A prediction model based on Big Data analysis using hybrid FCM clustering

Yang, Seokhwan; Kim, Jaechun; Chung, Mokdong

doi:10.1109/icitst.2014.7038833

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…NWP based accurate localized short term weather prediction system [7] predicting the values of meteorological variable. Another Prediction model based on big data analysis implements hybrid technique which innovates a FCM clustering algorithm for complex characteristics of the industries [8].…”

Section: Related Workmentioning

confidence: 99%

Environment Change Prediction to Adapt Climate- smart agriculture using big data analytics

Ramya¹,

Balaji²,

Girish³

et al. 2019

Preprint

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Now a day, the increased climate variability is challenging to farmers. It will expect to influence on crop and livestock productivity. So new approaches are required to farmers with updated and relevant information so as to support them in the decision making process, and to make them more resilient to climate change. The main objective of this project is to develop an application that predicts the environmental changes and to assist farmers in adapting to climate-smart Agriculture systems using big data Approach which increase the income and productivity of farmers. This paper is aiming to find Climate-Smart, Site-Specific Agriculture Decision-Making to problems, such as climate change-induced global food insecurity, to predicting and mitigating the impact of extreme weather events on global finance and also we can predict the Economic Dimensions of Climate Change around the world using Data and Analytics. This paper presents an enhanced automated prediction technique based on hadoop framework for efficient and scalable weather data analysing and forecasting system.

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Section: Related Workmentioning

confidence: 99%

Environment Change Prediction to Adapt Climate- smart agriculture using big data analytics

Ramya¹,

Balaji²,

Girish³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In our test, the clustering algorithms "Fruchtermann Reingold" and "ForceAtlas2" in the Gephi Suite were sufficient to visualize a usable structure of healthy and unhealthy bridges but the algorithm to create the edges had to be shaped by ourselves. Yang & Kim proposed a prediction model for Big Data Analysis based on hybrid FCM clustering [32]. This method provides the advantage of automatic classification of the data without preprocessing.…”

Section: Health Monitoring Through Graph Algorithmsmentioning

confidence: 99%

“…Supervised Learning has a high accuracy but several problems such as high requirements on the input data and difficult adoption if the data changes in structure. The hybrid FCM model combines the advantages of both models [32].…”

Section: Health Monitoring Through Graph Algorithmsmentioning

confidence: 99%