检索结果-南通市图书馆

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

引用

Chinese Medical Journal 2021年第15期134卷 1828-1837页

作者： Tai-Hui Xia Man Tan Jing-Hua Li Jing-Jing Wang Qing-Qing Wu De-Xing Kong Department of Ultrasound Beijing Obstetrics and Gynecology HospitalCapital Medical UniversityBeijing 100026China The School of Mathematical Sciences Zhejiang UniversityHangzhouZhejiang 310058China

Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is *** study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing ***:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this *** were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory *** images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 *** were 3323,2142,and 1548 images in each class,***,we performed a pre-processing algorithm to remove irrelevant information from each ***,a convolutional neural network was designed to identify different categories of fetal lung ultrasound ***,we used ten-fold cross-validation to validate the performance of our *** new machine learning algorithm automatically extracted and classified lung ultrasound image information related to *** was used to establish a grading *** performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic ***:A normal fetal lung GA grading model was established and *** sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,*** specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,*** total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,*** micro-average AUC was 0.957,and the macro-average AUC was ***:The normal fetal lung GA grading model could accurately identify ultraso

关键词： Convolutional neural network deep learning algorithms Grading model Normal fetal lung Fetal lung maturity Gestational age Artificial intelligence

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Application of deep learning algorithms in the field of intelligent Chinese medicine

Application of deep learning algorithms in the field of inte...

引用

作者： Yaofeng Miao School of engineering Xi’an International University

The realization of intelligence in Chinese medicine is an inevitable result of technological development. Thousands of years of experience in the development of Chinese medicine in our country have left us precious spiritual wealth. How to achieve further development with the help of deep learning algorithms in the context of big data is a subject worthy of study. Based on the background of big data, this paper analyzes the application of deep learning algorithms in the field of intelligent Chinese medicine.

关键词： deep learning algorithms Traditional Chinese medicine Intelligence

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Prediction of bearing capacity of pile foundation using deep learning approaches

引用

Frontiers of Structural and Civil Engineering 2024年第6期18卷 870-886页

作者： Manish KUMAR Divesh Ranjan KUMAR Jitendra KHATTI Pijush SAMUI Kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

引用

Forest Ecosystems 2020年第2期7卷 141-158页

作者： ilker Ercanli Cankiri Karatekin University Faculty of Forestry18200ÇankırıTurkey

Background:deep learning algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since *** paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra *** ***(Lamb.)Holmboe)in Konya Forest *** present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth *** 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network ***:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect *** alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(***,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent ***:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diame

关键词： Artificial intelligence Prediction deep learning algorithms Individual tree height

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Artificial intelligence strengthens cervical cancer screening–present and future

引用

Cancer Biology & Medicine 2024年第10期21卷 864-879页

作者： Tong Wu Eric Lucas Fanghui Zhao Partha Basu Youlin Qiao School of Population Medicine and Public Health Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China Early Detection Prevention&Infections Branch International Agency for Research on Cancer(WHO)25 avenue Tony GarnierLyon 69007France Department of Cancer Epidemiology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China

Cervical cancer is a severe threat to women’s *** majority of cervical cancer cases occur in developing *** WHO has proposed screening 70%of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical *** to an inadequate health infrastructure and organized screening strategy,most low-and middle-income countries are still far from achieving this *** part of the efforts to increase performance of cervical cancer screening,it is necessary to investigate the most accurate,efficient,and effective methods and *** intelligence(AI)is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical *** will soon have a more significant role in improving the implementation of cervical cancer screening,management,and *** review aims to report the state of AI with respect to cervical cancer *** discuss the primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases,as well as the challenges that we anticipate in the future.

关键词： Cervical cancer screening artificial intelligence deep learning algorithms

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

人工理性批判：对德雷福斯的人工智能哲学的现象学反思

引用

科学技术哲学 2019年第4期

作者：张昌盛

随着深度学习算法的突破，人工神经网络范式的人工智能取得空前的成就。德雷福斯等人对人工智能的哲学反思，至今具有深刻意义。据此，以德雷福斯从现象学角度对人工智能极限问题的探讨入手，引入塞尔的生物自然主义的观念作为对比进行... 详细信息

随着深度学习算法的突破，人工神经网络范式的人工智能取得空前的成就。德雷福斯等人对人工智能的哲学反思，至今具有深刻意义。据此，以德雷福斯从现象学角度对人工智能极限问题的探讨入手，引入塞尔的生物自然主义的观念作为对比进行分析，再通过先验现象学的视角，反思德雷福斯的问题并回应塞尔的挑战，重新思考现象学如何反思人工智能的限度。

关键词：深度学习算法人工神经网络生物自然主义先验现象学 deep learning algorithms artificial neural network biological naturalism transcendental phenomenology

来源：人大复印报刊资料评论

在线全文

人大复印报刊资料

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：