Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

Mújica-Vargas, Dante

doi:10.22201/icat.24486736e.2021.19.2.1581

Cited by 45 publications

(9 citation statements)

References 19 publications

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“…When the continuous iteration u i′j is unchanged, i.e., u i′j is at the optimal state, it indicates that the clustering process has converged to the local minimum of J to obtain the final classification of enterprise management performance evaluation results [20].…”

Section: Enterprise Management Performance Evaluation Modelmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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Enterprise core competence is closely related to enterprise management performance, and it is important to evaluate enterprise management performance. However, the current enterprise management performance evaluation model has the problems of high eigenvalues of sample data, low cumulative contribution and correlation, high error rate in the calculation of business management performance evaluation index weights, low evaluation accuracy, and long evaluation time. Therefore, the enterprise management performance evaluation model using improved fuzzy clustering algorithm in Internet of things (IoT) networks is proposed. First, in the IoT architecture, the enterprise management performance evaluation index system is established by using the balanced scorecard theory. Second, the evaluation index system is reduced in dimensionality by combining principal component analysis and kernel-independent component analysis, the fuzzy C-mean clustering algorithm based on the objective function is designed, and finally, the improved fuzzy clustering algorithm is obtained to establish the enterprise management performance evaluation model, the reduced evaluation index system is input, and the evaluation results are output. The results show that the sample data eigenvalue of this model is low. The maximum error rate of weight calculation is 2.3%, the accuracy is always more than 95%, and the average value of evaluation time is 0.57 s, which effectively realize enterprise management performance evaluation in IoT networks.

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Section: Enterprise Management Performance Evaluation Modelmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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“…In the following, we suppose that the susceptible population size remains constant, which constitutes a hypothesis valid during the exponential phase of epidemic waves. The Markovian stochastic and ODE deterministic approaches are linked by a common background consisting of the birth and death process approach used in the kinetics of molecular reactions by Delbrück [17], then in dynamical systems theory by numerous authors [18][19][20][21][22][23], namely in modelling of the epidemic spread in exponential growth. In the ODE approach, the Malthusian parameter is the dominant eigenvalue, and the equivalent in the Markovian approach is the Kolmogorov-Sinai entropy (called evolutionary entropy in [24][25][26]).…”

Section: Relationships Between Markovian and Ode Sir Approachesmentioning

confidence: 99%

“…UK exponential phase from 17 October 2020 to 30 October 2020The numbers of new cases are: 30 October 24,350, 23,014,24,646,22,833,20,843,19,746,22,961,20,484,21,195,26,624,21,282,18,761,16,943,16,133 17 October …”

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confidence: 99%

Estimation of Daily Reproduction Numbers during the COVID-19 Outbreak

et al. 2021

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(1) Background: The estimation of daily reproduction numbers throughout the contagiousness period is rarely considered, and only their sum R0 is calculated to quantify the contagiousness level of an infectious disease. (2) Methods: We provide the equation of the discrete dynamics of the epidemic’s growth and obtain an estimation of the daily reproduction numbers by using a deconvolution technique on a series of new COVID-19 cases. (3) Results: We provide both simulation results and estimations for several countries and waves of the COVID-19 outbreak. (4) Discussion: We discuss the role of noise on the stability of the epidemic’s dynamics. (5) Conclusions: We consider the possibility of improving the estimation of the distribution of daily reproduction numbers during the contagiousness period by taking into account the heterogeneity due to several host age classes.

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“…CNN is a type of neural network that delivered a promising performance on many competitions of computer vision and captivated the attention of industry and academia over the last years being a feedforward neural network that automatically extracts features using convolution structures [41][42][43][44][45][46][47]. CNN is a hot topic in image recognition [40].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

et al. 2021

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Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

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Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

Cited by 45 publications

References 19 publications

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Estimation of Daily Reproduction Numbers during the COVID-19 Outbreak

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

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