A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients’ cough and breathing sounds

Aly, Mohammed; Alotaibi, Nouf Saeed

doi:10.1016/j.imu.2022.101049

Cited by 19 publications

(7 citation statements)

References 37 publications

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“…In ML, the accuracy score is an assessment metric that relates the number of correct predictions by a model to the overall number of predictions made. Equation (1) is used to measure the accuracy by dividing the number of correct predictions by the total number of predictions [ 31 , 32 ]:

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Section: Resultsmentioning

confidence: 99%

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Aly

Alotaibi

2023

Molecules

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Images of molecules are often utilized in education and synthetic exploration to predict molecular characteristics. Deep learning (DL) has also had an influence on drug research, such as the interpretation of cellular images as well as the development of innovative methods for the synthesis of organic molecules. Although research in these areas has been significant, a comprehensive review of DL applications in drug development would be beyond the scope of a single Account. In this study, we will concentrate on a single major area where DL has influenced molecular design: the prediction of molecular properties of modified gedunin using machine learning (ML). AI and ML technologies are critical in drug research and development. In these other words, deep learning (DL) algorithms and artificial neural networks (ANN) have changed the field. In short, advances in AI and ML present a good potential for rational drug design and exploration, which will ultimately benefit humanity. In this paper, long short-term memory (LSTM) was used to convert a modified gedunin SMILE into a molecular image. The 2D molecular representations and their immediately visible highlights should then provide adequate data to predict the subordinate characteristics of atom design. We aim to find the properties of modified gedunin using K-means clustering; RNN-like ML tools. To support this postulation, neural network (NN) clustering based on the AI picture is used and evaluated in this study. The novel chemical developed via profound learning has long been predicted on characteristics. As a result, LSTM with RNNs allow us to predict the properties of molecules of modified gedunin. The total accuracy of the suggested model is 98.68%. The accuracy of the molecular property prediction of modified gedunin research is promising enough to evaluate extrapolation and generalization. The model suggested in this research requires just seconds or minutes to calculate, making it faster as well as more effective than existing techniques. In short, ML can be a useful tool for predicting the properties of modified gedunin molecules.

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Section: Resultsmentioning

confidence: 99%

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Aly

Alotaibi

2023

Molecules

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“…In the assessment of facial recognition expression systems, it's imperative to evaluate their performance across multiple metrics to ensure their effectiveness. Four key metrics commonly utilized for this purpose are Accuracy, Precision, Recall, and F1-Score [44][45][46][47][48].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

Aly

2024

Multimed Tools Appl

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This paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.

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“…In the study of Loay Aly and Alotaibi [36] , a dual-stage deep learning approach for COVID-19 classification through cough and breath tones was proposed. First, the cough and breath sounds are transformed into images via a Mel-scale spectrogram approach.…”

Section: Related Studiesmentioning

confidence: 99%

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs

Almutairi

2023

Heliyon

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A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients’ cough and breathing sounds

Cited by 19 publications

References 37 publications

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs

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