A New Model to Detect COVID-19 Coughing and Breathing Sound Symptoms Classification from CQT and Mel Spectrogram Image Representation using Deep Learning

Aly, Mohamed; Alotaibi, Nouf Saeed

doi:10.14569/ijacsa.2022.0130869

Cited by 5 publications

(4 citation statements)

References 33 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

Self Cite

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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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“…Classification plays a crucial role in the medical field [35][36][37], and this manuscript specifically focuses on T2W images from the dataset. The MR images of the BraTS type do not require any preprocessing steps such as head stripping or noise removal.…”

Section: Classificationmentioning

confidence: 99%

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Aly,

Alotaibi

2023

CMC

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Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection. Through simulation results, the chi-square method for feature selection successfully identifies and selects four GLCM features. By utilizing the modified U-Net architecture, we achieve precise segmentation of tumor images into three distinct regions: the whole tumor (WT), tumor core (TC), and enhanced tumor (ET). The proposed method consists of two important elements: an encoder component responsible for down-sampling and a decoder component responsible for up-sampling. These components are based on a modified U-Net architecture and are connected by a bridge section. Our proposed CNN architecture achieves superior classification accuracy compared to existing methods, reaching up to 99.65%. Additionally, our suggested technique yields impressive Dice scores of 0.8927, 0.9405, and 0.8487 for the tumor core, whole tumor, and enhanced tumor, respectively. Ultimately, the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods. The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

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A New Model to Detect COVID-19 Coughing and Breathing Sound Symptoms Classification from CQT and Mel Spectrogram Image Representation using Deep Learning

Cited by 5 publications

References 33 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

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

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