Titlebook: Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications; Nguyen Hoang Phuong,Nguyen Thi Huyen Chau,Vladi

culinary

书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications影响因子(影响力)




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications影响因子(影响力)学科排名




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications网络公开度




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications网络公开度学科排名




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications被引频次




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications被引频次学科排名




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications年度引用




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications年度引用学科排名




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications读者反馈




书目名称Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications读者反馈学科排名

丛林

边缘

Flatter

,Promoting STEM-Integrated Learning Through Engineering Design: High School Students’ Automatic Handg during manufacturing; product presentation and completion. In this article, we offer the technical design process of the automatic hand washing machine model. We experimented with two classes, including 70 students at An Khanh High School, Can Tho, Vietnam, and the performance evaluation of activi

弄污

A Classification System of Mammograms Based on Convolutional Neural Networks,an AUC of 0.89758. On the other hand, our proposed model achieves results which is higher than the results of a classification system applied to the model ResNet 50 for classifying breast cancer X-ray images into BI-RADS 045, BI-RADS 1, BI-RADS 23 categories which achieves a macAUC of 0.754, an aver

falsehood

OAGRE: Outlier Attenuated Gradient Boosted Regression,oach provides value in approx 30% of these synthetic datasets with an average improvement of between 7 and 12% lower root mean squared error. Based on these results we recommend the inclusion of the OAGRE method when testing algorithms on data with suspected heteroscedasticity issues.

谎言

吃掉

Data Processing and Feature Engineering for Stock Price Trend Prediction,s that improved prediction results by 9–20% over conventional feature generation methods. Our results are also of 2–11% better than those of some related studies on the same topic. Lastly, our data processing method achieved an accuracy of 75–80% in predicting results for the subsequent 6 months.

轻触

Book 2024ide effects. Because of this, we cannot apply untested methods to patients, and we first need to test these methods on other less critical applications. Several book chapters describe such applications—whose success paves the way for these methods to be used in biomedical situations. These applicati

intuition