity and efficiency with the Krill Swarm algorithm's robust global search capabilities. The algorithm uses K-means clustering results as the initial population for the krill swarm, ensuring a well-distributed starting point for the optimization process. Genetic crossover and mutation operations are introduced to overcome the potential stagnation of krill swarm populations in local optima. The krill population's motion is guided by three key components: induced motion, foraging motion, and random diffusion. These strategies ensure a balance between exploration and exploitation. The algorithm employs a fitness function based on cosine similarity and Euclidean distance, capturing semantic similarity between educational text items and ensuring clusters are compact and well-separated. This hybrid algorithm achieves a sophisticated balance between computational efficiency and clustering quality, offering a powerful tool for improving educational outcomes through advanced text analysis. For students, it enables better organization and access to learning materials, facilitating personalized learning pathways. At the same time, it provides insights into educational content, helping teachers identify patterns, gaps, and areas for improvement in their teaching resources.

关键词： Analysis of College Students Physical Education Learning Effect clustering algorithm

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Pointwise norm-based clustering of data in arbitrary dimensional space

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Communications in Statistics Case Studies Data Analysis and Applications 2023年第2期9卷 121-134页

作者： Modak, Soumita Faculty Department of Statistics University of Calcutta Basanti Devi College Kolkata India

In this paper, a novel nonparametric norm-based clustering algorithm is proposed to classify real-valued continuous data sets given in arbitrary dimensional space. For data univariate, multivariate or high-dimensional, with the number of study variables close to or larger than the data size, our straightforward algorithm is implemented throughout under an univariate set-up, where we make use of the observation-wise (or pointwise) norms which quantify the distances of the observations from the origin zero or the null vector. The method begins with determination of the sample quantile using nonparamteric bootstrapping on the computed norms and always converges independently. By its design, the suggested algorithm is fast enough to detect the number of existing clusters itself and to form well-defined groups. Data study demonstrates its competitiveness in comparison to 2 popular clustering algorithms K-means and K-medoids. © 2023 Taylor & Francis Group, LLC.

关键词： arbitrary-dimensional data set bootstrapping clustering algorithm high-dimensional applicability nonparametric method pointwise norm

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Acquaint based grid clustering algorithm for Underwater wireless sensor network

Acquaint based grid clustering algorithm for Underwater wire...

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Sundarameena, V. Priyatharisini, D. Manakula Vinayagar Inst Technol Dept CSE Pondicherry India

ISBN: (纸本)9781479938346

Underwater wireless sensor network (UWSN) is an important emerging research area in wide range of application, unlike the terrestrial network it uses the acoustic signal which has a unique characteristics like limited bandwidth, high and variable propagation delay, transmit energy, minimum network lifetime and so on. This paper proposes an efficient clustering algorithm having 3D-GRID network architecture and it uses limited control information for data gathering to improve the energy efficiency of the network. The GRID clustering method supports 3D deployment based on geographical location of the sensors which has cluster heads (CH) and non cluster heads (NCH). All the cluster heads are present in the center of the network and all the non-cluster head nodes are in minimum distance to the cluster head. This network structure helps to avoid the control packets for intra communication. For inter communication the CH data packet contains the control packet for route establishment and data transmission. Thus this technique reduce the usage of energy while communication and improve the lifetime of the network.

关键词： UWSN Acoustic Signal clustering algorithm GRID network architecture

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Attraction Recommendation and Itinerary Planning for Smart Rural Tourism Based on Regional Segmentation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 959-969页

作者： Chen, Ruiping Zhou, Yanli Zhang, Dejun Foshan Polytech Compliance Off Foshan 528137 Peoples R China Foshan Polytech Sch Culture Tourism & Creat Foshan 528137 Peoples R China

