The brain avatar of schizophrenic patients is different from the normal human brain avatar, and it is difficult to overcome the complex environmental effects of the brain through traditional magnetic resonance imaging...
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The brain avatar of schizophrenic patients is different from the normal human brain avatar, and it is difficult to overcome the complex environmental effects of the brain through traditional magnetic resonance imaging (MRI). In order to improve the accuracy of MRI in detecting brain information in patients with schizophrenia, this study is based on the support vector machine classification algorithm and combined with multimodal MRI detection method to construct a detection model suitable for patients with schizophrenia. In addition, this study combines the existing test cases to divide the brain into regions and design a comparative experiment to study the accuracy of the model proposed in this study. Finally, the study draws the results by sub-regional comparison. Studies have shown that the algorithm model of this study has certain effects on brain detection in patients with schizophrenia, and can be applied to practice, and can provide theoretical reference for subsequent related research.
As a Chinese version of twitter, micro-blog has been popular for many years. On this platform, a lot of comments are generated explosively every day. These comments contain the public's opinions on various topics,...
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As a Chinese version of twitter, micro-blog has been popular for many years. On this platform, a lot of comments are generated explosively every day. These comments contain the public's opinions on various topics, which have wide applications in both academic and industrial fields. In recent years, deep learning and some classificationalgorithms have been applied to sentiment analysis, and good results have been achieved. However, micro-blog sentiment classification is a challenging task, because micro-blog messages are short and noisy, and contain massive user-invented acronyms and informal words. Unfortunately, most researchers pay more attention to analyse the data after deep learning, but only simply remove the noisy data before using algorithm, so the result of sentiment analysis has reached a bottleneck. Here, the authors first purify the data using varied methods before deep learning, then, the supportvectormachine (SVM) classificationalgorithm is applied to sentiment classification of micro-blog using many types of features. Through comparing with the method of simply preprocessing data, the results show that their approach can improve the performance of micro-blog sentiment classification effectively and efficiently.
The Deepwater Horizon blowout in the Gulf of Mexico resulted in one of the largest accidental oil disasters in U.S. history. NASA acquired radar and hyperspectral imagery and made them available to the scientific comm...
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