A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals

Fakhry, Mahmoud; Brery, Abeer FathAllah

doi:10.1109/iraset52964.2022.9738309

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…Although 2D CNN has been demonstrated to perform effectively in tasks requiring the classification of heart sounds, they need further feature transformations. Several CNN architectures have been proposed for 1D CNN‐based methods to identify abnormal heart sounds, including in studies such as Fakhry and Brery (2022), Krishnan et al (2020), and Li, Yao, et al (2020). In a typical example, as demonstrated in Zeng et al (2023), raw data is utilized as input to a 1D CNN without additional transformations.…”

Section: Deep Learning For Heart Sound Classificationmentioning

confidence: 99%

Artificial intelligence for heart sound classification: A review

Chen,

Guo,

et al. 2024

Expert Systems

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Heart sound signal analysis is very important for the early identification and treatment of cardiovascular illness. With rapid advancements in science and technology, artificial intelligence technologies are providing tremendous opportunities to enhance diagnosis and clinical decision‐making. Instruments can now perform clinical diagnoses that previously could only be handled by human experts more conveniently and efficiently. Despite multiple works on automatic heart sound analysis, there are few summarization and review works. This article attempts to give a thorough overview of various heart sound analysis subtasks and examine the improvements made in each subtask by both machine learning techniques and deep learning algorithms. It goals to highlight the potential of AI to revolutionize cardiovascular healthcare by enabling accurate and automated analysis of heart sounds. The findings of this review are beneficial for researchers, clinicians, and engineers in the development and application of AI‐based solutions for improved heart sound classification and diagnosis.

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Section: Deep Learning For Heart Sound Classificationmentioning

confidence: 99%

Artificial intelligence for heart sound classification: A review

Chen,

Guo,

et al. 2024

Expert Systems

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“…For instance, instant energy of the heart sound was extracted and used as the input of stacked auto-encoder networks [62]. Multiple statistical features (such as mean, median, variance, and many more) were extracted from all 75 ms segments in each complete heart sound clip, and fed into a bidirectional LSTM (BiLSTM) net for classification in [123].…”

Section: B Deep Learning For Classificationmentioning

confidence: 99%

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Zhao¹,

Chen²,

Nguyên³

et al. 2023

Preprint

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Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017-2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.

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“…In this study, the RMSProp optimizer is exploited for tuning the parameter of the 1DCNN model. The root mean squared propagation RMSprop optimizer was equivalent to SGDM optimizer [30]. The objective is to attenuate oscillation similar to momentum.…”

Section: B) 1dcnn Model With Rmsprop Optimizermentioning

confidence: 99%

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

Sharmila.G

2023

IJRITCC

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The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.

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A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals

Cited by 12 publications

References 21 publications

Artificial intelligence for heart sound classification: A review

Artificial intelligence for heart sound classification: A review

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

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