A new neural network based algorithm for identifying handwritten mathematical equations

Shinde, Sagar; Waghulade, R. B.; Bormane, D. S.

doi:10.1109/icoei.2017.8300916

Cited by 11 publications

(1 citation statement)

References 9 publications

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“…This proposed compiler optimization model comprises three working phases: model training, feature extraction [24,25], as well as model exploitation (feature selection). First, the inputs were fed into the model training phase, which tries to match the right weights as well as bias to a learning algorithm [26,27] in order to minimize a loss function throughout the validation range.…”

Section: Proposed Compiler Optimization Prediction Modelmentioning

confidence: 99%

Compiler Optimization Prediction with New Self-Improved Optimization Model

Shewale¹,

Shinde²,

Gurav³

et al. 2023

IJACSA

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Users may now choose from a vast range of compiler optimizations. These optimizations interact in a variety of sophisticated ways with one another and with the source code. The order in which optimization steps are applied can have a considerable influence on the performance obtained. As a result, we created a revolutionary compiler optimization prediction model. Our model comprises three operational phases: model training, feature extraction, as well as model exploitation. The model training step includes initialization as well as the formation of candidate sample sets. The inputs were then sent to the feature extraction phase, which retrieved static, dynamic, and improved entropy features. These extracted features were then optimized by the feature exploitation phase, which employs an improved hunger games search algorithm to choose the best features. In this work, we used a Convolutional Neural Network to predict compiler optimization based on these selected characteristics, and the findings show that our innovative compiler optimization model surpasses previous approaches.

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