Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

Kim, Joongheon; Lee, Wonjun

doi:10.1109/tsmc.2015.2415463

Cited by 29 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially, multiple UAV devices are able to gather extremely large-scale surveillance and cellular network big-data [39][40][41]. For surveillance, multiple UAV devices can be utilized for monitoring extreme harsh areas and then for gathering security big-data from extreme areas such as dense forests and seaside coasts where network infrastructure cannot be established.…”

Section: Applications In Big-data Processing Platformsmentioning

confidence: 99%

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.

show abstract

Section: Applications In Big-data Processing Platformsmentioning

confidence: 99%

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…To enable visual big-data information processing in centralized storage for surveillance applications, the first step is collecting corresponding massive visual data. With the concept of crowdsourcing [7], the visual and image data can be gathered from surveillance camera devices (SCDs) via densely deployed CAPs to cloud storage as illustrated in Fig 1 [24–26]. …”

Section: Preliminary Knowledgementioning

confidence: 99%

“…The 60 GHz path-loss model in the IEEE 802.11ad standards for line-of-sight (LoS) scenarios is as follows [7]: where d denotes a distance a CAP and a SCD (in a meter scale). This paper assumes that there is no blockage between cloud access points and surveillance devices, therefore only a 60 GHz LoS path-loss model is considered.…”

Section: Preliminary Knowledgementioning

confidence: 99%

Achievable Rate Estimation of IEEE 802.11ad Visual Big-Data Uplink Access in Cloud-Enabled Surveillance Applications

Kim

2016

PLoS ONE

Self Cite

View full text Add to dashboard Cite

This paper addresses the computation procedures for estimating the impact of interference in 60 GHz IEEE 802.11ad uplink access in order to construct visual big-data database from randomly deployed surveillance camera sensing devices. The acquired large-scale massive visual information from surveillance camera devices will be used for organizing big-data database, i.e., this estimation is essential for constructing centralized cloud-enabled surveillance database. This performance estimation study captures interference impacts on the target cloud access points from multiple interference components generated by the 60 GHz wireless transmissions from nearby surveillance camera devices to their associated cloud access points. With this uplink interference scenario, the interference impacts on the main wireless transmission from a target surveillance camera device to its associated target cloud access point with a number of settings are measured and estimated under the consideration of 60 GHz radiation characteristics and antenna radiation pattern models.

show abstract

“…Apple Research Kit [17] permits users to access interactive apps to employ deep learning algorithms to perform face recognition and gain insights into Parkinson and Asperger's diseases; IBM has in partnership with Medtronic consolidated and made available large amounts of data on diabetes and insulin [18]. Hence with the prevalence of high-performance computing, networking infrastructure, novel ways for users to participate and contribute online and the proliferation of electronic devices capable of easily transmitting medical data, crowdsourcing has a lot of potential in helping solve many of the critical problems in medical science [4,5,19].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Classification of Medical Images Using Deep Learning to Aid in Adaptive Big Data Crowdsourcing Platforms

Sudha

Sneha

2021

ICT With Intelligent Applications

View full text Add to dashboard Cite

Deep Learning has radically transformed the field of Medical Image Analysis by surpassing human performance in several challenging classification tasks and thus has widespread applications in industry and academia. In this work, we employ a Deep Convolutional Neural Network (DCNN) that uses Transfer Learning and is trained using the Open-access Medical Image Repositories and OpenI repository. We use DCNN to assist in a crowdsourced Big Data platform by conducting two crucial classification tasks: (1) Interclass classification of the type of the medical image-MRI, Ultrasound or X-Ray; (2) Intraclass classification of the X-Ray images into chest or abdomen X-Rays. Our approach automates the classification process of medical images, to provide certified medical experts with medical images of individuals on a crowdsourced platform, along with similar medical images of the suspected medical condition for validation, in order to enhance diagnostic effectiveness. In this work, we focus primarily on the classification tasks using DCNN; the elucidation of the privacy-preserving architecture of our cloud-based crowdsourcing platform and classification using conventional clustering approaches in the absence of large training images is deferred to future work.

show abstract

Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

Cited by 29 publications

References 22 publications

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Achievable Rate Estimation of IEEE 802.11ad Visual Big-Data Uplink Access in Cloud-Enabled Surveillance Applications

Classification of Medical Images Using Deep Learning to Aid in Adaptive Big Data Crowdsourcing Platforms

Contact Info

Product

Resources

About