Titlebook: Data Mining and Big Data; 5th International Co Ying Tan,Yuhui Shi,Milan Tuba Conference proceedings 2020 Springer Nature Singapore Pte Ltd.

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Pareto Optimization in Oil Refinery,ecomposed and viewed as a hierarchy of embedded orgraphs. Performance indicators and controlling factors lists are created based on the orgraphs and technical specifications of an object, thus allowing to systematize sources of influence. Using statistical data archives to train, the neural network

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Hypothesis Verification for Designing Flyback Booster by Analysis of Variance Visualized on Trianguvolutionary computation. We prepared two datasets of nondominated solutions due to two problem definitions, which differ merely in the definition of some design variables based on a design hypothesis gained from evolutionary-computation results. This study aims at verifying the hypothesis by applyin

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Application of Decision Tree Algorithm Based on Clustering and Entropy Method Level Division for Reto find the main factors affecting the regional economy with the support of big data and empirical evidence. First extract some important indicators that affect the regional economy, and use the entropy method to find the relative weights and scores of these indicators. Then use K-means to divide th

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An Evaluation Algorithm of the Importance of Network Node Based on Community Influence,ation dissemination. There are few methods to study the influence of communities on social networks among the existing node importance evaluation algorithms, and it is difficult to find nodes that promote information dissemination among communities. In view of this reason, this paper proposes a node

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PCA Based Kernel Initialization for Convolutional Neural Networks,ected layers. In this paper, we proposed a convolution kernel initialization method based on the two-dimensional principal component analysis (2DPCA), in which a parametric equalization normalization method is used to adjust the scale between each neuron weight. After that the weight initial value c

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Analysis of Breast Cancer Detection Using Different Machine Learning Techniques,accuracy and enhances the performance of three different classifiers: Decision Tree (J48), Naïve Bayes (NB), and Sequential Minimal Optimization (SMO). We also validate and compare the classifiers on two benchmark datasets: Wisconsin Breast Cancer (WBC) and Breast Cancer dataset. Data with imbalance

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Hypothesis Verification for Designing Flyback Booster by Analysis of Variance Visualized on Trianguiscontinuous surface naturally evaded in the hypersonic range because of surface temperature upsurge is capable of enhancing the lift-to-drag ratio in the low-speed range; the hypothesis grew into a new design problem.