An Overview of Convolutional Neural Network: Its Architecture and Applications

Sakib, Shadman; Ahmed, Nazib; Kabir, Ahmed Jawad; Ahmed, Hridon

doi:10.20944/preprints201811.0546.v1

Cited by 28 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gated recurrent units (GRU) and long short-term memory (LSTM) are improved versions of RNN, and they provide state-of-the-art performance in many applications, including machine translation, speech recognition, and image captioning Abduh et al (2019). Heart sound signals are sequential data with strong temporal correlation, so heart sound classification can be efficiently processed by RNN Nogueira et al (2019); Ismail et al (2022); Sakib et al (2019). Figure 2 describes the Waveform representation of S1, S2, S3, and S4 sounds in systole and diastole intervals.…”

Section: Figurementioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

Heart sound classification plays a critical role in the early diagnosis of cardiovascular diseases. Although there have been many advances in heart sound classification in the last few years, most of them are still based on conventional segmented features and shallow structure-based classifiers. Therefore, we propose a new heart sound classification method based on improved mel-frequency cepstrum coefficient features and deep residual learning. Firstly, the heart sound signal is preprocessed, and its improved features are computed. Then, these features are used as input features of the neural network. The pathological information in the heart sound signal is further extracted by the deep residual network. Finally, the heart sound signal is classified into different categories according to the features learned by the neural network. This paper presents comprehensive analyses of different network parameters and network connection strategies. The proposed method achieves an accuracy of 94.43% on the dataset in this paper.

show abstract

Section: Figurementioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…The main advantage of CNNs compared to a traditional neural network is that they automatically detect important features without any human supervision. Specifically, CNNs learn relevant features from image/video at different levels, similar to a human brain [110]. This is very relevant to analyze both biomedical and food data, whose classification in view of safety security actions is extremely important.…”

Section: Applications Of Deep Learning Approaches For Nmr-based Metab...mentioning

confidence: 99%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

View full text Add to dashboard Cite

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

show abstract

“…The model used in rice disease classification relies on the Convolutional Network (CNN). CNN processes image data, which will build by the network using information from the executed process [8].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

Priyangka

Kumara

2021

LKJITI

View full text Add to dashboard Cite

Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.

show abstract

An Overview of Convolutional Neural Network: Its Architecture and Applications

Cited by 28 publications

References 29 publications

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

Contact Info

Product

Resources

About