Biomedical Image Classification with Multi Response Linear Regression (MLR) as Meta-Learner Combiner and Its Effectiveness on Small to Large Data Sets

2020 ◽

pp. 30-46

Author(s):

Saranya N. ◽

Saravana Selvam

Keyword(s):

Big Data ◽

Random Forest ◽

Linear Regression ◽

Comprehensive Evaluation ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Classification Methods ◽

Computing Science ◽

Data Collections

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

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Cloud-enabled Fiji for reproducible, integrated and modular image processing

10.1101/2021.10.22.465513 ◽

2021 ◽

Author(s):

Ling-Hong Hung ◽

Evan Straw ◽

Shishir Reddy ◽

Zachary Colburn ◽

Ka Yee Yeung

Keyword(s):

Focal Adhesion ◽

3D Imaging ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Cloud Platform ◽

Biomedical Image ◽

Complex Image ◽

Different Types ◽

Computational Resources

Biomedical image analyses can require many steps processing different types of data. Analysis of increasingly large data sets often exceeds the capacity of local computational resources. We present an easy-to-use and modular cloud platform that allows biomedical researchers to reproducibly execute and share complex analytical workflows to process large image datasets. The workflows and the platform are encapsulated in software containers to ensure reproducibility and facilitate installation of even the most complicated workflows. The platform is both graphical and interactive allowing users to use the viewer of their choice to adjust the image pre-processing and analysis steps to iteratively improve the final results. We demonstrate the utility of our platform via two use cases in focal adhesion and 3D imaging analyses. In particular, our focal adhesion workflow demonstrates integration of Fiji with Jupyter Notebooks. Our 3D imaging use case applies Fiji/BigStitcher to big datasets on the cloud. The accessibility and modularity of the cloud platform democratizes the application and development of complex image analysis workflows.

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An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010008794x ◽

1991 ◽

Vol 49 ◽

pp. 726-727

Author(s):

John A. Hunt

Keyword(s):

Quantitative Analysis ◽

Large Data ◽

Difference Spectrum ◽

Large Data Sets ◽

Foil Thickness ◽

Data Sets ◽

Analysis Techniques ◽

Spectrum Imaging ◽

Normal Spectrum ◽

Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100132078 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1488-1489

Author(s):

Thomas W. Shattuck ◽

James R. Anderson ◽

Neil W. Tindale ◽

Peter R. Buseck

Keyword(s):

Cluster Analysis ◽

Chemical Reactivity ◽

Large Data ◽

Large Data Sets ◽

Particle Analysis ◽

Data Sets ◽

Halogen Chemistry ◽

Complete Study ◽

Components Analysis ◽

Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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Faculty Opinions recommendation of Detecting novel associations in large data sets.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13805958.793484294 ◽

2014 ◽

Author(s):

Daniel Lee

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Novel Associations

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NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

Visnyk Universytetu “Ukraina” ◽

10.36994/2707-4110-2019-2-23-03 ◽

2019 ◽

Author(s):

Mykhajlo Klymash ◽

Olena Hordiichuk — Bublivska ◽

Ihor Tchaikovskyi ◽

Oksana Urikova

Keyword(s):

Distributed Systems ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Data Decomposition ◽

Distributed Information ◽

Software Model ◽

Computing Performance ◽

Mapreduce Model ◽

Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of computational efficiency on distributed systems were obtained using the MPI messaging protocol and MapReduce node interaction software model. Were analyzed the efficiency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

Proceedings of the Water Environment Federation ◽

10.2175/193864718824828128 ◽

2018 ◽

Vol 2018 (6) ◽

pp. 38-39

Author(s):

Austa Parker ◽

Yan Qu ◽

David Hokanson ◽

Jeff Soller ◽

Eric Dickenson ◽

...

Keyword(s):

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Statistical Tools

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Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽

10.3390/computers10040047 ◽

2021 ◽

Vol 10 (4) ◽

pp. 47

Author(s):

Fariha Iffath ◽

A. S. M. Kayes ◽

Md. Tahsin Rahman ◽

Jannatul Ferdows ◽

Mohammad Shamsul Arefin ◽

...

Keyword(s):

Source Code ◽

Large Data ◽

Large Data Sets ◽

Detection Technique ◽

Data Sets ◽

Plagiarism Detection ◽

Source Codes ◽

Efficient Detection ◽

Mathematical Problems ◽

Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

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