The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect da...The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect data from sensors.Automated decisions are made after evaluating the information of illness people records.Patients’health and well-being can be monitored through IoT medical devices.It is possible to trace the origins of biological,medical equipment and processes.Human reliability is a major concern in user activity and fitness trackers in day-to-day activities.The fundamental challenge is to measure the efficiency of the human system accurately.Aim to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper.The proposed framework implements several hospital information system case studies by building a time-saving simulation environment.As the world’s population ages,more and more people suffer from physical and emotional ailments.Using the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.展开更多
Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes.At the same time,clustering is one of the efficient te...Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes.At the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it.Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset.Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool.With this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data.In addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and classification.The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy data.For improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering.Finally,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big data.The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.展开更多
文摘The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect data from sensors.Automated decisions are made after evaluating the information of illness people records.Patients’health and well-being can be monitored through IoT medical devices.It is possible to trace the origins of biological,medical equipment and processes.Human reliability is a major concern in user activity and fitness trackers in day-to-day activities.The fundamental challenge is to measure the efficiency of the human system accurately.Aim to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper.The proposed framework implements several hospital information system case studies by building a time-saving simulation environment.As the world’s population ages,more and more people suffer from physical and emotional ailments.Using the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.
基金supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:HI21C1831)the Soonchunhyang University Research Fund.
文摘Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes.At the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it.Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset.Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool.With this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data.In addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and classification.The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy data.For improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering.Finally,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big data.The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.
