This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such...This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such as blogs,forums,emails and online archives.The discussion highlights the role of AI in different stages of the research process,including question generation,sample and design definition,ethical considerations,data analysis,and results dissemination,emphasizing how AI can automate complex tasks and enhance research design.The paper also reports on practical experiences using AI tools,specifically ChatGPT-4,in conducting web-mediated documentary analysis and shares some ideas for the integration of AI in social research.展开更多
The advent of the artificial intelligence(AI)age offers substantial potentials for predicting regional gross domestic product(GDP)growth and transportation dynamics.This article presents an in-depth overview of the AI...The advent of the artificial intelligence(AI)age offers substantial potentials for predicting regional gross domestic product(GDP)growth and transportation dynamics.This article presents an in-depth overview of the AI and empirical modeling techniques used in this area,emphasizing the significant possibilities that AI presents and discussing potential obstacles.The use of AI is essential in managing complicated data,allowing for effective analysis of detailed regional economic trends.This capacity will be essential for making economic policies and plans that respond to each region’s specific needs and capabilities.This paper first explores the relationship and impact of different modes of transportation and regional economic growth.Subsequently,various empirical models and methodological frameworks,including the factors employed for studied economic analysis were comprehensively discussed and summarized.In the last part,the discussion focuses on the potential role of AI to revolutionize regional economic research using different AI approaches.This includes its capacity to handle vast and intricate databases,its ability to forecast future patterns using historical and current data,and its assistance in advanced decision making.The present study enhances our awareness of how AI is revolutionizing the field of regional economic growth study,shedding light on both its current application and future possibilities.This study contributes to the advancement of AI predictive models in decision making for predicting regional economic growth across the globe.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This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such as blogs,forums,emails and online archives.The discussion highlights the role of AI in different stages of the research process,including question generation,sample and design definition,ethical considerations,data analysis,and results dissemination,emphasizing how AI can automate complex tasks and enhance research design.The paper also reports on practical experiences using AI tools,specifically ChatGPT-4,in conducting web-mediated documentary analysis and shares some ideas for the integration of AI in social research.
基金supported by the National Key R&D Program of China(No.2023YFE0202400)the Fujian Province Highway Open Course Subject Funding Project(No.MGSKFKT202203)+1 种基金the Fundamental Research Funds for the Central UniversitiesTongji University Innovative Research Team Grant for Humanities and Social Sciences.
文摘The advent of the artificial intelligence(AI)age offers substantial potentials for predicting regional gross domestic product(GDP)growth and transportation dynamics.This article presents an in-depth overview of the AI and empirical modeling techniques used in this area,emphasizing the significant possibilities that AI presents and discussing potential obstacles.The use of AI is essential in managing complicated data,allowing for effective analysis of detailed regional economic trends.This capacity will be essential for making economic policies and plans that respond to each region’s specific needs and capabilities.This paper first explores the relationship and impact of different modes of transportation and regional economic growth.Subsequently,various empirical models and methodological frameworks,including the factors employed for studied economic analysis were comprehensively discussed and summarized.In the last part,the discussion focuses on the potential role of AI to revolutionize regional economic research using different AI approaches.This includes its capacity to handle vast and intricate databases,its ability to forecast future patterns using historical and current data,and its assistance in advanced decision making.The present study enhances our awareness of how AI is revolutionizing the field of regional economic growth study,shedding light on both its current application and future possibilities.This study contributes to the advancement of AI predictive models in decision making for predicting regional economic growth across the globe.
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.
