Check TextCheck URLsFull Website CheckerAPI Energetic task conjecture for electrocardiogram in mobile health technologies based on Naive Bayesian classifier ABSTRACT With the rapid development of mobile health technologies and applications in recent years

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Energetic task conjecture for electrocardiogram in mobile health technologies based on Naive Bayesian classifier

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ABSTRACT

With the rapid development of mobile health technologies and applications in recent years, large amounts of electrocardiogram (ECG) signals that need to be processed timely have been produced. Although the CPU-based sequential automated ECG analysis algorithm (CPU-AECG) designed for identifying seven types of heartbeats has been in use for years, it is single-threaded and handling lots of concurrent ECG signals still poses a severe challenge. Due to this challenge it was developed into a novel GPU-based automated ECG analysis algorithm (GPU-AECG) to effectively shorten the program executing time. A new concurrency based GPU-AECG, named cGPU-AECG, is also developed to handle multiple concurrent signals. Compared with the CPU-AECG, the cGPU-AECG achieves a 35 times speedup when handling 24-h-long ECG data. With cGPU-AECG, handle 24-h-ECG signals from thousands of users in a few seconds and provide prompt feedback, which not only greatly improves the user experience of mobile health services to propose the dynamic scheduling algorithm using machine learning techniques and naïve-Bayes classifier in cGPU-AECG environment. The proposed method is one of the energetic task forecast methods and load distribution at any moment is conducted according to the latest information from previous and current server status. The distinction of this method with previous studies is the use of data mining techniques (classification) in load distribution. Since this classification method has higher accuracy and speed compared with other methods, therefore this classifier helps us to achieve the optimal solution in less time.

INTRODUCTION

As the information technology spreads fast, most of the data were born digital as well as exchanged on internet today. According to the estimation of Lyman and Varian , the new data stored in digital media devices have already been more than 92 % in 2002, while the size of these new data was also more than five exabytes. In fact, the problems of analyzing the large scale data were not suddenly occurred but have been there for several years because the creation of data is usually much easier than finding useful things from the data. Even though computer systems today are much faster than those in the 1930s, the large scale data is a strain to analyze by the computers we have today. In response to the problems of analyzing large-scale data, quite a few efficient methods, such as sampling, data condensation, density-based approaches, grid-based approaches, divide and conquer, incremental learning, and distributed computing, have been presented. Of course, these methods are constantly used to improve the performance of the operators of data analytics process. The results of these methods may be able to analyses the system. Illustrate that with the efficient methods at hand, we (e.g., principal components analysis; PCA) is a typical example that is aimed at reducing the input data volume to accelerate the process of data analytics. Another reduction method that reduces the data computations of data clustering is sampling, which can also be used to speed up the computation time of data analytics. Although the advances of computer systems and internet technologies have witnessed the development of computing hardware following the Moore’s law for several decades, the problems of handling the large-scale data still exist when we are entering the age of big data. That is why Fisher et al. pointed out that big data means that the data is unable to be handled and processed by most current information systems or methods because data in the big data era will not only become too big to be loaded into a single machine, it also implies that most traditional data mining methods or data analytics developed for a centralized data analysis process may not be able to be applied directly to big data.

LITERATURE SURVEY

X. Fan, C. He, Y. Cai, and Y. Li, ”HCloud: A novel application-oriented cloud platform for preventive healthcare”In this paper based on the cloud computing which helps to the healthcare prevention.Nowadays, healthcare is the important application for cloud computing.so, the cloud was named ad Hcloud. Loosely coupling and powerful parallel computing to compute the details and prevent healthcare services.Huge physiological data storage was proposed here.

C. He, X. Fan, and Y. Li, ”Toward ubiquitous healthcare services with a novel efficient cloud platform.”Ubiquitous computing is nothing but mobile computing. It has ability to deal with multimodal, heterogeneous and non stationary

physiological data to provide persistent personalized services. Plugin algorithm used to get the semi structure (or) unstructured medical data are adapted by this cloud architecture. It shows the result robust, stable and efficient features satisfy the high concurrent request from ubiquitous healthcare services.

