检索结果-内蒙古大学图书馆

An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education

INFORMATION 2022年第4期13卷 197页

作者： Guo, Yu Chen, Yue Xie, Yuanyan Ban, Xiaojuan Univ Sci & Technol Beijing Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China

Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency-inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy.

关键词： study group division course recommendation feature vectors semantic analysis clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of K-means algorithm to Werner deconvolution solutions for depth and image estimations

引用

HELIYON 2022年第11期8卷 e11665页

作者： Eshimiakhe, Daniel Lawal, Kola Ahmadu Bello Univ Dept Phys Zaria Nigeria

One of the most popular techniques for computer-assisted solution estimates for magnetics and gravity field data is Werner deconvolution. The approaches frequently produce erratic results and may not always forecast the maximum number of the geologic entity that produces them due to the intrinsic instability of potential field data. This led to the application of the K-means machine learning algorithm to further enhance the detection of the geologic potential field-generated bodies. Two substances that resembled dikes were combined to form a synthetic magnetic model. Random noise was added to the synthetic data, to make the solutions a bit more complex. Werner deconvolution technique was applied to the synthetic model to generate solutions. K-means unsupervised machine learning algorithm was applied to the generated solutions created by the synthetic data. We further applied this algorithm to real data sets from a mining site. The clustering result shows a good spatial corre-spondence with the geologic model, and the method was able to estimate the precise location and depth of the dike bodies. The proposed method is entirely data-driven and has proven to work in the presence of noise.

关键词： clustering algorithm Potential field Werner deconvolution

来源：评论

学校读者我要写书评

暂无评论

Scalable Grid-Based Data Gathering algorithm for Environmental Monitoring Wireless Sensor Networks

引用

IEEE ACCESS 2020年 8卷 79357-79367页

作者： Padmanaban, Yuvaraj Muthukumarasamy, Manimozhi Vellore Inst Technol VIT Sch Elect Engn Vellore 632014 Tamil Nadu India

Proper utilization of the available low-power is essential to extend the lifetime of the battery-operated wireless sensor networks (WSNs) for environmental monitoring applications. It is mandatory because the batteries cannot be replaced or recharged after deployment due to impracticality. To utilize the power properly, an appropriate cluster-based data gathering algorithm is needed which reduces the overall power consumption of the network significantly. So, in this paper, a grid-based data gathering algorithm called energy-efficient structured clustering algorithm with relay (EESCA-WR) is proposed. In this algorithm, the grids have a single grid leader (GL) and multiple grid relays (GRs). The count of GRs in a grid is variable based on the geographic location of the grid with respect to the destination sink (DS). By doing this, we ensure that the reduction in power consumption is achieved because of the multi-hop short-distance data communications. Also, the GLs are rotated in the right intervals in hybrid modes to minimize the usage of control messages considerably. A hybrid GL selection policy, a threshold-based GL rotation policy, and the policy of allotting dedicated relay-clusters in every grid make the proposed algorithm unique and better for homogeneous and heterogeneous wireless sensor networks. Performance evaluation of the proposed algorithm is carried out by varying the length of the field, the node-density, the grid-count, and the initial energy. Experimental results show that EESCA-WR is extremely scalable, energy-efficient with a minimum number of control messages, and can be used for large scale WSNs.

关键词： clustering algorithm EESCA grid-based data gathering heterogeneous network lifetime wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Evolutionary game-based trajectory design algorithm for mobile sink in wireless sensor networks

引用

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第3期16卷 1-10页

作者： Bencan, Gong Panpan, Dong Peng, Chen Dong, Ren China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Hubei Peoples R China China Three Gorges Univ Hubei Engn Technol Res Ctr Farmland Environm Moni Yichang Peoples R China

In wireless sensor networks, if sink node is stationary, the nodes close to sink will deplete energy faster than those in other areas. The unbalanced energy consumption among the nodes will lead to the energy hole problem, which shortens the network lifetime. To solve the problem, evolutionary game-based trajectory design algorithm for mobile sink in wireless sensor networks is proposed in this article. In evolutionary game model, the average residual energy of each cluster, average intra-cluster energy consumption, and average inter-cluster energy consumption are used to design utility function. The sink will select the cluster with more utility value as its new location and move to the cluster head which has the largest residual energy and the shortest distance to other cluster heads. The simulation results show that the algorithm can effectively balance network load, reduce network energy consumption, prolong the network lifetime, and increase the number of data packets that sink receives.

