Volume 25 , Issue 1 , PP: 179-189, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Imène Issaouı 1 * , Afef Selmi 2
Doi: https://doi.org/10.54216/IJNS.250116
For smart cities to succeed, substantial developments to take place in roads, city streets, public transportation, houses, businesses, and other aspects of city life must be drawn up. In today’s world, there is a crucial necessity for effective management of cities to reduce the effect of COVID19 disease with increasing population in cities. Multiple metrics had already been taken to lower the infection rate of COVID19, from the beginning of the outbreaks, such as maintaining distance from another person and wearing face masks. Ensuring security in public places of smart cities needs state-of-the-art technology, including computer vision, deep learning and deep transfer learning for automated detection of face masks and monitoring of whether people wear masks accurately. The achievement of machine learning (ML and) artificial intelligence (AI) techniques in face recognition and object detection makes it fit for the development of FMD methods. The fundamental concept behind the generalized intuitionistic fuzzy soft set is highly productive in making decisions because it considers ways to manipulate an additional intuitionistic fuzzy input from the director to balance any disturbance in the data delivered by the assessment analyst. This manuscript offers the design of Weighted Soft Discernibility Matrix with Deep Learning Assisted Face Mask Detection (WSDMDL-FMD) technique for Smart City Environment. The WSDMDL-FMD technique proficiently discriminates the facial images with the presence or absence of masks. The WSDMDL-FMD technique comprises two stages: Mask RCNN-based face detection and WSDM-based face mask classification. Primarily, the WSDMDL-FMD technique uses Mask RCNN-based face detection. Next, the convolutional neural network (CNN) model derives features from the detected faces and its hyperparameters can be chosen by cuckoo optimization algorithm (COA). For face mask classification, the WSDMDL-FMD technique applies WSDM model. To evaluate the results of the WSDMDL-FMD technique, a series of experiments were involved. The obtained outcomes stated that the WSDMDL-FMD method reaches superior performance than other models
Artificial intelligence , Learning System , Smart City , Deep Learning , Face Mask Detection
[1] Smarandache, F., Neutrosophic set a generalization of the intuitionistic fuzzy sets. Inter. J. Pure Appl. Math., 24, 287 – 297, 2005.
[2] Mathew, L.P., Sebastian, L. and Thankachan, B., 2024. Some Operations on Trapezoidal Single Valued Neutrosophic Fuzzy Numbers. International Journal of Neutrosophic Science, 23(3), pp.29-9.
[3] Noaman, I.A.R., Hasan, A.H. and Ahmed, S.M., 2024. Optimizing Weibull Distribution Parameters for Improved Earthquake Modeling in Japan: A Comparative Approach. International Journal of Neutrosophic Science, 24(1), pp.65-5.
[4] Doaa Nihad Tomma, L. A. A. Al-Swidi. "Necessary and Sufficient Conditions for a Stability of the Concepts of Stable Interior and Stable Exterior via Neutrosophic Crisp Sets." International Journal of Neutrosophic Science, Vol. 24, No. 1, 2024 ,PP. 87-93
[5] Mathews, P., Sebastian, L. and Thankachan, B., 2024. Neutrosophic Fuzzy Score Matrices: A Robust Framework for Advancing Medical Diagnostics. International Journal of Neutrosophic Science, 23(3), pp.08-8.
[6] R. Saarumathi, W. Ritha. (2024). A Legitimate Productive Repertoire Replica Betwixt Envirotech Outlay Towards Fragile Commodities Using Trapezoidal Neutrosophic Fuzzy Number. International Journal of Neutrosophic Science, 24 ( 1 ), 104-118.
[7] G.J. Chowdary, N.S. Punn, S.K. Sonbhadra, S. Agarwal, Face Mask Detection using Transfer Learning of InceptionV3, in: International Conference on Big Data Analytics, Springer, Cham, 2020, pp. 81-90, pp. 1–11.
[8] S. Yang, P. Luo, C.C. Loy, X. Tang, WIDER FACE: A face detection benchmark, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016.
