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

culinary

書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications影響因子(影響力)




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications影響因子(影響力)學(xué)科排名




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications網(wǎng)絡(luò)公開度




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications被引頻次




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications被引頻次學(xué)科排名




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications年度引用




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications年度引用學(xué)科排名




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications讀者反饋




書目名稱Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications讀者反饋學(xué)科排名

叢林

邊緣

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.

謊言

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.

輕觸

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