Automatic Face Mask Recognition System With FCM AND BPNN

Ayyappa, Yalanati; Neelakanteswara, P.; Bekkanti, Ashok; Tondeti, Yamini; Basha, Cmak Zeelan

doi:10.1109/iccmc51019.2021.9418243

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Recall 97% WIDER FACE + MAFA FACE [19] 2021 In an automated face mask identification system, median filtering and the back propagation neural network (BPNN) were proposed.…”

Section: Acc 97% Precision 92%mentioning

confidence: 99%

Increasing validation accuracy of a face mask detection by new deep learning model-based classification

Joodi

Saleh

Kadhim

2022

IJEECS

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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieved lower computational complexity and number of layers, while being more reliable compared with other algorithms applied to recognize face masks. The findings reveal that the model's validation accuracy reaches 97.55% to 98.43% at different learning rates and different values of features vector in the dense layer, which represents a neural network layer that is connected deeply of the CNN proposed model training. Finally, the suggested model enhances recognition performance parameters such as precision, recall, and area under the curve (AUC).

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“…Recall 97% WIDER FACE + MAFA FACE [19] 2021 In an automated face mask identification system, median filtering and the back propagation neural network (BPNN) were proposed.…”

Section: Acc 97% Precision 92%mentioning

confidence: 99%

Increasing validation accuracy of a face mask detection by new deep learning model-based classification

Joodi

Saleh

Kadhim

2022

IJEECS

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“…BPNN has been widely utilized across various research domains, including facial recognition [10], rainfall-runoff modelling [11], and stock price prediction [12]. Notably, studies have investigated the use of BPNN to model the relationship between compound structures and Kovats retention indices [13,14].…”

Section: Introductionmentioning

confidence: 99%

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds

Safhadi,

Noviandy,

Irvanizam

et al. 2024

Infolitika J. Data Sci.

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The identification of chemical compounds in essential oils is crucial in industries such as pharmaceuticals, perfumery, and food. Kovats Retention Index (RI) values are essential for compound identification using gas chromatography-mass spectrometry (GC-MS). Traditional RI determination methods are time-consuming, labor-intensive, and susceptible to experimental variability. Recent advancements in data science suggest that artificial intelligence (AI) can enhance RI prediction accuracy and efficiency. However, the full potential of AI, particularly artificial neural networks (ANN), in predicting RI values remains underexplored. This study develops a backpropagation neural network (BPNN) model to predict the Kovats RI values of essential oil compounds using five molecular descriptors: ATSc1, VCH-7, SP-1, Kier1, and MLogP. We trained the BPNN on a dataset of 340 essential oil compounds and optimized it through hyperparameter tuning. We show that the optimized BPNN model, with an epoch count of 100, a learning rate of 0.1, a hidden layer size of 10 neurons, and the ReLU activation function, achieves an R² value of 0.934 and a Root Mean Squared Error (RMSE) of 76.98. These results indicate a high correlation between predicted and actual RI values and a low average prediction error. Our findings demonstrate that BPNNs can significantly improve the efficiency and accuracy of compound identification, reducing reliance on traditional experimental methods.

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“…Furthermore, the adaptability of FCM is showcased through its integration with the whale optimization algorithm for innovative image segmentation approaches [11] and the construction of TSK fuzzy regression models utilizing FCM for enhanced data analysis precision [12]. Notably, FCM has also been applied in the automatic recognition of face masks using BPNN [13], in anti-noise image segmentation methods to improve reliability in noisy environments [14], and in the extraction of topics from textual data collections, demonstrating its versatility in handling unstructured data [15]. In more specific applications, FCM has contributed to the development of safety warning models for coal faces using fuzzy clustering and neural networks [16] and the enhancement of anomaly detection in databases through a FCM-based isolation forest method [17].…”

Section: Introductionmentioning

confidence: 99%

Integrating fuzzy metrics and negation operator in FCM algorithm via genetic algorithm for MRI image segmentation

Kutlu,

Ayaz,

Garg

2024

Neural Comput & Applic

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In this study, we redefine FCM algorithm by integrating fuzzy set theory, fuzzy metrics, and Sugeno negation principles. This innovative approach overcomes the limitations inherent in conventional machine learning models, especially in situations characterized by uncertainty, noise, and ambiguity. Our model utilizes the membership degrees from fuzzy set theory, and transforms the concept of proximity defined by fuzzy metrics into a minimization problem. This transformation is achieved using a linguistic negation operator, which is crucial for optimizing FCM algorithm's objective function. A significant innovation in our research is the use of GA for optimizing parameters within the contexts of fuzzy metrics and Sugeno negation. The precise optimization capabilities of GA greatly enhance the sensitivity and adaptability of FCM algorithm, thereby improving overall performance. By leveraging the meticulous parameter adjustments provided by GA, our approach has shown superior results in practical applications, such as brain MRI image segmentation, surpassing traditional methods. Experimental results highlight the considerable enhancements our proposed FCM algorithms bring over existing methods across various performance metrics. In conclusion, this study makes a valuable addition to the field of fuzzy-based machine learning methodologies. It combines the optimization strength of GA with the flexible classification capabilities of fuzzy logic. The integration of Sugeno negation and fuzzy metrics not only improves the accuracy and precision of FCM algorithm but also provides significant benefits in handling complex and ambiguous datasets. This research signifies a major advance in machine learning and fuzzy logic, setting the stage for future applications and studies.

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Automatic Face Mask Recognition System With FCM AND BPNN

Cited by 11 publications

References 13 publications

Increasing validation accuracy of a face mask detection by new deep learning model-based classification

Increasing validation accuracy of a face mask detection by new deep learning model-based classification

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds

Integrating fuzzy metrics and negation operator in FCM algorithm via genetic algorithm for MRI image segmentation

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