scholarly journals Classification of large-scale fundus image data sets: A cloud-computing framework

2016 ◽  
Author(s):  
Sohini Roychowdhury
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Image Data ◽  
Data Sets ◽  
Fundus Image ◽  
Computing Framework

Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

2018 ◽  
Vol 15 (9) ◽  
pp. 1451-1455 ◽  
Author(s):  
Grant J. Scott ◽  
Kyle C. Hagan ◽  
Richard A. Marcum ◽  
James Alex Hurt ◽  
Derek T. Anderson ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Image Data ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image

scholarly journals New Approaches for Quantitative Reconstruction of Radiation Dose in Human Blood Cells

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shanaz A. Ghandhi ◽  
Igor Shuryak ◽  
Shad R. Morton ◽  
Sally A. Amundson ◽  
David J. Brenner
Keyword(s):  
Radiation Dose ◽  
Large Scale ◽  
Mean Squared Error ◽  
Data Sets ◽  
Reconstruction Method ◽  
Dose Reconstruction ◽  
Test Dataset ◽  
Human Blood Cells ◽  
Exposure Levels

AbstractIn the event of a nuclear attack or large-scale radiation event, there would be an urgent need for assessing the dose to which hundreds or thousands of individuals were exposed. Biodosimetry approaches are being developed to address this need, including transcriptomics. Studies have identified many genes with potential for biodosimetry, but, to date most have focused on classification of samples by exposure levels, rather than dose reconstruction. We report here a proof-of-principle study applying new methods to select radiation-responsive genes to generate quantitative, rather than categorical, radiation dose reconstructions based on a blood sample. We used a new normalization method to reduce effects of variability of signal intensity in unirradiated samples across studies; developed a quantitative dose-reconstruction method that is generally under-utilized compared to categorical methods; and combined these to determine a gene set as a reconstructor. Our dose-reconstruction biomarker was trained using two data sets and tested on two independent ones. It was able to reconstruct dose up to 4.5 Gy with root mean squared error (RMSE) of ± 0.35 Gy on a test dataset using the same platform, and up to 6.0 Gy with RMSE of ± 1.74 Gy on a test set using a different platform.

scholarly journals Hypervisor Vulnerabilities and Some Defense Mechanisms, in Cloud Computing Environment

2020 ◽  
Vol 10 (2) ◽  
pp. 42-48
Author(s):  
Dina Mohsen Zoughbi ◽  
Nitul Dutta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Large Scale ◽  
Virtual Machines ◽  
Virtual Technology ◽  
Cloud Computing Environment ◽  
Security Issues ◽  
Material Resources ◽  
Side Channels

Cloud computing is the most important technology at the present time, in terms of reducing applications costs and makes them more scalable and flexible. As the cloud currency is based on building virtualization technology, so it can secure a large-scale environment with limited security capacity such as the cloud. Where, Malicious activities lead the attackers to penetrate virtualization technologies that endanger the infrastructure, and then enabling attacker access to other virtual machines which running on the same vulnerable device. The proposed work in this paper is to review and discuss the attacks and intrusions that allow a malicious virtual machine (VM) to penetrate hypervisor, especially the technologies that malicious virtual machines work on, to steal more than their allocated quota from material resources, and the use of side channels to steal data and Passing buffer barriers between virtual machines. This paper is based on the Security Study of Cloud Hypervisors and classification of vulnerabilities, security issues, and possible solutions that virtual machines are exposed to. Therefore, we aim to provide researchers, academics, and industry with a better understanding of all attacks and defense mechanisms to protect cloud security. and work on building a new security architecture in a virtual technology based on hypervisor to protect and ensure the security of the cloud.

scholarly journals MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

2018 ◽  
Author(s):  
Li Chen ◽  
Bai Zhang ◽  
Michael Schnaubelt ◽  
Punit Shah ◽  
Paul Aiyetan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Open Source ◽  
High Performance ◽  
Large Scale ◽  
Rapid Development ◽  
Data File ◽  
Data Sets ◽  
Proteomics Data ◽  
Amazon Web Services