U. Varshney, ”Mobile health: Four emerging

themes of research.” Nowadays mobile application has more attention from the patients, healthcare professionals, application developers, network service providers etc., Mobile health was provided which gives advances in a) Expanding

healthcare coverage – patients related. b) Improving decision making – healthcare professional related. c) Managic chronic conditions

– IT related. d)Suitable healthcare in emergencies

– application related

B. Liu, J. Li, C. Chen, W. Tan, Q. Chen, and M. Zhou, ”Efficient motif discovery for large-scale time series in healthcare.” Time series was fastly grownup in large scale time series data, traditional methods for motif discovery method for large scale time series are not applicable. Mdlats was developed and
implemented
on a hadoop platform which deployed in a hospital for clinical electrocardiography classification. It gives an great improvement in the real world healthcare data which is also used in large time series.

H. Mamaghanian, N. Khaled, D.Atienza, and P.Vandergheynst,”Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes.” To monitor a cardiac patient continuously we can enable a remote system which have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, enhanced patient atonomy, mobility ;safety. We can use the WBSN for enabled ECG monitors to find the required functionality, miniaturization and energy efficiency. Through, embedded ECG compression the energy efficiency was improved in order to reduce airtime over energy hungry wireless links.

ARCHITECTURE DIAGRAM

Fig : Architecture Diagram

ECG is a non-invasive, convenient, and

reliable method of measuring the electric characteristics of the heart . It provides abundant information regarding the health state of the heart and has become one of the most important methods for diagnosing multiple health problems on an individual. The name node in the file system acts as a master server and the cluster contains many nodes and each node in that cluster will be a data node. The data storage of the system is maintained by these nodes.

ARCHITECTURE OF THE HEALTHCARE PLATFORM

In this section, the healthcare platform on which the cGPU-AECG runs is presented and then the process of cGPU-AECG is described in detail.

The wide availability of remote diagnosing and

monitoring allows patients to access general
health information from healthcare platforms at
anytime, without having to visit a medical

provider. Using new information technologies like cloud computing wearable devices or intelligent mobile phones, of an individual can be collected and then uploaded to a healthcare platform via the internet.

58

Fig : Healthcare Platform

The platform then schedules data analysis and gives feedback to users. The original healthcare platform hardware is organized into clusters by a number of ordinary PCs (personal computers) and servers with no prominent configurations. The con-current processing relies on the HTTP (HyperTextTransfer Protocol) node balancer.ECG processing tasks are resolved by a single task among different servers without real parallelized compute abilities, which can

IMPLEMENTATION MODULES

DATA PREPROCESS

The size of the data were splits and controlled by the Input Split method within the File Input Format class of the Map Reduce job. The number of splits is influenced by the HDFS block size, and one mapper job is created for each split data chunk. Map Reduce application mapper function will find the average value and associated with the key as place.

be called multi-in/single- out. The user experience is poor when long-term ECG tasks are performed, and when there is a large number of requests that are beyond the capabilities of node balancer scheduling. It is likely that adopting a new methodology to enhance the paralleling computing abilities will achieve multi-in/multi-out performance.ECG processing tasks are resolved by a single task among different servers without real parallelized compute abilities, which can be called multi-in/single- out. The user experience

EXISTING SYSTEM

Efficiently handling large amounts of data is a big challenge to current computational resources. Due to the characteristics of the healthcare service. A reduction in computational resources would lead to prolonged delay in result feedback and poor user experiences. The capability of handling many concurrent ECG signals in real time is critical for automated ECG analysis platforms.

PROPOSED SYSTEM

We propose naïve Bayes and GPU-based algorithm efficiently parallelizes the time-consuming parts of CPU-AECG and achieves excellent overall speedups. The proposed algorithm, on the basis of GPU-AECG, enabling concurrent analysis challenge of multiple long-term ECG signals. In this way, when concentrated users requests create surging computational burden for the healthcare platform, the CPU and GPU computing resources can be better utilized.

DRIVER OPERATION

The driver is what actually sets up the job, submits it, and waits for completion. This action was done by the map reduce algorithm. The driver is driven from a configuration file for ease of use for things like being able to specify the input/output directories. It can also accept Groovy script based mappers and reducers without recompilation.

MAPPER OPERATION

The actual mapping routine was a simple filter so that only variables that matched certain criteria would be sent to the reducer. The default Hadoop input file format reader opens the files and starts reading through the files for the pairs. Once it finds a pair, it reads both the key and all the values to pass that along to the

59

mapper. The mapper is just accepting data from the input file format reader.

