Optimized Laplacian Generalized Classifier Neural Network

Naik, Shraddha M.; Jagannath, Ravi Prasad K.; Kuppili, Venkatanareshbabu

doi:10.1109/icccnt.2018.8494187

Cited by 1 publication

(1 citation statement)

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“…Furthermore, the modification of neural network algorithms is a focus of several studies. Shraddha et al, identified that instead of using the gaussian RBF kernel on a generalized classifier neural network (GCNN), it is better to use the Laplace kernel and optimal smoothing parameter calculated using a population-based Sine Cosine Algorithm [19].…”

Section: Related Workmentioning

confidence: 99%

Improvement on I-Devices Using L-GCNN Classifier for Smart Mosque Simulation

Fadila¹,

Hariyadi²,

Hanani³

et al. 2022

ijasca

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I-Device (Intelligent Devices) is one of the fastest growing devices since the beginning of this decade. Some of its major problems are accuracy and performance. This study aims to present an improvement in the performance of those devices. We used a simulation application for I-Devices to conduct the experiment. The simulation was built based on classifying results using Logarithmic learning for Generalized Classifier Neural Networks (L-GCNN). The output was a simulation that will be implemented on a smart mosque system. L-GCNN itself was a modification method of GCNN to improve the processing speed and have high accuracy as a classifier method. This method will take a role when the given parameters meet the conditions of the devices to take an action. To simplify the understanding of the simulation models, we used a game application to make an interactive simulation for our project in an environment that represents the real-world condition of the mosque. The result of this study shows that the devices could make a decision by themselves accurately. Additionally, using LGCNN models, we could reduce the processing iteration compared to other models. The experiment results show that LGCNN has an average value of 90% in accuracy, precision, recall, and f1. Keywords: Automation, Classifier, L-GCNN, Neural Network, Decision.

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Section: Related Workmentioning

confidence: 99%