关键词： Evolutionary game mobile sink wireless sensor networks trajectory design algorithm clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach to Solve the Vehicle Routing Problem with Time Windows and Synchronized Visits In-Home Health Care

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第12期45卷 10637-10652页

作者： Euchi, Jalel Zidi, Salah Laouamer, Lamri Qassim Univ Coll Business & Econ Dept Management Informat Syst & Prod Management Buraydah Saudi Arabia Sfax Univ OLID Lab ISGIS LR19ES21 Sfax 3021 Tunisia

With technological progress in particular telemedicine and health care, the information should meet and serve as well the needs of people and in particular whom with reduced mobility, the elderly as well as people with difficulties to access to medical resources and services. These services should be achieved in a fast and reliable manner based on case priorities. One of the major challenges in health care is the routing and scheduling problem to meet people's needs. Of course, the objective is to considerably minimize costs while respecting priorities according to cases that will face. Through this article, we propose a new technique for home healthcare routing and scheduling problem purely based on an artificial intelligence technique to optimize the offered services within a distributed environment. The automatic learning and search method seem to be interesting to optimize the allocation of visits to beneficiaries. The proposed approach has several advantages in terms of especially cost, efforts, and gaining time. A comparative study was carried out to evaluate the effectiveness of the planned technique compared to previous work.

关键词： Healthcare Visit scheduling Caregivers routing Ant colony system Synchronized visits clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A New Intuitionistic Fuzzy c-means clustering algorithm

A New Intuitionistic Fuzzy c-means Clustering Algorithm

引用

International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

作者： Jiang, Hui Zhou, Xiaoguang Feng, Baisheng Zhang, Mingdong Naval Aviat Inst Flight Simulat & Training Ctr Hu Ludao 125001 LiaoNing Peoples R China

ISBN: (纸本)9781479925650

This paper presents a new intuitionistic fuzzy cmeans (IFCM) clustering algorithm by adapting a new method to calculate the hesitation degree of data point in cluster. From the definition of fuzzy entropy, if a clustering result of a data point has bigger fuzzy entropy, the clustering result should have more uncertainty. It means that we have insufficient information to deal with the clustering of a data point, so the hesitation degree of clustering result of the data point should be greater. Form this opinion, a mathematical model is applied to calculate the hesitation degree of clustering of data point based on fuzzy entropy is given. An IFCM clustering algorithms is present. Experiments are performed using two-dimensional synthetic data-sets referred from previous papers. Results have shown that proposed algorithm is not only effective for linear and nonlinear separation, but also able to describe more information comparing to fuzzy c-means clustering algorithm.

关键词： intuitionistic fuzzy set clustering algorithm fuzzy entropy

来源：评论

学校读者我要写书评

暂无评论

A Mental clustering algorithm based on the Embedded Agent in Wireless Sensor Network

A Mental Clustering Algorithm based on the Embedded Agent in...

引用

3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： Jian, Yu-Mei Chen, Ming Feng, Guo-Fu Shanghai Ocean Univ Coll Informat Technol Shanghai 201306 Peoples R China

ISBN: (纸本)9783037858431

In this paper, a clustering protocol for Embedded Agent sensor network is proposed. In LEACH protocol, the selection probability P of candidate cluster head is constant without thinking about residual energy, which caused the lower residual energy easy to be the candidate cluster head. Compared with traditional structure of wireless sensor network, parameter was optimized. The confidence of the node, select probability and residual energy are the parameter of candidate cluster head, communication cost as the parameter of final cluster node. Compared with LEACH and ASCH, EACA take more parameters than LEACH. Results show that EACA can balance the energy load in the network, and effectively reduce more energy consumption.

