A Low-cost Artificial Neural Network Model for Raspberry Pi

Truong, S.

doi:10.48084/etasr.3357

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The proposed algorithm used MNIST dataset to train, and testing showed a recognition rate of 94%. The proposed algorithm gave a recognition rate of 89% when tested for real-time objects, which is around 2.7 times faster than the conventional neural network for MNIST image recognition [6].…”

Section: Literature Surveymentioning

confidence: 92%

A Check on WHO Protocol Implementation for COVID-19 Using IoT

Sharma

Dhingra

Dawar

2021

Internet of Things and Its Applications

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COVID-19, which is also known as coronavirus disease, causes a situation of emergency in all over the world from March 2020, and every country was under lockdown to control its rigorous spread. While other people are self-conscious about their looks, they hide their emotions from the public by hiding their faces. Scientists proved that wearing face masks works on impeding COVID-19 transmission [1]. Since the outbreak, the WHO suggested some measures to control the spread, and the governance bodies all over the world are adopting measures suggested by the WHO. The essential precautions that the World Health Organization suggested are to maintain personal hygiene and social distancing and to wear a face mask in public places. Wearing a mask in public areas is one of the most effective methods, and it actually reduces the growth rate of COVID-19 by 40%. However, wearing a mask for a very long time could be exhausting and frustrating, and this is the main reason that people do not follow it [2]. The lawmakers and the governments worldwide are facing a lot of difficulties in controlling the spread of this deadly virus [3]. To ensure that this protocol is followed, an IoT system is proposed, which will ensure a correct way to wear a face mask and thus can result in a controlled situation when it comes to the spread of the coronavirus pandemic.The coronavirus epidemic has given rise to an extraordinary degree of worldwide scientific cooperation. Artificial intelligence (AI) based on machine learning and deep learning can help to fight COVID-19 in many ways [4]. The advancements in the area of machine learning and data analysis have allowed various researchers and

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Section: Literature Surveymentioning

confidence: 92%

A Check on WHO Protocol Implementation for COVID-19 Using IoT

Sharma

Dhingra

Dawar

2021

Internet of Things and Its Applications

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“…The Raspberry Pi 3 is simply a performed sized card processor [31], containing a micro-controller and a CPU. The Raspberry Pi processor core system is a Broadcom BCM2837 System-on-Chip (SoC) multimedia processor, which has 64-bit quad-core ARMv8 Cortex A53 with 1GB of RAM.…”

Section: A Used Raspberry Pi Card Synopsis and System Patternmentioning

confidence: 99%

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Helali

Hajaiej

Chérif

2020

Eng. Technol. Appl. Sci. Res.

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The real-time performance of Automatic Speech Recognition (ASR) is a big challenge and needs high computing capability and exhaustive memory consumption. Getting a robust performance against inevitable various difficult situations such as speaker variations, accents, and noise is a tedious task. It’s crucial to expand new and efficient approaches for speech signal extraction features and pre-processing. In order to fix the high dependency issue related to processing succeeding steps in ARS and enhance the extracted features’ quality, noise robustness can be solved within the ARS extraction block feature, removing implicitly the need for further additional specific compensation parameters or data collection. This paper proposes a new robust acoustic extraction approach development based on a hybrid technique consisting of Perceptual Wavelet Packet (PWP) and Mel Frequency Cepstral Coefficients (MFCCs). The proposed system was implemented on a Rasberry Pi board and its performance was checked in a clean environment, reaching 99% average accuracy. The recognition rate was improved (from 80% to 99%) for the majority of Signal-to-Noise Ratios (SNRs) under real noisy conditions for positive SNRs and considerably improved results especially for negative SNRs.

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An improved nonlinear correlation method for feature selection of complex data

Shang

2023

Nonlinear Dyn

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A Low-cost Artificial Neural Network Model for Raspberry Pi

Cited by 6 publications

References 17 publications

A Check on WHO Protocol Implementation for COVID-19 Using IoT

A Check on WHO Protocol Implementation for COVID-19 Using IoT

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

An improved nonlinear correlation method for feature selection of complex data

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