Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost

Dar, Jawad Ahmad; Srivastava, Kamal Kr; Lone, Sajaad Ahmed

doi:10.1016/j.compbiomed.2022.106123

Cited by 17 publications

(4 citation statements)

References 29 publications

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“…For Instance, the two critical concerns in the medical industry are considering analysing data and a precise diagnosis. However, analysing pulmonary abnormalities is time-consuming and may depend on the doctors' diagnostic competence and clinical expertise [ 39 ]. Pre-processing is done on the pulmonary incoming sound patterns in order to successfully extract the key elements required for future processing.…”

Section: Discussionmentioning

confidence: 99%

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Dagne,

Palanivel

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Dagne,

Palanivel

et al. 2023

Heliyon

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“…Table 1 illustrates the heterogeneous characteristics of several common NLP datasets. Additionally, language data also exhibits the characteristics of a long-tail distribution, where low-frequency words account for a high proportion in the corpus, while the coverage of high-frequency words is limited, posing challenges to word representation learning [2]. Designing deep learning architectures that can adapt to the heterogeneity and complexity of data is an urgent problem to be solved.…”

Section: Fusion Challenges Of Deep Learning and Natural Language Proc...mentioning

confidence: 99%

Optimization Strategies for Deep Learning Models in Natural Language Processing

Yao,

Yuan

2024

JTPES

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Deep learning models have achieved remarkable performance in the field of natural language processing (NLP), but they still face many challenges in practical applications, such as data heterogeneity and complexity, the black-box nature of models, and difficulties in transfer learning across multilingual and cross-domain scenarios. In this paper, corresponding improvement measures are proposed from four perspectives: model structure, loss functions, regularization methods, and optimization strategies, to address these issues. Extensive experiments on three tasks including text classification, named entity recognition, and reading comprehension confirm the feasibility and effectiveness of the proposed optimization solutions. The experimental results demonstrate that introducing innovative mechanisms like Multi-Head Attention and Focal Loss, and judiciously applying techniques such as LayerNorm and AdamW, can significantly improve model performance. Finally, this paper also explores model compression techniques, providing new insights for deploying deep models in resource-constrained scenarios.

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“…Mel-scale Frequency Cepstral Coe cients (MFCC) has been validated as one of the most superior features [8]. In the early diagnosis of COVID-19, MFCC features extracted from cough audio samples via a deep convolutional neural network have achieved a recognition accuracy of 82.7% [9]. Several studies have employed audio and motion data to develop a CNN-based model for detecting cough episodes in the eld, with a precision of 82% and a recall rate of 55% [10].…”

Section: Introductionmentioning

confidence: 99%

Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring

Wang,

Min,

et al. 2024

Preprint

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This study introduces an automated, non-intrusive method for detecting cough events in clinical settings, using a flexible chest patch with tri-axial acceleration sensors. It aims to achieve remote and automatic identification of coughing in patients with respiratory system diseases, providing clinicians with a tool for the remote and objective recording and recognition of changes in patients' cough patterns. Twenty-five young and healthy persons (hereinafter referred to as healthy adults) and twenty-five clinical diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor. The top 56% of the higher scoring features were then combined using several feature selection algorithm to perform the cough classification task. The Multi-Criteria Decision Making (MCDM) method was used to select the classifier with the highest scores. The optimized classifier proposed in this paper achieved Accuracy of 87.1%, Precision of 95%, Recall of 79.1%, F1 score of 86.4%, and AUC of 95.4% for recognizing coughs in healthy adults, and Accuracy of 96.1%, Precision of 95%, Recall of 97.4%, F1 score of 96.2%, and AUC of 98.7% for recognizing coughs in patients; and an overall Accuracy of 92% for distinguishing coughs in the combined group of healthy adults and patients. Our study demonstrates the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrences in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate the clinical status.

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Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost

Cited by 17 publications

References 29 publications

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Advanced modeling and optimizing for surface sterilization process of grape vine (Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques

Optimization Strategies for Deep Learning Models in Natural Language Processing

Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring

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