关键词： WSN Mental factor Agent clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

MULTI-CORE BASED PARALLEL N-PATH LABELING HKM clustering algorithm

MULTI-CORE BASED PARALLEL N-PATH LABELING HKM CLUSTERING ALG...

引用

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Liao, Kaiyang Liu, Guizhong Qiao, Zhen Liu, Chaoteng Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)9781479916030

The detection of useful patterns in large datasets has attracted considerable interest recently. The hierarchical K-means clustering algorithm (HKM) is very efficient in large scale data analysis. It has been extensively used for building visual vocabulary in large scale image/video retrieval. However, the accuracy and speed of HKM still have room for improvement. In this paper, we propose a Parallel N-path labeling HKM clustering algorithm (PNHKM) which improves on the HKM clustering algorithm in the following ways. Firstly, we adopt a Greedy N-best Paths Labeling (GNPL) method to improve the clustering accuracy. Secondly, we focus on developing a parallel clustering algorithm for multicore processors. Our results confirm that the PNHKM is much faster and more effective.

关键词： Parallel algorithm clustering algorithm video retrieval

来源：评论

学校读者我要写书评

暂无评论

Investigating populations in generalized Darwinism

引用

BIOLOGY & PHILOSOPHY 2020年第1期35卷 1-27页

作者： Baraghith, Karim Heinrich Heine Univ Dusseldorf Dusseldorf Ctr Log & Philosophy Sci Dept Philosophy Univ Str 1 D-40225 Dusseldorf Germany

Darwinian evolution is a population-level phenomenon. This paper deals with a structural population concept within the framework of generalized Darwinism (GD), resp. within a generalized theory of evolution. According to some skeptical authors, GD is in need of a valid population concept in order to become a practicable research program. Populations are crucial and basic elements of any evolutionary explanation-biological or cultural-and have to be defined as clearly as possible. I suggest the "causal interactionist population concept" (CIPC), by R. Millstein for this purpose, and I will try to embed the approach into a generalized evolutionary perspective by mathematically formalizing its key definitions. Using graph-theory, (meta-) populations as described in the CIPC can serve as proper clusters of evolutionary classification based on the rates of interactions between their elements. I will introduce the concept of a cohesion index (CI) as a measurement of possible population candidates within a distribution of elements. The strength of this approach lies in its applicability and interactions are relatively easy to observe. Furthermore, problems of clustering tokens (e.g. of cultural information) via typicality, e.g. their similarity in intrinsic key characteristics, can be avoided, because CIPC is a (mainly) external approach. However, some formal problems and conceptual ambiguities occur within a simple version of this CI, which will be addressed in this paper as well as some possible applications.

关键词： Population concept Generalized Darwinism Cultural evolution CIPC Graph theory clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Multiscale topology optimization for non-uniform microstructures with hybrid cellular automata

引用

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第2期62卷 757-770页

作者： Jia, Jiao Da, Daicong Loh, Cha-Liang Zhao, Haibin Yin, Sha Xu, Jun Beihang Univ Vehicle Energy & Safety Lab VESL Beijing 100191 Peoples R China Beihang Univ Sch Transportat Sci & Engn Dept Automot Engn Beijing 100191 Peoples R China Univ Paris Est Lab Modelisat & Simulat Multi Echelle MSME UMR CNRS 8208 5 Bd Descartes F-77454 Marne La Vallee France Changshu Automot Trim Co Ltd Changshu 215500 Peoples R China

Based on hybrid cellular automata (HCA), we present a two-scale optimization model for heterogeneous structures with non-uniform porous cells at the microscopic scale. The method uses theK-means clustering algorithm to achieve locally nonperiodicity through easily obtained elemental strain energy. This energy is used again for a two-scale topological optimization procedure without sensitivity analysis, avoiding drastically the computational complexity. Both the experimental tests and numerical results illustrate a significant increase in the resulting structural stiffness with locally nonperiodicity, as compared to using uniform periodic cells. The effects of parameters such as clustering number and adopted method versus classical Optimality Criteria (OC) are discussed. Finally, the proposed methodology is extended to 3D two-scale heterogeneous structure design.

关键词： Concurrent optimization Hybrid cellular automata clustering algorithm Non-uniform microstructure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：