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An Improved algorithm of Nearness Degree of Incidence Based on Grey Neural Network

IEEE ACCESS 2020年 8卷 207044-207053页

作者： Geng, Xinyu Ye, Jinchi Xia, Zhen Mu, Yukun Zhu, Liping Southwest Petr Univ Sch Comp Sci Chengdu 610500 Peoples R China

The traditional model of grey nearness degree of incidence contains some inherent limitations in the calculation of data sequences. It does not consider the impacts of certain data on degree of incidence when there are significant differences in orders of magnitude between adjacent data in the same sequence, and big errors may occur in the calculation of some special oscillation sequences. In response to these problems, we propose a new improved method, which uses the characteristics of the model of grey nearness degree of incidence and introduces a neural network algorithm to define a grey neural network-nearness degree of incidence. Thereby, a model of nearness degree of incidence is established based on grey neural network. Then we apply a new model to the field of data mining. According to the clustering algorithm, we take all the degrees of incidence as the variables of the distance metric function, and use the clustering algorithm of data mining for data analysis. Finally, through simulation experiments, we verify the effectiveness of the clustering algorithm under the new distance metric definition. The experimental results show that, compared with other methods, the computational outcomes of the improved model are more consistent with the actual situation. The cluster algorithm with the model used can deliver results that have a high accuracy, so the new model can be applicated in a wide range of fields.

关键词： clustering algorithms Data models Analytical models Mathematical model Measurement Artificial neural networks Training Grey neural network nearness degree of incidence data mining clustering algorithm

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Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework

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International Journal for Consumer and Product Safety 2023年第4期30卷

作者： Dajie Zuo Cheng Qian Daiquan Xiao Xuecai Xu Hui Wang a School of Transportation and Logistics Southwest Jiaotong University Chengdu China b Shanghai Municipal Engineering Design Institute(Group) Co. Ltd Shanghai China c School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China d Wuhan Huake Quanda Transport Planning and Design Consulting Co. Ltd Wuhan China

With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras’ high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.

关键词： Injury severity prediction deep learning clustering algorithm OPTICS recurrent neural network

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Single-Valued Neutrosophic clustering algorithms Based on Similarity Measures

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JOURNAL OF CLASSIFICATION 2017年第1期34卷 148-162页

作者： Ye, Jun Shaoxing Univ Shaoxing Zhejiang Peoples R China

clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) can describe and handle indeterminate and inconsistent information, while fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with it. To cluster the information represented by single-valued neutrosophic data, this paper proposes single-valued neutrosophic clustering algorithms based on similarity measures of SVNSs. Firstly, we introduce a similarity measure between SVNSs based on the min and max operators and propose another new similarity measure between SVNSs. Then, we present clustering algorithms based on the similarity measures of SVNSs for the clustering analysis of single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the single-valued neutrosophic clustering algorithms.

关键词： Single-valued neutrososophic set clustering algorithm Similarity measure Similarity matrix

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A Text Mining Model Based on Improved Density clustering algorithm

A Text Mining Model Based on Improved Density Clustering Alg...

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2013 IEEE 4th International Conference on Electronics Information and Emergency Communication

作者： Chen QiLu Jianfeng Zhang Hao CIMS Center of Tongji University

The clustering algorithm based on density is widely used on text mining model, for example the DBSCAN（density- based spatial clustering of application with noise） algorithm. DBSCAN algorithm is sensitive in choose of parameters, it is hard to find suitable parameters. In this paper a method based on k-means algorithm is introduced to estimate the ε neighborhood and minpts. Finally an example is given to show the effectiveness of this algorithm.

关键词： clustering algorithm text mining density clustering

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Infrared polymorphic target recognition based on single step cascade neural network

Infrared polymorphic target recognition based on single step...

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Annual Conference of Chinese-Society-of-Optical-Engineering (CSOE) - AI in Optics and Photonics (AOPC)

作者： Hu, Zhuangzhuang Qin, Hanlin Peng, Xin Yue, Tong Yue, Heng Luo, Guohui Zhu, Wenrui Xidian Univ Sch Phys & Optoelect Engn Xian 710071 Peoples R China

ISBN: (数字)9781510634572

ISBN: (纸本)9781510634572

Infared ship recognition has many applications in port supervision and management. However, when the imaging distance is long or the target changes are obvious, it is difficult to achieve accurate detection and recognition by traditional methods. In this paper, we designed a single step cascade neural network that consists of three parts: feature extraction module, scale transform module and classification regression module. Firstly, the VGG network is used to extract the different level features of the target images. Then the scale transform module is used to fuse the high-level features and the low-level features to reflect the semantic information and shallow information of the targets more completely. The generated region of interest is input to classification regression module that predicts the targets location and classes. The main contribution of this paper is to combine the specific problems of infrared polymorphic ships detection and recognition. The clustering algorithm is used to generate the appropriate anchors to adapt our targets, and the attention mechanism is introduced into the model training process. Compared with the traditional detection and recognition methods, the proposed single step cascade neural network achieves the better average precision in polymorphic ships.

关键词： infrared image target recognition clustering algorithm neural network

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Multi-resource scheduling and routing for emergency recovery operations

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2020年 50卷 101780页

作者： Bodaghi, Behrooz Shahparvari, Shahrooz Fadaki, Masih Lau, Kwok Hung Ekambaram, Palaneeswaran Chhetri, Prem Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic Australia RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic Australia

Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.