As the rural tourism industry develops, effective attraction recommendations and planning are crucial for the tourist experience. Then, a rural scenic spot tourism recommendation and planning technology based on regional segmentation was proposed. The scenic area was divided into multiple grids based on tourist check-in behaviour, and the interest and influence of the scenic area were associated with the grid check-in behaviour. Content recommendation was achieved through two factors: popularity and regional location. And considering the sparsity of data in the recommendation, clustering algorithms were introduced to model tourist check-in behaviour based on factors such as time and regional location, and content recommendation was achieved through tourist preferences. In the performance analysis of recommendation models, the proposed model has an accuracy of 0.965 and 0.956 on the Gowalla and Yelp datasets, respectively, which is superior to other models. Comparing the recommendation loss performance of different models, the proposed model has an RMSE loss of 0.120 on the Gowalla dataset, which is superior to other models. In practical application analysis, when the recommended number is 5, the accuracy and recall of the proposed model are 0.138 and 0.069, respectively, which are superior to other models. In tourism itinerary planning, the overall planning time of the model is the shortest. Therefore, the proposed model has excellent application effects, and the research content provides important technical references for tourist travel and rural tourism destination planning.

关键词： Regional division trip planning recommended tourist attractions clustering algorithm time factor

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Image Classification and Retrieval of TCM Materials Based on Feature Enhancement

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TRAITEMENT DU SIGNAL 2024年第6期41卷 3223-3233页

作者： Zhu, Xianghe Hubei Univ Chinese Med Coll Informat Engn Wuhan 430065 Peoples R China Hubei Shizhen Lab Wuhan 430000 Peoples R China

With the global promotion and application of Traditional Chinese Medicine (TCM), the identification and management of TCM materials have become critical issues that need to be addressed. Traditional methods for identifying TCM materials rely on manual experience and expert knowledge, leading to low efficiency and a high likelihood of errors. With the development of image processing technology, image-based classification and retrieval of TCM materials have gradually become a research hotspot. However, existing methods often encounter challenges such as insufficient classification accuracy and low retrieval efficiency when faced with the diversity and complexity of TCM material images. Therefore, how to effectively extract image features and improve the accuracy of classification and retrieval has become the central challenge in current research. Traditional image features, such as color, shape, and texture, are commonly used in the classification and retrieval of TCM materials. However, these features are often unable to fully reflect the diversity and detail of the materials, especially when distinguishing between morphologically similar materials. Although deep learning techniques have made breakthroughs in the field of image processing, the application of deep learning in TCM material image classification still faces many challenges due to insufficient data and annotation. A combination of technologies, including superpixel segmentation, feature point extraction, and clustering encoding, provides an effective approach to improving classification and retrieval performance and warrants further research. A kind of feature enhancement-based method for the classification and retrieval of TCM material images was proposed in this study, consisting of four main components. First, fine image segmentation was performed using the Simple Linear Iterative clustering (SLIC) superpixel segmentation technique to extract features;second, an initial classification method based on f

关键词： TCM materials image classification image retrieval superpixel segmentation feature extraction clustering algorithm reordering

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A performance evaluation method of new business model based on grey correlation algorithm

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International Journal of Information Technology and Management 2025年第1-2期24卷 92-105页

作者： Wang, Yan Department of Business Administration Xingzhi College of Xi'an University of Finance and Economics Xi'an710038 China

A new business model performance evaluation method based on grey correlation algorithm is designed to solve the problems of large evaluation error and low key of screening indicators in the new business model performance evaluation. First, analyse the new business model and screen the performance evaluation indicators of the new business model. Then, the clustering algorithm is used to determine the cluster family of each index, extract the performance evaluation index characteristics of the new business model, and construct the performance evaluation index system. Finally, the grey correlation algorithm is used to determine the grey correlation degree between the indicators, quantify the evaluation indicators, build a grey correlation model for the performance evaluation of the new business model, and realise the performance evaluation. The experimental results show that the evaluation error of the proposed evaluation method is only 2%, and the key degree of the selected index is higher than 90%, which is increased by more than 5%. This method has higher practical application value. © 2025 Inderscience Enterprises Ltd.

