Alzheimer's disease(AD)is a prevalent neurodegenerative disease characterized by cognitive decline in the early stage.Mild cognitive impairment(MCI)is considered as an intermediate stage between normal aging and A...Alzheimer's disease(AD)is a prevalent neurodegenerative disease characterized by cognitive decline in the early stage.Mild cognitive impairment(MCI)is considered as an intermediate stage between normal aging and AD.In recent years,studies related to resting-state functional MRI(rs-fMRI)indicated that the occurrence and development process of MCI and AD might be closely linked to spontaneous brain activity and alterations in functional connectivity among brain regions,and rs-fMRI could provide important reference for specific diagnosis and early treatment of MCI and AD.The research progresses of rs-fMRI for MCI and AD were reviewed in this article.展开更多
Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable ...Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.展开更多
文摘Alzheimer's disease(AD)is a prevalent neurodegenerative disease characterized by cognitive decline in the early stage.Mild cognitive impairment(MCI)is considered as an intermediate stage between normal aging and AD.In recent years,studies related to resting-state functional MRI(rs-fMRI)indicated that the occurrence and development process of MCI and AD might be closely linked to spontaneous brain activity and alterations in functional connectivity among brain regions,and rs-fMRI could provide important reference for specific diagnosis and early treatment of MCI and AD.The research progresses of rs-fMRI for MCI and AD were reviewed in this article.
文摘Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.
