An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP alg...An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.展开更多
Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process mi...Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.展开更多
The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,i...The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,in tumor pathology.The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology,including tumor diagnosis,subtyping,grading,staging,and prognostic prediction,as well as the identification of pathological features,biomarkers and genetic changes.The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks.In addition,AI is useful for pathologists to meet the requirements of precision oncology.However,there are still some challenges relating to the implementation of AI,including the issues of algorithm validation and interpretability,computing systems,the unbelieving attitude of pathologists,clinicians and patients,as well as regulators and reimbursements.Herein,we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.展开更多
Cardiovascular diseases are a prominent cause of mortality,emphasizing the need for early prevention and diagnosis.Utilizing artificial intelligence(AI)models,heart sound analysis emerges as a noninvasive and universa...Cardiovascular diseases are a prominent cause of mortality,emphasizing the need for early prevention and diagnosis.Utilizing artificial intelligence(AI)models,heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions.However,real-world medical data are dispersed across medical institutions,forming“data islands”due to data sharing limitations for security reasons.To this end,federated learning(FL)has been extensively employed in the medical field,which can effectively model across multiple institutions.Additionally,conventional supervised classification methods require fully labeled data classes,e.g.,binary classification requires labeling of positive and negative samples.Nevertheless,the process of labeling healthcare data is timeconsuming and labor-intensive,leading to the possibility of mislabeling negative samples.In this study,we validate an FL framework with a naive positive-unlabeled(PU)learning strategy.Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples.Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces.Additionally,our contribution extends to feature importance analysis,where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds.The study demonstrated an impressive accuracy of 84%,comparable to outcomes in supervised learning,thereby advancing the application of FL in abnormal heart sound detection.展开更多
文摘An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.
基金supported by a grant (12-TI-C04) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government
文摘Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.
基金National Nature Science Foundation of China,Grant/Award Numbers:81871990,81472263。
文摘The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,in tumor pathology.The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology,including tumor diagnosis,subtyping,grading,staging,and prognostic prediction,as well as the identification of pathological features,biomarkers and genetic changes.The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks.In addition,AI is useful for pathologists to meet the requirements of precision oncology.However,there are still some challenges relating to the implementation of AI,including the issues of algorithm validation and interpretability,computing systems,the unbelieving attitude of pathologists,clinicians and patients,as well as regulators and reimbursements.Herein,we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.
基金partially supported by the National Natural Science Foundation of China(grant number 62272044)the Ministry of Science and Technology of the People’s Republic of China with the STI2030-Major Projects(grant number 2021ZD0201900)+5 种基金the Teli Young Fellow Program from the Beijing Institute of Technology,Chinathe Grants-in-Aid for Scientific Research(grant number 20H00569)from the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japanthe JSPS KAKENHI(grant number 20H00569),Japanthe JST Mirai Program(grant number 21473074),Japanthe JST MOONSHOT Program(grant number JPMJMS229B),Japanthe BIT Research and Innovation Promoting Project(grant number 2023YCXZ014).
文摘Cardiovascular diseases are a prominent cause of mortality,emphasizing the need for early prevention and diagnosis.Utilizing artificial intelligence(AI)models,heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions.However,real-world medical data are dispersed across medical institutions,forming“data islands”due to data sharing limitations for security reasons.To this end,federated learning(FL)has been extensively employed in the medical field,which can effectively model across multiple institutions.Additionally,conventional supervised classification methods require fully labeled data classes,e.g.,binary classification requires labeling of positive and negative samples.Nevertheless,the process of labeling healthcare data is timeconsuming and labor-intensive,leading to the possibility of mislabeling negative samples.In this study,we validate an FL framework with a naive positive-unlabeled(PU)learning strategy.Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples.Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces.Additionally,our contribution extends to feature importance analysis,where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds.The study demonstrated an impressive accuracy of 84%,comparable to outcomes in supervised learning,thereby advancing the application of FL in abnormal heart sound detection.
