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.

Support Vector Machine Classification Algorithm and Its Application

Information computing and applications: First International Conference, ICICA 2010, Tangshan, China, October 15-18, 2010 : proceedings

A deep learning approach based on convolutional LSTM for detecting diabetes.

https://re.public.polimi.it/bitstream/11311/1119189/2/1-s2.0-S0169260719308107-main%20integrated%20machine%20learning.pdf

Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring

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.

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An Optimization-Based Diabetes Prediction Model Using CNN and Bi-Directional LSTM in Real-Time Environment

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.

Application of Least Squares Support Vector Machine in Fault Diagnosis

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.

Application of Atmosphere-Environment Quality Assessment Based on Fuzzy Comprehensive Evaluation

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.

Prediction Based on Wavelet Transform and Support Vector Machine

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.

Yongli Zhang

Papers

Support Vector Machine Classification Algorithm and Its Application

Application of Least Squares Support Vector Machine in Fault Diagnosis

Application of Atmosphere-Environment Quality Assessment Based on Fuzzy Comprehensive Evaluation

Prediction Based on Wavelet Transform and Support Vector Machine

Application of Fuzzy Mathematics in Real Estate Valuation