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 4 publications

(3 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

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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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“…Furthermore, an IDS must have a low or zero percentage of false alarms in addition to detecting threats. Hence, the suggested model's performance is evaluated based on four important parameters, namely: Accuracy (ACY), Recall (RE), Precision (PRE), and F1-Score (FS) [37][38][39]. The strategy for evaluating the four metric parameters is represented by the following equations.…”

Section: Evaluation Stagementioning

confidence: 99%

Cyberattack Detection in Wireless Sensor Networks using a Hybrid Feature Reduction Technique with AI and Machine Learning Methods

Behiry

Aly

2023

Preprint

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Security of a Wireless Sensor Network (WSN) is crucial for preventing data sharing from intruders. This paper makes a suggestion for a machine learning-based intelligent hybrid model and AI for identifying cyberattacks. The security of a Wireless Sensor Network (WSN) guards against malevolent hackers cyberattacks on data, networks, and computers. The qualities that are most closely associated to the selected attack categories are also identified using a feature reduction algorithm (SVD and PCA) and machine learning methods. In order to reduce/extract features and rank them, this paper suggests using the K-means clustering model enhanced information gain (KMC-IG). A Synthetic Minority Excessively Technique is also being introduced. Intrusion prevention systems and network traffic categorization are the eventual important stage. The study evaluates the accuracy, precision, recall, and F-measure of a proposed deep learning-based feed-forward neural network algorithm for intrusion detection and classification. Three important datasets, namely NSL-KDD, UNSW-NB 15, and CICIDS 2017, are considered, and the proposed algorithm's performance is assessed for each dataset under two scenarios: full features and reduced features. The study also compares the results of the proposed DLFFNN-KMC-IG with benchmark machine learning approaches. After dimensional reduction and balancing, the proposed algorithm achieves high accuracy, precision, recall, and F-measure for all three datasets. Specifically, for the NSL-KDD dataset in the reduced feature set, the algorithm achieves 99.7% accuracy, 99.8% precision, 97.8% recall, and 98.8% F-measure. Similarly, for the CICIDS2017 dataset, the algorithm achieves 99.8% accuracy, 98.7% precision, 97.7% recall, and 98.7% F-measure. Finally, for the UNSW-NB15 dataset, the algorithm achieves 99.1% accuracy, 98.7% precision, 98.4% recall, and 99.6% F-measure.

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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 4 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

Cyberattack Detection in Wireless Sensor Networks using a Hybrid Feature Reduction Technique with AI and Machine Learning Methods

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