Design and Development of Software based Waveform Generation using Microcontroller 8051

Kulkarni, Varada Potnis; Kulkarni, Pranav; Kulkarni, R.D.

doi:10.1109/icnte44896.2019.8945834

Cited by 1 publication

(2 citation statements)

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“…To address this problem, LSTM networks have been developed and applied to various applications such as stock price prediction, air quality monitoring, sea surface temperature monitoring, fight passenger count monitoring, and automatic speech recognition. Te model's efectiveness has been demonstrated through its good performance in the results [13].…”

Section: Previous Workmentioning

confidence: 94%

“…Te most prevalent models of neural networks are multiple-layer perceptron, convolutional neural networks, and recurrent neural networks. [13] Te use of RNNs in time series analysis is a common approach for representing hidden states and capturing data characteristics. However, simple RNNs sufer from the issue of long-term dependencies and are not efcient in utilizing past information collected over extended periods.…”

Section: Previous Workmentioning

confidence: 99%

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Investigation on Machine Learning Approaches for Environmental Noise Classifications

ALBAJI

Rashid

Hamid

2023

Journal of Electrical and Computer Engineering

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This project aims to investigate the best machine learning (ML) algorithm for classifying sounds originating from the environment that were considered noise pollution in smart cities. Sound collection was carried out using necessary sound capture tools, after which ML classification models were utilized for sound recognition. Additionally, noise pollution monitoring using Python was conducted to provide accurate results for sixteen different types of noise that were collected in sixteen cities in Malaysia. The numbers on the diagonal represent the correctly classified noises from the test set. Using these correlation matrices, the F1 score was calculated, and a comparison was performed for all models. The best model was found to be random forest.

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Section: Previous Workmentioning

confidence: 94%

Section: Previous Workmentioning

confidence: 99%