Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Mamun, Khondaker A.; Alhussein, Musaed; Sailunaz, Kashfia; Islam, Mohammad Saiful

doi:10.1016/j.future.2015.11.010

Cited by 69 publications

(31 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The big data generated by sensors are processed using MapReduce infrastructure and detect the abnormality in the data in real time. Mamun et al (2017) proposed a framework for detecting and monitoring Parkison's patients using cloud computing. Such patients can be monitored remotely by doctors by diagnosing their voice signals over the cloud.…”

Section: Integration Of Iot and Cloud Computing In Healthcare Servicesmentioning

confidence: 99%

IoT-based cloud framework to control Ebola virus outbreak

Sareen

Sood

Gupta

2016

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Ebola is a deadly infectious virus that spreads very quickly through human-to-human transmission and sometimes death. The continuous detection and remote monitoring of infected patients are required in order to prevent the spread of Ebola virus disease (EVD). Healthcare services based on Internet of Things (IoT) and cloud computing technologies are emerging as a more effective and proactive solution which provides remote continuous monitoring of patients. A novel architecture based on Radio Frequency Identification Device (RFID), wearable sensor technology, and cloud computing infrastructure is proposed for the detection and monitoring of Ebola infected patients. The aim of this work is to prevent the spreading of the infection at the early stage of the outbreak. The J48 decision tree is used to evaluate the level of infection in a user depending on his symptoms. RFID is used to automatically sense the close proximity interactions (CPIs) between users. Temporal Network Analysis (TNA) is applied to describe and monitor the current state of the outbreak using the CPI data. The performance and accuracy of our proposed model are evaluated on Amazon EC2 cloud using synthetic data of two million users. Our proposed model provided 94 % accuracy for the classification and 92 % of the resource utilization.

show abstract

Section: Integration Of Iot and Cloud Computing In Healthcare Servicesmentioning

confidence: 99%

IoT-based cloud framework to control Ebola virus outbreak

Sareen

Sood

Gupta

2016

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…F o l i n o and P i s a n i [18] S i n g h, J u n e j a and M a l h o t r a [19] tackled the problem of load balancing in computing environments. M a m u n et al [23] improved the speech-signal processing and recognition system to detect the Parkinson's disease in cloud-based framework. A r a n g o a et al [24] found a solution to the decision-making system which is based on cloud environment in Water distribution systems.…”

Section: O G H a D D A M R M O G H A D D A M And C H E R I E T [22]mentioning

confidence: 99%

“… -A l k h a n a k et al [25]  -W a n g et al [38]  -B i t t e n c o u r t and M a d e i r a [31]  -W u et al [10]  -H a n et al [4]  -A. K o n a k, S. K o n a k and S n y d e r [5]  -B a r a t i and S h a r i f i a n [7] Cloud load prediction K a r a m a g i and Z a p a t a [9]  -B a m a k a n et al  -F a n g et al [39]  -Huu et al [34]  -K o e h l e r [14]  -J i a n g et al [16]  -F o l i n o and P i s a n i [18]  -S i n g h, J u n e j a and M a l h o t r a [19]  -M a m u n et al [23] Speech-signal processing A r a n g o a et al [24] Decision-making system…”

Section: O G H a D D A M R M O G H A D D A M And C H E R I E T [22]mentioning

confidence: 99%

See 1 more Smart Citation

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

Naidu

Bhagat²

2017

Cybernetics and Information Technologies

View full text Add to dashboard Cite

show abstract

“…If users need a multi-source remote sensing data product or another specific product, then they need to search for and download related data and use their own workstation. However, if the user's computing capability and/or knowledge on remote sensing (as well as their computer skills) are limited, then it would be challenging for such user to obtain the remote sensing products they need [5,6]. Hence, this motivates the need for a well-designed platform with new data processing system architectures and service patterns to provide products and services for both skilled and unskilled researchers.…”

Section: Introductionmentioning

confidence: 99%

A cloud-based remote sensing data production system

Yan

Wang

et al. 2018

Future Generation Computer Systems

View full text Add to dashboard Cite

The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts "master-slave" architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a "minimum data transferring" strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, $ Fully documented templates are available in the elsarticle package on CTAN.

show abstract

Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Cited by 69 publications

References 27 publications

IoT-based cloud framework to control Ebola virus outbreak

IoT-based cloud framework to control Ebola virus outbreak

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

A cloud-based remote sensing data production system

Contact Info

Product

Resources

About