REDUCE OPERATION

With the sequencing and mapping complete, the resulting pairs that matched the criteria to be analyzed were forwarded to the reducer. While the actual simple averaging operation was straightforward and relatively simple to set up, the reducer turned out to be more complex than expected.

MAP REDUCE PROCESS

A data node may do this again in turn, leading to a multi-level tree structure. The data node processes the smaller problem, and passes the answer back to a reducer node to perform the reduction operation. The map and reduce functions of Map-Reduce are both defined with respect to data structured in pairs.

BAYES CLASSIFER ALGORITHM

INPUT:Original ECG dataset size n

OUTPUT:Datasetofabnormaltypelist

CONCLUSION

The proposed weather prediction using big data environment. The method used in our project is Hadoop with map reduces to analyse the sensor data, which is stored in the National Climatic Data Centre (NCDC) is a efficient solution. Map reduce is frame work for highly parallel and distributed systems across huge dataset. It is used to analyse for the given data and predict required output to our project. By using map reduce with hadoop helps in removing scalability bottleneck. This type of technology used to analyse large data sets has potential to great enhancement to weather forecast. Hence we predict the future weather forecast, minimum and maximum temperature, hot days and cold days based on the data obtained from the NCDC.

SortedSet

probabilities =

newTreeSet(

new Comparator() {

FUTURE ENHANCEMENT

Further development will extend to the modeling of more detailed scenarios to facilitate prediction based on detailed input crime. Thus, the impact on the area for an upcoming holiday, where the weather is expected to be warm, could be evaluated. The aim here was to extract an underlying generalized model of crime incidents. However, specific locations best modeled independently of the other data on specific times of the year. Although there considerations can be modeled separately if sufficient amount of high quality data is backing it. As well as many statistical tools can study the exceptional events that will provide greater insight to how these events change the levels of crime and ultimately defining the rules that will modify incidence count significantly.

