In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of he...Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human life.There are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and telemedicine.These applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service management.The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users.The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services.There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model.The proposed ontological model is derived from different classes,relationships,and constraints to integrate health care services.This model is developed using Protégébased on each interrelated/correlated health care service having different values.Semantic querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge acquisition.The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model.Comparative to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.展开更多
IOT has carried outimportant function in converting the traditional fitness care corporation.With developing call for in population,traditional healthcare structures have reached their outmost functionality in present...IOT has carried outimportant function in converting the traditional fitness care corporation.With developing call for in population,traditional healthcare structures have reached their outmost functionality in presenting sufficient and as plenty as mark offerings.The worldwide is handling devastating developingantique population disaster and the right want for assisted-dwelling environments is turning into inevitable for senior citizens.There furthermore a determination by means of the use of way of countrywide healthcare organizations to increase crucial manual for individualized,right blanketed care to prevent and manipulate excessive coronial situations.Many tech orientated packages related to HealthMonitoring have been delivered these days as taking advantage of net boom everywhere on globe,manner to improvements in cellular and in IOT generation.Such as optimized indoor networks insurance,community shape,and fairly-lowdevice fee performances,advanced tool reliability,low device energy consumption,and hundreds higher unusual common usual performance in network safety and privacy.Studies have highlighted fantastic advantages of integrating IOT with health care location and as era is improving the rate also cannot be that terrific of a problem.However,many challenges in this new paradigm shift notwithstanding the fact that exist,that need to be addressed.So the out most purpose of this research paper is 3 essential departments:First,evaluation of key elements that drove the adoption and boom of the Internet of factors based totally domestic some distance off monitoring;Second,present fashionable improvement of IOT in home a long manner off monitoring shape and key building gadgets;Third,communicate future very last effects and distinct guidelines of such type a long way off monitoring packages going ahead.Such Research is a wonderful manner in advance now not outstanding in IOT Terminology but in standard fitness care location.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
基金the Deanship of Scientific Research(DSR),King Abdul-Aziz University,Jeddah,Saudi Arabia under Grant No.(D-504-611-1443).
文摘Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human life.There are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and telemedicine.These applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service management.The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users.The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services.There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model.The proposed ontological model is derived from different classes,relationships,and constraints to integrate health care services.This model is developed using Protégébased on each interrelated/correlated health care service having different values.Semantic querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge acquisition.The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model.Comparative to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.
文摘IOT has carried outimportant function in converting the traditional fitness care corporation.With developing call for in population,traditional healthcare structures have reached their outmost functionality in presenting sufficient and as plenty as mark offerings.The worldwide is handling devastating developingantique population disaster and the right want for assisted-dwelling environments is turning into inevitable for senior citizens.There furthermore a determination by means of the use of way of countrywide healthcare organizations to increase crucial manual for individualized,right blanketed care to prevent and manipulate excessive coronial situations.Many tech orientated packages related to HealthMonitoring have been delivered these days as taking advantage of net boom everywhere on globe,manner to improvements in cellular and in IOT generation.Such as optimized indoor networks insurance,community shape,and fairly-lowdevice fee performances,advanced tool reliability,low device energy consumption,and hundreds higher unusual common usual performance in network safety and privacy.Studies have highlighted fantastic advantages of integrating IOT with health care location and as era is improving the rate also cannot be that terrific of a problem.However,many challenges in this new paradigm shift notwithstanding the fact that exist,that need to be addressed.So the out most purpose of this research paper is 3 essential departments:First,evaluation of key elements that drove the adoption and boom of the Internet of factors based totally domestic some distance off monitoring;Second,present fashionable improvement of IOT in home a long manner off monitoring shape and key building gadgets;Third,communicate future very last effects and distinct guidelines of such type a long way off monitoring packages going ahead.Such Research is a wonderful manner in advance now not outstanding in IOT Terminology but in standard fitness care location.
