1,083 Hits in 3.8 sec

Big Data Spark Solution for Functional Magnetic Resonance Imaging [article]

Saman Sarraf, Mehdi Ostadhashem
2016 arXiv   pre-print
In this paper, we designed, developed and successfully tested a new pipeline for medical imaging data (especially functional magnetic resonance imaging - fMRI) using Big Data Spark / PySpark platform on  ...  Healthcare is also one the industries willing to use big data platforms so that some big data analytics tools have been adopted in this field to some extent.  ...  Cristina Saverino Post-doctoral fellowship at Toronto Rehabilitation Institute-University Health Network for extending their help and support in this study.  ... 
arXiv:1603.07064v1 fatcat:pt2wcx4w3jce7c7bk65xbimav4

Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics

Milad Makkie, Xiang Li, Tianming Liu, Shannon Quinn, Binbin Lin, Jieping Ye
2016 2016 IEEE International Conference on Big Data (Big Data)  
analysis and visualization of such fMRI big data are extremely limited and largely under-discussed.  ...  of fMRI data.  ...  This by itself shows that we have achieved a method to meet the challenge posed by fMRI big data for more efficient and scalable data analytics method.  ... 
doi:10.1109/bigdata.2016.7841000 dblp:conf/bigdataconf/MakkieLLQLY16 fatcat:zk7qohvpi5ftjakakmfxtj7u2q

HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI)

Milad Makkie, Shijie Zhao, Xi Jiang, Jinglei Lv, Yu Zhao, Bao Ge, Xiang Li, Junwei Han, Tianming Liu
2015 Brain Informatics  
Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis  ...  big data.'  ...  Acknowledgments We thank all investigators contributing data to the 1000 Functional Connectomes project, without whom this analysis could not have been performed. T.  ... 
doi:10.1007/s40708-015-0024-0 pmid:27747565 pmcid:PMC4737667 fatcat:q6bx2x34ifhfvcrpi5tc62jy6q

2019 Index IEEE Transactions on Big Data Vol. 5

2020 IEEE Transactions on Big Data  
., +, TBData June 2019 109-119 Brain A Distributed Computing Platform for fMRI Big Data Analytics.  ...  ., +, TBData Sept. 2019 355-367 Diseases A Distributed Computing Platform for fMRI Big Data Analytics.  ...  T Task analysis A  ... 
doi:10.1109/tbdata.2020.2975953 fatcat:aeai72ddszachltlggv3u5dpru

SWADESH: A Comprehensive Platform for Multimodal Data and Analytics for Advanced Research in Alzheimer's Disease and Other Brain Disorders

Pravat K. Mandal, George Perry, Paula Moreira
2021 Journal of Alzheimer's Disease  
Framework for SWADESH: a comprehensive platform for multimodal neuroimaging data, quality control, and data analytics.  ...  It is a web-based platform that provides a framework for storing and processing data.  ... 
doi:10.3233/jad-215354 pmid:34744083 fatcat:jmrp6lfv7fgexiijuibbemftl4

Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics [article]

Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu
2018 arXiv   pre-print
of hierarchical neuroscientific information from massive fMRI big data in the future.  ...  for distributed deep Convolutional Autoencoder model.  ...  autoencoder model and apply it for fMRI big data analysis.  ... 
arXiv:1710.08961v3 fatcat:saxu4iobqrakdg6borq3lygjcu

A Brief Review on scheduling algorithms of MapReduce Optimization Techniques

R Lavanya, Jeevanshu Malhotra, Rajeshwari Swaminathan
2019 Journal of Physics, Conference Series  
Scheduling algorithms of MapReduce model using hadoop vary with design and behaviour, and are used for handling many issues like data locality, awareness with resource, energy and time.  ...  Scheduling has been an active area of research in computing systems since their inception.  ...  A Distributed Computing Platform for fMRI Big Data Analytics Interactions within Human brain are very complex and to understand behaviour of brain and analyze underlying data poses a challenge to neuroscience  ... 
doi:10.1088/1742-6596/1362/1/012001 fatcat:ikw7wxziybbm7e2eph2xkgtif4

Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics

Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu
2019 Neurocomputing  
autoencoder model and apply it for fMRI big data analysis.  ...  This model aims to both learn the complex hierarchical structures of the tfMRI big data and to leverage the processing power of multiple GPUs in a distributed fashion.  ...  and effective knowledge discovery from fMRI big data.  ... 
doi:10.1016/j.neucom.2018.09.066 pmid:31354187 pmcid:PMC6660166 fatcat:cssvsm4255gsfmcgqh3ica7nwa