ABSTRACTRapid development and wide adoption of mass spectrometry-based proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale. This progress has also created unprecedented challenges for individual labs to store, manage and analyze proteomics data, both in the cost for proprietary software and high-performance computing, and the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI) support, for LC-MS/MS data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignment, false discovery rate estimation, protein inference, determination of protein post-translation modifications, and quantitation of specific (modified) peptides and proteins. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale iTRAQ/TMT LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at: https://bitbucket.org/mschnau/ms-pycloud/downloads/

scholarly journals Automatic Classification of Morphologically Similar Fish Species Using Their Head Contours

2020 ◽  
Vol 10 (10) ◽  
pp. 3408
Author(s):  
Pere Marti-Puig ◽  
Amalia Manjabacas ◽  
Antoni Lombarte
Keyword(s):  
Fish Species ◽  
Image Data ◽  
Classification Problem ◽  
Marine Resources ◽  
Data Sets ◽  
Similar Form ◽  
Multi Class Classification ◽  
Demersal Species ◽  
Do So

This work deals with the task of distinguishing between different Mediterranean demersal species of fish that share a remarkably similar form and that are also used for the evaluation of marine resources. The experts who are currently able to classify these types of species do so by considering only a segment of the contour of the fish, specifically its head, instead of using the entire silhouette of the animal. Based on this knowledge, a set of features to classify contour segments is presented to address both a binary and a multi-class classification problem. In addition to the difficulty present in successfully discriminating between very similar forms, we have the limitation of having small, unreliably labeled image data sets. The results obtained were comparable to those obtained by trained experts.

scholarly journals DESIGN AND IMPLEMENTATION OF A FOVEAL PROJECTION DISPLAY

2008 ◽  
Vol 08 (02) ◽  
pp. 243-263 ◽  
Author(s):  
BENJAMIN A. AHLBORN ◽  
OLIVER KREYLOS ◽  
SOHAIL SHAFII ◽  
BERND HAMANN ◽  
OLIVER G. STAADT
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Image Data ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image ◽  
Projection Display ◽  
Display Resolution

We introduce a system that adds a foveal inset to large-scale projection displays. The effective resolution of the foveal inset projection is higher than the original display resolution, allowing the user to see more details and finer features in large data sets. The foveal inset is generated by projecting a high-resolution image onto a mirror mounted on a panCtilt unit that is controlled by the user with a laser pointer. Our implementation is based on Chromium and supports many OpenGL applications without modifications.We present experimental results using high-resolution image data from medical imaging and aerial photography.

scholarly journals Biological data annotation via a human-augmenting AI-based labeling system

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Douwe van der Wal ◽  
Iny Jhun ◽  
Israa Laklouk ◽  
Jeff Nirschl ◽  
Lara Richer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Microscopic Analysis ◽  
Image Data ◽  
Cell Types ◽  
Biological Data ◽  
Data Sets ◽  
Data Set ◽  
Data Annotation ◽  
Labeling System

AbstractBiology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

scholarly journals A Methodology for Automatic Detection and Classification of Pests using Optimized SVM in Greenhouse Crops

2019 ◽  
Vol 8 (6) ◽  
pp. 1485-1491
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Image Data ◽  
Space Structure ◽  
Digital Revolution ◽  
Greenhouse Crops ◽  
Point Of Interest ◽  
Agriculture Industry ◽  
Aerial Vehicle ◽  
Processing Techniques

Digital revolution is taking place in every industry. The technologies namely Cloud Computing and the Internet of Things (IoT) are considered to be as a digital revolution. Comparatively with other industries Agriculture industry has less usage of these digital revolutionary technologies. In recent years Agriculture industry uses such type of digital revolution technologies to counterpart traditional practices which greatly influence the productivity. The IoT is set to push the future of farming to the next level by collecting the production data which includes weather and soil data, image data of crop, pests, etc. through internet enabled communication objects. Performing computation and providing advisory on this large scale of data that is collected by communication objects by Cloud Computing technology in terms of Leaf is point of interest which has infestation problem with biological organisms such as pests observed by naked eye is time consuming. We make use of digital revolution device like Unmanned Aerial Vehicle (UAV) which collects the data from user point of inter-est, Digital Image Processing techniques, Pattern recognition Algorithms for above stated problem to develop an advisory based cloud system which provides advisory based on detection of pests present on off-seasonal crops rose, lengthy type crops cucumber which are cultivated in new agricultural farming i.e. limited space structure namely Greenhouse.

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