A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification

Chen, Xi; Kopsaftopoulos, Fotis; Wu, Qi; Ren, He; Chang, Fu‐Kuo

doi:10.3390/s19020275

Cited by 34 publications

(13 citation statements)

References 35 publications

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“…Aditya K et al proposed CNN with a tensor deep tracking approach [38] to classify the environmental sounds and obtained 49% and 77% accuracies on the ECS-10 dataset with CNN and 56% on the ECS-10 dataset with TDSN (Tensor Deep Stacking Network). Chen X et al proposed 1D CNN to identify flight state and obtained useful features dynamically from the basement of a newly built body wing via wind tunnel observations [39] . Pons J and Serra X proposed the CNN model for music sounds classification [40] .…”

Section: Background Workmentioning

confidence: 99%

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kumar¹,

Pja

2021

AIMS Public Health

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The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models. Results As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds. Conclusion A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

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Section: Background Workmentioning

confidence: 99%

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kumar¹,

Pja

2021

AIMS Public Health

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“…The vectors A and C is obtained by (42) where r 1 and r 2 are random vectors located in the slope of [0, 1] and the value of a lies between 0and2. The GWO has been recently applied in optimising flight models, especially to identify the flight state using CNNs [58] as well as modifying the hidden parameters of the SAE architecture [56]. Researchers suggest that the GWO is simple in design, fast with very high search precision, thereby making it easy to realise and implement in practical engineering applications [54].…”

Section: Grey Wolf Optimiser (Gwo)mentioning

confidence: 99%

Advances in Optimisation Algorithms and Techniques for Deep Learning

Nwankpa¹

2020

Adv. sci. technol. eng. syst. j.

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In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.

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“…The feature extraction phase is a key step to extract important elements of an object while the feature classification is a phase to classify the object into classes based on their similarity. In the feature extraction phase, it is important to define and select useful features to recognize the object [20]. The features are the characteristic of the object that can be measured such as shape, color, texture and other representations of features.…”

Section: Related Workmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.

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A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification

Cited by 34 publications

References 35 publications

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Advances in Optimisation Algorithms and Techniques for Deep Learning

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

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