Slides Nair Calyam NeuroCI Course Gateways 2018.pdf [article]

Prasad Calyam
2018 Figshare  
p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px Helvetica} Neuroscientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations  ...  We also discuss our vision towards a course sequence curriculum for graduate/undergraduate students from biological/psychological sciences and computer science/engineering to jointly build "selfservice  ...  optimization as suitable for diverse analytics from distributed data sources 1.  ... 
doi:10.6084/m9.figshare.7152536.v1 fatcat:u5dz2yn6zrhplbytlr2jhztugu

Open Neuroscience Solutions for the Connectome-wide Association Era

Michael Peter Milham
2012 Neuron  
(e.g., NiPype, Niak), or (4) provide novel analytic platforms for the connectome (e.g., The Brain Connectivity Toolbox, Connectome Toolkit, Connectir).  ...  The neuroimaging community is at a crossroads. Long characterized by individualism, the data and computational and analytic needs of the connectome-wide association era necessitate cultural reform.  ... 
doi:10.1016/j.neuron.2011.11.004 pmid:22284177 fatcat:4z44au4hafdqxew5hqthgnqkw4

BHARAT: An Integrated Big Data Analytic Model for Early Diagnostic Biomarker of Alzheimer's Disease

Ankita Sharma, Deepika Shukla, Tripti Goel, Pravat Kumar Mandal
2019 Frontiers in Neurology  
requirements of this big data framework for early AD diagnosis.  ...  This big data framework for AD incorporates the three "V"s (volume, variety, velocity) with advanced data mining, machine learning, and statistical modeling algorithms.  ...  data in a single platform.  ... 
doi:10.3389/fneur.2019.00009 pmid:30800093 pmcid:PMC6375828 fatcat:o65audtvbbh7xeu4ui2lqjwmmi

Granular computing with multiple granular layers for brain big data processing

Guoyin Wang, Ji Xu
2014 Brain Informatics  
Big data is the term for a collection of datasets so huge and complex that it becomes difficult to be processed using on-hand theoretical models and technique tools.  ...  intelligent processing of brain big data.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s)  ... 
doi:10.1007/s40708-014-0001-z pmid:27747523 pmcid:PMC4883151 fatcat:zl2e3f7hm5a2bknzkbmmlrcet4

Big data in healthcare: management, analysis and future prospects

Sabyasachi Dash, Sushil Kumar Shakyawar, Mohit Sharma, Sandeep Kaushik
2019 Journal of Big Data  
There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis.  ...  In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things.  ...  Other big companies such as Oracle Corporation and Google Inc. are also focusing to develop cloud-based storage and distributed computing power platforms.  ... 
doi:10.1186/s40537-019-0217-0 fatcat:6yb7kk5ervaqhjt6lhe3utkivq

Trends in big data analytics

Karthik Kambatla, Giorgos Kollias, Vipin Kumar, Ananth Grama
2014 Journal of Parallel and Distributed Computing  
parallel and distributed systems is in big-data analytics.  ...  Datasets are often distributed and their size and privacy considerations warrant distributed techniques. Data often resides on platforms with widely varying computational and network capabilities.  ...  Recent hardware advances have played a major role in realizing the distributed software platforms needed for big-data analytics.  ... 
doi:10.1016/j.jpdc.2014.01.003 fatcat:2v52tvp4tff7xh3cma5gdnlxgq

Building a generic platform for big sensor data application

Chun-Hsiang Lee, David Birch, Chao Wu, Dilshan Silva, Orestis Tsinalis, Yang Li, Shulin Yan, Moustafa Ghanem, Yike Guo
2013 2013 IEEE International Conference on Big Data  
The architecture of such a platform is a current research question in the field of Big Data and Smart Cities.  ...  In this paper we explore five key challenges in this field and provide a response through a sensor data platform "Concinnity" which can take sensor data from collection to final product via a data repository  ...  Sensor-generated big data is typically never under centralized control at a single location and is stored at distributed locations.  ... 
doi:10.1109/bigdata.2013.6691559 dblp:conf/bigdataconf/LeeBWSTLYGG13 fatcat:jofvnykv4ff5pbdrltgeh7m7m4
« Previous Showing results 1 — 15 out of 1,083 results