Tabular Data

2014 ◽  
pp. 73-86
Author(s):  
Sarah Stowell
Keyword(s):  
Tabular Data

Spatial Analysis of Water Resources Data in Selected Districts of Bihar

2019 ◽  
Vol 6 (03) ◽  
Author(s):  
MANIBHUSHAN MANIBHUSHAN ◽  
AKRAM AHMED
Keyword(s):  
Water Resources ◽  
Spatial Data ◽  
Water Bodies ◽  
Tabular Form ◽  
Tabular Data ◽  
Standing Water ◽  
Attribute Data ◽  
State Study ◽  
Scientific Personnel ◽  
Bihar State

The main aim of this study is to apply geographic information system (GIS) and data mining techniques to get the attribute data in a spatial and tabular form related to district wise availability of standing water bodies in their area and number of Bihar state. An analysis has been done on available spatial data and maps to get non-spatial/ tabular data, which are in a more easily understandable form. Data extracted district-wise related to area and number of standing water bodies according to their size of Bihar state. Study shows that the number and area of standing water bodies in Madhubani, East Champaran and Patna districts are 2185, 1753, 350 and 2355.42, 6752.36 and 8429.68 ham respectively. In this way, number and area of standing water bodies of other districts of Bihar are also extracted from geodatabases and digitized maps. This type of information is more useful than the spatial data because a common person is able to understand these tabular data and they can use this data for their own purposes. These data can be utilized by scientific personnel as well as farmers and that will be used in agriculture for better utilization of water resources to enhance agricultural productivity and income of farmers of Bihar state.

DeepSqueeze: Deep Semantic Compression for Tabular Data

2020 ◽  
Author(s):  
Amir Ilkhechi ◽  
Andrew Crotty ◽  
Alex Galakatos ◽  
Yicong Mao ◽  
Grace Fan ◽  
...  
Keyword(s):  
Tabular Data

scholarly journals Deep learning approach on tabular data to predict early-onset neonatal sepsis

2020 ◽  
pp. 1-21
Author(s):  
Redwan Hasif Alvi ◽  
Md. Habibur Rahman ◽  
Adib Al Shaeed Khan ◽  
Rashedur M. Rahman
Keyword(s):  
Deep Learning ◽  
Neonatal Sepsis ◽  
Early Onset ◽  
Learning Approach ◽  
Tabular Data

scholarly journals Crowdsourcing Historical Tabular Data

2019 ◽  
Author(s):  
Christian Clausner ◽  
Justin Hayes ◽  
Apostolos Antonacopoulos
Keyword(s):  
Tabular Data

scholarly journals Publisher Correction: Converting tabular data into images for deep learning with convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yitan Zhu ◽  
Thomas Brettin ◽  
Fangfang Xia ◽  
Alexander Partin ◽  
Maulik Shukla ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Tabular Data

Extracting Tabular data for Question-Answering from Documents

2020 ◽  
Author(s):  
Palak Jain ◽  
Tushar Goel ◽  
Ishan Verma ◽  
Mohammad Shakir ◽  
Lipika Dey ◽  
...  
Keyword(s):  
Question Answering ◽  
Tabular Data

scholarly journals WORCS: A workflow for open reproducible code in science

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Caspar J. Van Lissa ◽  
Andreas M. Brandmaier ◽  
Loek Brinkman ◽  
Anna-Lena Lamprecht ◽  
Aaron Peikert ◽  
...  
Keyword(s):  
Best Practices ◽  
Source Code ◽  
R Package ◽  
Open Science ◽  
Research Projects ◽  
Tabular Data ◽  
Step Procedure ◽  
Starting Point ◽  
Conducting Research ◽  
And Training

Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

scholarly journals Aggregating Time Series and Tabular Data in Deep Learning Model for University Students’ GPA Prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Harjanto Prabowo ◽  
Alam A. Hidayat ◽  
Tjeng Wawan Cenggoro ◽  
Reza Rahutomo ◽  
Kartika Purwandari ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
University Students ◽  
Learning Model ◽  
Tabular Data ◽  
Deep Learning Model
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