关键词： clustering algorithm consistency check correlation sequence grey correlation algorithm main sequence new business model performance evaluation

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A Geometric clustering algorithm and Its Applications to Structural Data

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18th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Xu, Shutan Zou, Shuxue Wang, Lincong Jilin Univ Coll Comp Sci & Technol Changchun 130023 Jilin Peoples R China

ISBN: (纸本)9783319052694;9783319052687

An important feature of structural data especially those from structural determination and protein-ligand docking programs is that their distribution could be both uniform and non-uniform. Traditional clustering algorithms developed specifically for non-uniformly distributed data may not be adequate for their classification. Here we present a geometric partitional algorithm that could be applied to both uniformly and non-uniformly distributed data. The algorithm is a top-down approach that recursively selects the outliers as the seeds to form new clusters until all the structures within a cluster satisfy certain requirements. The applications of the algorithm to a diverse set of data from NMR structure determination, protein-ligand docking and simulation show that it is superior to the previous clustering algorithms for the identification of the correct but minor clusters. The algorithm should be useful for the identification of correct docking poses and for speeding up an iterative process widely used in NMR structure determination.

关键词： clustering algorithm structure determination protein-ligand docking structure classification

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SDEC: Spectrum-Aware Degree-Ranking-Based Energy-Efficient clustering algorithm for Multi-hop CRN

SDEC: Spectrum-Aware Degree-Ranking-Based Energy-Efficient C...

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Joint International Conference on Pervasive Computing and Web Society

作者： Kong, Lili Wang, Yali Wei, Yifei Wang, Haolong Man, Yi Wang, Xiaojun Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitorin Sch Elect Engn Beijing 100876 Peoples R China Dublin City Univ Rince Inst Sch Elect Engn Dublin 9 Ireland

ISBN: (纸本)9783319092652;9783319092645

Cognitive radio is a key technology for promoting spectrum efficiency by exploiting the existence of spectrum holes under the current static spectrum allocation policy. However, using spectrum bands in an opportunistic way has brought some significant challenges such as connectivity and energy consumption in dynamic environment. In this paper, we propose an adaptive SDEC (spectrum-aware degree-ranking-based energy-efficient clustering algorithm) applying to multi-hop wireless CRN, which incorporates the spectrum quality and the number of neighbor nodes into consideration. Results of simulations illustrate that the algorithm can well fit into CR network and achieve significant improvement both on the load balancing and the average power consumption. Moreover, SDEC has preferable stability and validity due to its low complexity and quick convergence under dynamic spectrum change.

关键词： Multi-hop Wireless CRN clustering algorithm Spectrum Quality Degree Ranking

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A New clustering algorithm and Relevant Theoretical Analysis for Ad-hoc Networks

A New Clustering Algorithm and Relevant Theoretical Analysis...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Wu, Jing Meng, Guang-Xue North China Inst Sci & Technol Coll Comp Yanjiao 101601 Hebei Peoples R China

ISBN: (纸本)9783038351153

In ad-hoc networks, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA's problem that "it only considers on intra-cluster stability, and neglects the inter-cluster stability", a new clustering algorithm (NCA) was proposed. Firstly, NCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that NCA can improve cluster stability and reduce clustering overheads.

关键词： Ad-hoc networks clustering algorithm Stability

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Fuzzy clustering of structural members for efficient design optimization

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Asian Journal of Civil Engineering 2023年第3期24卷 711-725页

作者： Patil, Ishwaragouda S. Joshi, Sharad G. Patil, Shabarish V. Department of Civil Engineering KLE Institute of Technology Hubballi 580027 India KLE Institute of Technology Hubballi 580027 India

The basis for grouping structural elements is a tradeoff between optimization in design and construction. The grouped elements should have common optimal design specifications that are structurally safe and convenient for construction. Fuzzy clustering provides flexibility to regroup the elements using membership values indicating their affinity to a cluster for the considered parameter. The proposed Fuzzy C-Means algorithm demonstrates grouping efficacy based on a dominant design parameter and also provides leverage for regrouping of an element considering other design or construction aspects. The method is evaluated by applying it to footings of four buildings with varying complexity, taking load on footing as basis for grouping. Distinct and homogeneous clusters are obtained with small computational effort, with further scope for regrouping members based on other parameters using their membership values for the parameter considered. The paper illustrates various examples for grouping and optimizing structural design. The proposed methodology proved to be very efficient in grouping structural elements that optimizes the overall structural design. In case of truss example nearly 7% reduction in weight was found compared to previous research. Optimization methodology is also proposed for rebar design of Reinforced Cement Concrete structures with fixed topology and cross-sectional dimensions. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Automated grouping clustering algorithm Fuzzy C-means clustering Narrow design search Structural optimization

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