Author
Yongli Zhang
Bio: Yongli Zhang is an academic researcher from North China University of Science and Technology. The author has contributed to research in topics: Support vector machine & Structured support vector machine. The author has an hindex of 3, co-authored 7 publications receiving 61 citations.
Papers
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14 Sep 2012
TL;DR: The basic theory ofsupport vector machine, the basic idea of the classification and currently used support vector machine classification algorithm, practical problems with which an algorithm is solved and proves the effectiveness of the algorithm, the final outlook of the prospects of support vector machines in classification applications.
Abstract: The support vector machine is a new type of machine learning methods based on statistical learning theory. Because of good promotion and a higher accuracy, support vector machine has become the research focus of the machine learning community. This paper introduces the basic theory of support vector machine, the basic idea of the classification and currently used support vector machine classification algorithm. Practical problems with which an algorithm, and proves the effectiveness of the algorithm, the final outlook of the prospects of support vector machines in classification applications. Finally the prospect of the prospect of support vector machines in classification applications.
162 citations
28 Oct 2011
TL;DR: Improve overall system performance of fault diagnosis and find a good method for fault diagnosis through experiments when learning samples is not enough, equipment failure does not reduce and the classification accuracy has increased even.
Abstract: In daily life fault diagnosis is widely used production. With the rapid development of science and technology, the new high-tech products emerged. It is not enough data of samples. Conventional approach is ineffective. It is need to find a good method. The least squares support vector machine algorithm and the proximal of support vector machine applied to fault diagnosis. Through experiments when learning samples is not enough, equipment failure does not reduce and the classification accuracy has increased even. On fault diagnosis the training speed has been to improve and the cost of building has been reduced. Improve overall system performance of fault diagnosis.
4 citations
28 Oct 2011
TL;DR: The fuzzy composite appraisal method is used in the atmosphere-environment quality assessment and can get the effects of all guided pollution factor upon the assessed area, which can provide reliable basis for the environmental planning and management.
Abstract: In this paper, the fuzzy composite appraisal method is used in the atmosphere-environment quality assessment. Take Da Tong as an example, synthesize the received fuzzy information and take the method of maximum subordination principle, make the objective and practical appraisal on the area of atmosphere-environment quality assessment. we can get the effects of all guided pollution factor upon the assessed area, which can provide reliable basis for the environmental planning and management.
3 citations
28 Oct 2011
TL;DR: The prediction precision of the new Wavelet-SVM model is higher than that of the SVM model and the artificial neural network model for many processes, such as runoff, precipitation, temperature.
Abstract: In this paper, a model combining the wavelet transform and support vector machine to predict the time series is set up. First, wavelet transform is applied to decompose the series into sub series with different time scales. Then, the SVM is applied to the sub series to simulate and predict future behavior. And then by the inverse wavelet transform, the series are reconstructed, which is the prediction for the time series. The prediction precision of the new model is higher than that of the SVM model and the artificial neural network model for many processes, such as runoff, precipitation, temperature. The universal applicability of the new Wavelet-SVM model and the improvement direction are discussed in this paper.
2 citations
28 Oct 2011
TL;DR: By applying fuzzy theory into real estate valuation, calculation example shows that the application of the method of real estate assessment can overcome the shortcomings of traditional methods, the valuation results are more scientific and rational and fair.
Abstract: Similarity degree and the principle of selection are analyzed in this paper. Fuzzy mathematics is used to Market comparison approach. By applying fuzzy theory into real estate valuation, calculation example shows that the application of the method of real estate assessment can overcome the shortcomings of traditional methods, the valuation results are more scientific and rational and fair.
1 citations
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14 Sep 2012
TL;DR: The basic theory ofsupport vector machine, the basic idea of the classification and currently used support vector machine classification algorithm, practical problems with which an algorithm is solved and proves the effectiveness of the algorithm, the final outlook of the prospects of support vector machines in classification applications.
Abstract: The support vector machine is a new type of machine learning methods based on statistical learning theory. Because of good promotion and a higher accuracy, support vector machine has become the research focus of the machine learning community. This paper introduces the basic theory of support vector machine, the basic idea of the classification and currently used support vector machine classification algorithm. Practical problems with which an algorithm, and proves the effectiveness of the algorithm, the final outlook of the prospects of support vector machines in classification applications. Finally the prospect of the prospect of support vector machines in classification applications.
162 citations
Book•
01 Jan 2010
77 citations
TL;DR: A novel diabetes classifying model based on Convolutional Long Short-term Memory (Conv-LSTM) that was not applied yet is developed and outperformed the other three models along with the state-of-the-art models.
48 citations
TL;DR: validation of an accurate artificially intelligence framework for the diagnosis of IUGR condition in the antepartum period is provided and the employed physiology based heart rate features constitute an interpretable link between the machine learning results and the quantitative estimators of fetal wellbeing.
43 citations
TL;DR: A real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database and it is demonstrated that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy, sensitivity, and specificity.
Abstract: Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals.
34 citations