[9] B. Yang, J. Yan, Z. Lei, S.Z. Li, Fine-grained evaluation on face detection in the wild, in: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015, 2015.
[10] V. Jain, E. Learned-Miller, Fddb: A benchmark for face detection in unconstrained settings, UMass Amherst Tech. Rep., 2010, vol. 2, no. 4, UMass Amherst technical report, 2010.
[11] M. Inamdar, N. Mehendale, Real-Time Face Mask Identification Using Facemasknet Deep Learning Network, SSRN Electron. J. (2020).
[12] Habib, S., Alsanea, M., Aloraini, M., Al-Rawashdeh, H.S., Islam, M. and Khan, S., 2022. An efficient and effective deep learning-based model for real-time face mask detection. Sensors, 22(7), p.2602.
[13] Alsaheel, A., Alhassoun, R., Alrashed, R., Almatrafi, N., Almallouhi, N. and Albahli, S., 2023. Deep Fakes in Healthcare: How Deep Learning Can Help to Detect Forgeries. Computers, Materials & Continua, 76(2).
[14] Albahli, S. and Nawaz, M., 2023. MedNet: Medical deepfakes detection using an improved deep learning approach. Multimedia Tools and Applications, pp.1-19.
[15] Alajlan, N.N. and Ibrahim, D.M., 2022. TinyML: Enabling of inference deep learning models on ultra-low-power IoT edge devices for AI applications. Micromachines, 13(6), p.851.
[16] Alhasson, H.F., Almozainy, E., Alharbi, M., Almansour, N., Alharbi, S.S. and Khan, R.U., 2023. A Deep Learning-Based Mobile Application for Monkeypox Detection. Applied Sciences, 13(23), p.12589.
[17] Mostafa, S.A., Ravi, S., Zebari, D.A., Zebari, N.A., Mohammed, M.A., Nedoma, J., Martinek, R., Deveci, M. and Ding, W., 2024. A YOLO-based deep learning model for Real-Time face mask detection via drone surveillance in public spaces. Information Sciences, p.120865.
[18] Naseri, R.A.S., Kurnaz, A. and Farhan, H.M., 2023. Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach. Applied Soft Computing, 134, p.109933.
[19] Sheikh, B.U.H. and Zafar, A., 2024. Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system. Multimedia Tools and Applications, 83(8), pp.23873-23899.
[20] AL-Ghamdi, A.S., Alshammari, S.M. and Ragab, M., 2023. Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic. Computer Systems Science & Engineering, 46(2).
[21] Yasaswi, B., 2024. AOA based masked region-CNN model for detection of parking space in IoT environment. International Research Journal of Multidisciplinary Technovation, 6(1), pp.97-108.
[22] Vu, V.Q., Tran, M.Q., Amer, M., Khatiwada, M., Ghoneim, S.S. and Elsisi, M., 2023. A practical hybrid IoT architecture with deep learning technique for healthcare and security applications. Information, 14(7), p.379.
[23] Fontana, G., Calabrese, M., Agnusdei, L., Papadia, G. and Del Prete, A., 2024. SolDef_AI: An Open Source PCB Dataset for Mask R-CNN Defect Detection in Soldering Processes of Electronic Components. Journal of Manufacturing and Materials Processing, 8(3), p.117.
[24] Zahra, U., Khan, M.A., Alhaisoni, M., Alasiry, A., Marzougui, M. and Masood, A., 2023. An Integrated Framework of Two-Stream Deep Learning Models Optimal Information Fusion for Fruits Disease Recognition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[25] Gör, H., 2024. Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance. Mathematics, 12(3), p.483.
[26] Khan, M.J., Kumam, P., Liu, P., Kumam, W. and Ashraf, S., 2019. A novel approach to generalized intuitionistic fuzzy soft sets and its application in decision support system. Mathematics, 7(8), p.742.
[27] https://www.kaggle.com/datasets/mrviswamitrakaushik/facedatahybrid
[28] Sethi, S., Kathuria, M. and Kaushik, T., 2021. Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread. Journal of biomedical informatics, 120, p.103848.