关键词： Expendable resources Non-expendable resources Emergency recovery operations Multi-resource scheduling Heuristics algorithms clustering algorithm Coronavirus outbreak case

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Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries

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APPLIED SOFT COMPUTING 2018年 73卷 816-828页

作者： Khalili-Damghani, Kaveh Abdi, Farshid Abolmakarem, Shaghayegh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

This paper proposes a hybrid soft computing approach on the basis of clustering, rule extraction, and decision tree methodology to predict the segment of the new customers in customer-centric companies. In the first module, K-means algorithm is applied to cluster the past customers of company on the basis of their purchase behavior. In the second module, a hybrid feature selection method based on filtering and a multi-attribute decision making method is proposed. Finally, On the basis of customers' characteristics and using decision tree analysis, IF-THEN rules are mined. The proposed approach is applied in two case studies in the field of insurance and telecommunication in order to predict potentially profitable leads and outline the most influential features available to customers in order to perform this prediction. The results validate the efficacy and applicability of proposed approach to handle real-life cases. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Data mining clustering algorithm Feature selection Rule mining Decision tree

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Evolving the attribute flow for dynamical clustering in signed networks

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CHAOS SOLITONS & FRACTALS 2018年 110卷 20-27页

作者： Li, Hui-Jia Bu, Zhan Li, Yulong Zhang, Zhongyuan Chu, Yanchang Li, Guijun Cao, Jie Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 100080 Peoples R China Nanjing Univ Finance & Econ Jiangsu Prov Key Lab E Business Nanjing 210003 Jiangsu Peoples R China Cent Univ Finance & Econ Sch Stat & Math Beijing 100080 Peoples R China Civil Aviat Univ China Econ & Management Coll Tianjin 300300 Peoples R China Civil Aviat Univ China Res Ctr Beijing Tianjin Hebei Civil Aviat Coordin Tianjin 300300 Peoples R China

In real networks, clustering is of great value to the analysis, design, and optimization of numerous complex systems in natural science and engineering, e.g. power supply systems, modern transportation networks, and real-world networks. However, the majority of them simply pay attention to the density of edges rather than the signs of edges as the attributes to cluster, which usually suffer a high-level computational complexity. In this paper, a new rule is proposed to update the attributes flow, which can guarantee network clustering reach a state of optimal convergence. The positive and negative update rule we introduced, represent the cooperative and hostile relationship, and the attribute configuration will convergence and one can identify the reasonable cluster configuration automatically. An algorithm with high efficiency is proposed: a nearly linear relationship is found between the time complexity and the size in sparse networks. Finally, we conduct the verification of the algorithmic performance by a representative simulations on Correlates of War data. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Signed networks Attribute flow clustering algorithm Dynamical systems Convergence and divergence

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Insight About Nonlinear Dimensionality Reduction Methods Applied to Protein Molecular Dynamics 7th

Insight About Nonlinear Dimensionality Reduction Methods App...

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7th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： de Souza, Vinicius Carius Goliatt, Leonardo Capriles, Priscila V. Z. Univ Fed Juiz de Fora Juiz De Fora Brazil

ISBN: (纸本)9783030179359;9783030179342

The advance in molecular dynamics (MD) techniques has made this method common in studies involving the discovery of physicochemical and conformational properties of proteins. However, the analysis may be difficult since MD generates a lot of conformations with high dimensionality. Among the methods used to explore this problem, machine learning has been used to find a lower dimensional manifold called "intrinsic dimensionality space" which is embedded in a high dimensional space and represents the essential motions of proteins. To identify this manifold, Euclidean distance between intra-molecular Ca atoms for each conformation was used. The approaches used were combining data dimensionality reduction (AutoEncoder, Isomap, t-SNE, MDS, Spectral and PCA methods) and Ward algorithm to group similar conformations and find the representative structures. Findings pointed out that Spectral and Isomap methods were able to generate low-dimensionality spaces providing good insights about the classes separation of conformations. As they are nonlinear methods, the low-dimensionality generated represents better the protein motions than PCA embedding, so they could be considered alternatives to full MD analyses.

关键词： Molecular dynamics Manifold clustering algorithm

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Change point estimation in a dynamic stochastic block model

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4330-4388页

作者： Monika Bhattacharjee Moulinath Banerjee George Michailidis Informatics Institute University of Florida Gainesville Department of Statistics University of Michigan Ann Arbor Informatics Institute Department of Statistics University of Florida Gainesville

We consider the problem of estimating the location of a single change point in a network generated by a dynamic stochastic block model mechanism. This model produces community structure in the network that exhibits change at a single time epoch. We propose two methods of estimating the change point, together with the model parameters, before and after its occurrence. The first employs a least-squares criterion function and takes into consideration the full structure of the stochastic block model and is evaluated at each point in time. Hence, as an intermediate step, it requires estimating the community structure based on a clustering algorithm at every time point. The second method comprises the following two steps: in the first one, a least-squares function is used and evaluated at each time point, but ignoring the community structure and only considering a random graph generating mechanism exhibiting a change point. Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters. The first method, since it requires knowledge of the community structure and hence clustering at every point in time, is significantly more computationally expensive than the second one. On the other hand, it requires a significantly less stringent identifiability condition for consistent estimation of the change point and the model parameters than the second method; however, it also requires a condition on the misclassification rate of misallocating network nodes to their respective communities that may fail to hold in many realistic settings. Despite the apparent stringency of the identifiability condition for the second method, we show that networks generated by a stochastic block mechanism exhibiting a change in their structure can easily satisfy this condition under a multitude of scenar

关键词： stochastic block model Erőds-Rényi random graph change point edge probability matrix community detection estimation clustering algorithm convergence rate

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