Classification of orthostatic intolerance through data analytics

2021 ◽  
Vol 59 (3) ◽  
pp. 621-632
Author(s):  
Steven Gilmore ◽  
Joseph Hart ◽  
Justen Geddes ◽  
Christian H. Olsen ◽  
Jesper Mehlsen ◽  
...  
Keyword(s):  
Data Analytics ◽  
Orthostatic Intolerance

Classification of Pests for Rice Crop Using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 27-31
Author(s):  
R. P. L. Durgabai ◽  
P. Bhargavi ◽  
S. Jyothi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Andhra Pradesh ◽  
Big Data Analytics ◽  
Rice Crop ◽  
Strategic Decisions ◽  
High Productivity ◽  
Almost All ◽  
Big Data Technology

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.

scholarly journals Drone data for decision making in regeneration forests: from raw data to actionable insights

2020 ◽  
Author(s):  
Stefano Puliti ◽  
Aksel Granhus
Keyword(s):  
Data Analytics ◽  
Logistic Model ◽  
Laser Scanning ◽  
Binary Classification ◽  
Multiple Linear Regression Model ◽  
Cost Models ◽  
Airborne Laser ◽  
Aerial Vehicle ◽  
The Cost

Unmanned aerial vehicle (UAV) photogrammetric data and data analytics were used to model stand-level immediate tending need and cost in regeneration forests. Field reference data were used to train and validate a logistic model for the binary classification of immediate tending need and a multiple linear regression model to predict the cost to perform the tending operation. The performance of the models derived from UAV data was compared to models utilizing the following alternative data sources: airborne laser scanning data (ALS), prior inventory information (Prior), and the combination of UAV and Prior and ALS and Prior. The use of UAV and Prior data outperformed the remaining alternatives in terms of classification of tending needs, while UAV alone produced the most accurate cost models. Our results are encouraging for further use of UAVs in the operational management of regeneration forests and show that UAV data and data analytics are useful for deriving actionable insights.

scholarly journals A new theoretical understanding of big data analytics capabilities in organizations: a thematic analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

AbstractBig Data Analytics (BDA) usage in the industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision-making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in the organizational domain. Big Data has been defined in various ways and, the past literature about the classification of BDA and its capabilities is explored in this research. We conducted a literature review using PRISMA methodology and integrated a thematic analysis using NVIVO12. By adopting five steps of the PRISMA framework—70 sample articles, we generate five themes, which are informed through organization development theory, and develop a novel empirical research model, which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

A Database Framework for the Characterization and Classification of Parametric Models Based on Complexity Metrics to Support Data Analytics

2019 ◽  
Author(s):  
Jorge D. Camba ◽  
Manuel Contero ◽  
David Pérez-López ◽  
Pedro Company
Keyword(s):  
Data Analytics ◽  
Parametric Models ◽  
Graph Representation ◽  
Report Generation ◽  
Cad System ◽  
Complexity Metrics ◽  
Database Structure ◽  
Cad Models ◽  
Feature Based

Abstract We present a database architecture for exploring, classifying, and visualizing feature-based parametric CAD models based on quantitative complexity metrics. The system consists of (1) an external relational database structure where models are stored along with their graph representation and the numerical values of each complexity metric, (2) a client module that is integrated in the user’s CAD system and facilitates navigation within the repository, and (3) a report generation module that allows exporting CAD complexity data from the external repository to other applications for validation and analysis. In this paper, we justify the need for our system in the context of data analytics and discuss the rationale of its design as well as its architecture and implementations details. Finally, we describe a use case that illustrates the application of our framework in the characterization and evaluation of CAD models.

Extending The Theoretical Understanding of Big Data Analytics Capabilities In Organizations: A Thematic Analysis

2021 ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

Abstract Big Data Analytics (BDA) usage in industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in organizational domain. Big Data has been defined in various ways and , the past literature about classification of BDA and its capabilities is explored in this research . We conduct a literature review using PRISMA methodology, and integrate a thematic analysis using NVIVO12. By adopting five steps of PRISMA framework - , 70 sample articles we generate five themes, which informed through organization development theory, and develop a novel empirical research model which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

scholarly journals An Automatic Classification of Diabetics with Multilayer Perceptron using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 4978-4983
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Multilayer Perceptron ◽  
Data Analytics ◽  
Social Protection ◽  
Human Life ◽  
Automatic Classification ◽  
The Body ◽  
Run Time

Diabetes mellitus is one of the major non-transmittable sicknesses which have unimaginable impact on human life today. Enormous Data Analytics improves social protection structure through the reduction run time and the perfect cost. Automated investigation impacts the exact appraisal of diabetics in a successful way. A diabetic influences individuals in different pieces of the body. A PC technique on the shade diabetics ought to be inspected to analyze the various impacts definitely. This is the pre-screening framework for early determination by diabetologist. The proposed work provides the report on the order of injuries from diabetic's dataset with fundamental advances, for example, pre-preparing and characterization. Here Multilayer Perceptron investigation is utilized to separate the highlights. The re-enactment quantifies the precise finding and affirms the exactness esteems up to 95% for Classification.

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