Prediction of Forest Fire using Neural Network based on Extreme Learning Machines (ELM)

2019 ◽  
Author(s):  
Mochammad Anshori ◽  
Farhanna Mar'i ◽  
Mukhammad Wildan Alauddin ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Neural Network ◽  
Forest Fire ◽  
Extreme Learning Machines ◽  
Learning Machines

scholarly journals Extreme learning machines for weather-based modelling of silk cocoon production

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
PRAMIT PANDIT ◽  
BISHVAJIT BAKSHI ◽  
SHILPA M.
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Extreme Learning Machines ◽  
Linear Regression Models ◽  
Weather Variables ◽  
Neural Network Models ◽  
Cocoon Production ◽  
Silk Cocoon ◽  
Learning Machines ◽  
Non Linear

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

scholarly journals Optimizing Convolutional Neural Network Parameters for Better Image Classification

2020 ◽  
Author(s):  
Manik Dhingra ◽  
Sarthak Rawat ◽  
Jinan Fiaidhi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Web Service ◽  
Recognition Task ◽  
Extreme Learning Machines ◽  
Data Set ◽  
Learning Machines ◽  
Fully Connected ◽  
Fully Connected Networks

The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.

scholarly journals Optimizing Convolutional Neural Network Parameters for Better Image Classification

2020 ◽  
Author(s):  
Manik Dhingra ◽  
Sarthak Rawat ◽  
Jinan Fiaidhi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Web Service ◽  
Recognition Task ◽  
Extreme Learning Machines ◽  
Data Set ◽  
Learning Machines ◽  
Fully Connected ◽  
Fully Connected Networks

The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.

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