The proceedings contain 31 papers. The topics discussed include: robust non-negative matrix factorization based on noise fuzzy clustering mechanism;combinatorial testing approach for cloud mobility service;categorical...
ISBN:
(纸本)9781450372633
The proceedings contain 31 papers. The topics discussed include: robust non-negative matrix factorization based on noise fuzzy clustering mechanism;combinatorial testing approach for cloud mobility service;categorical modeling method to analyze factors relating to longevity of populations in the east and Southeast asia;improving bees-based imputation using nearest neighbor for heuristic function in imputing data;a comparative study of unsupervised classification algorithms in multi-sized data sets;prediction of meningitis outbreaks in Nigeria using machinelearningalgorithms;forecasting dengue incidence with the Chi-squared automatic interaction detection technique;eye semantic segmentation using ensemble of deep convolutional neural networks;game genre classification from icon and screenshot images using convolutional neural networks;a deep learning model for extracting live streaming video highlights using audience messages;and image analysis of mushroom types classification by convolution neural networks.
In this research, we compared the accuracy of machinelearningalgorithms that could be used for predictive analytics in higher education. The proposed experiment is based on a combination of classic machinelearning ...
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We propose in this work the categorical modeling method based on machinelearning techniques to analyze environmental and economic factors anticipating to affect longevity patterns of people. The advantage of categori...
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We propose in this work the categorical modeling method based on machinelearning techniques to analyze environmental and economic factors anticipating to affect longevity patterns of people. The advantage of categori...
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ISBN:
(纸本)9781450372633
We propose in this work the categorical modeling method based on machinelearning techniques to analyze environmental and economic factors anticipating to affect longevity patterns of people. The advantage of categorical modeling from the original numeric data is that the derived models are concise and easy for interpretation. We apply various learningalgorithms during the modeling phase and it turns out that decision tree learning shows the best performance in classifying level of longevity according to the important factors. The classification accuracies on various countries range between 85 to 100%. The tree models also reveal prominent economic and environmental factors affecting longevity of populations living in the East and Southeast asia regions including Japan, South Korea, Singapore, Thailand, Malaysia, Indonesia, and Vietnam. Even though the derived models differ from one country to another, there exists one common environmental factor showing negative impact to longevity in every model. This factor is the particulate emission damage, which is the loss of life due to exposure to ozone pollution and concentrations of particulates less than 2.5 microns in diameter, or PM2.5. Electric power consumption at the moderate level shows positive impact toward long life in almost every country, except Japan. The two most important environment factors appear in the longevity pattern of Japanese population are particulate emission damage and forest depletion.
This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machinelearning approach. As employee turnover has become a vital issue these days du...
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With the development of computer technology, the applications of machinelearning are more and more extensive. Andmachinelearning is providing endless opportunities to develop new applications. One of those applicat...
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With the development of computer technology, the applications of machinelearning are more and more extensive. Andmachinelearning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks(CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machinelearning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.
As one of the popular evolutionary algorithms, differential evolution (DE) shows outstanding convergence rate on continuous optimization problems. But prematurity probably still occurs in classical DE when using relat...
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