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In response to the problems of analyzing large-scale data, quite a few efficient methods, such
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Of course, these methods are constantly used to improve the performance of the operators of
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also be used to speed up the computation time of data analytics.
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Although the advances of computer systems and internet technologies have witnessed the development of computing
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hardware following the Moore’s law for several decades, the problems of handling the large-scale data
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still exist when we are entering the age of big data.
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will not only become too big to be loaded into a single machine, it also
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A novel application-oriented cloud platform for preventive healthcare”In this paper based on the cloud computing
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Nowadays, healthcare is the important application for cloud computing.
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so, the cloud was named ad Hcloud.
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Loosely coupling and powerful parallel computing to compute the details and prevent healthcare services.
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Huge physiological data storage was proposed here.
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Li, ”Toward ubiquitous healthcare services with a novel efficient cloud platform.
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”Ubiquitous computing is nothing but mobile computing.
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It has ability to deal with multimodal, heterogeneous and non stationary
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physiological data to provide persistent personalized services.
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Plugin algorithm used to get the semi structure (or) unstructured medical data are adapted by
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It shows the result robust, stable and efficient features satisfy the high concurrent request from
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” Nowadays mobile application has more attention from the patients, healthcare professionals, application developers, network
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Mobile health was provided which gives advances in a) Expanding
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b) Improving decision making – healthcare professional related.
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Zhou, ”Efficient motif discovery for large-scale time series in healthcare.
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” Time series was fastly grownup in large scale time series data, traditional methods for
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motif discovery method for large scale time series are not applicable.
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on a hadoop platform which deployed in a hospital for clinical electrocardiography classification.
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It gives an great improvement in the real world healthcare data which is also used
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Vandergheynst,”Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes.
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” To monitor a cardiac patient continuously we can enable a remote system which have
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the potential to achieve improved personalization and quality of care, increased ability of prevention and
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early diagnosis, enhanced patient atonomy, mobility &safety.
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We can use the WBSN for enabled ECG monitors to find the required functionality, miniaturization
Plagiarized
Through, embedded ECG compression the energy efficiency was improved in order to reduce airtime over
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reliable method of measuring the electric characteristics of the heart .
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It provides abundant information regarding the health state of the heart and has become one
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of the most important methods for diagnosing multiple health problems on an individual.
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The name node in the file system acts as a master server and the cluster
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contains many nodes and each node in that cluster will be a data node.
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The data storage of the system is maintained by these nodes.
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In this section, the healthcare platform on which the cGPU-AECG runs is presented and then
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the process of cGPU-AECG is described in detail.
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The wide availability of remote diagnosing and
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anytime, without having to visit a medical
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Using new information technologies like cloud computing wearable devices or intelligent mobile phones, of an
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individual can be collected and then uploaded to a healthcare platform via the internet.
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The platform then schedules data analysis and gives feedback to users.
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The original healthcare platform hardware is organized into clusters by a number of ordinary PCs
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(personal computers) and servers with no prominent configurations.
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The con-current processing relies on the HTTP (HyperTextTransfer Protocol) node balancer.
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ECG processing tasks are resolved by a single task among different servers without real parallelized
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The size of the data were splits and controlled by the Input Split method within
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the File Input Format class of the Map Reduce job.
Unique
The number of splits is influenced by the HDFS block size, and one mapper job
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is created for each split data chunk.
Unique
Map Reduce application mapper function will find the average value and associated with the key
Unique
The user experience is poor when long-term ECG tasks are performed, and when there is
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a large number of requests that are beyond the capabilities of node balancer scheduling.
Completely Unique
It is likely that adopting a new methodology to enhance the paralleling computing abilities will
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ECG processing tasks are resolved by a single task among different servers without real parallelized
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compute abilities, which can be called multi-in/single- out.
Unique
Efficiently handling large amounts of data is a big challenge to current computational resources.
Unique
Due to the characteristics of the healthcare service.
Unique
A reduction in computational resources would lead to prolonged delay in result feedback and poor
Unique
The capability of handling many concurrent ECG signals in real time is critical for automated
Unique
We propose naïve Bayes and GPU-based algorithm efficiently parallelizes the time-consuming parts of CPU-AECG and
Unique
The proposed algorithm, on the basis of GPU-AECG, enabling concurrent analysis challenge of multiple long-term
Unique
In this way, when concentrated users requests create surging computational burden for the healthcare platform,
Unique
the CPU and GPU computing resources can be better utilized.
Unique
The driver is what actually sets up the job, submits it, and waits for completion.
Unique
This action was done by the map reduce algorithm.
Unique
The driver is driven from a configuration file for ease of use for things like
Unique
being able to specify the input/output directories.
Unique
It can also accept Groovy script based mappers and reducers without recompilation.
Unique
The actual mapping routine was a simple filter so that only variables that matched certain
Unique
criteria would be sent to the reducer.
Unique
The default Hadoop input file format reader opens the files and starts reading through the
Unique
Once it finds a pair, it reads both the key and all the values to
Unique
The mapper is just accepting data from the input file format reader.
Unique
With the sequencing and mapping complete, the resulting pairs that matched the criteria to be
Unique
While the actual simple averaging operation was straightforward and relatively simple to set up, the
Unique
reducer turned out to be more complex than expected.
Unique
A data node may do this again in turn, leading to a multi-level tree structure.
Unique
The data node processes the smaller problem, and passes the answer back to a reducer
Unique
The map and reduce functions of Map-Reduce are both defined with respect to data structured
Unique
The proposed weather prediction using big data environment.
Unique
The method used in our project is Hadoop with map reduces to analyse the sensor
Unique
data, which is stored in the National Climatic Data Centre (NCDC) is a efficient solution.
Unique
Map reduce is frame work for highly parallel and distributed systems across huge dataset.
Unique
It is used to analyse for the given data and predict required output to our
Unique
By using map reduce with hadoop helps in removing scalability bottleneck.
Unique
This type of technology used to analyse large data sets has potential to great enhancement
Unique
Hence we predict the future weather forecast, minimum and maximum temperature, hot days and cold
Unique
days based on the data obtained from the NCDC.
Unique
Further development will extend to the modeling of more detailed scenarios to facilitate prediction based
Unique
Thus, the impact on the area for an upcoming holiday, where the weather is expected
Unique
The aim here was to extract an underlying generalized model of crime incidents.
Completely Unique
However, specific locations best modeled independently of the other data on specific times of the
Unique
Although there considerations can be modeled separately if sufficient amount of high quality data is

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