Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model

Shinde, Rupali Kiran; Alam, Md. Shahinur; Park, Seong Gyoon; Park, Sang Myeong; Kim, Nam

doi:10.3390/healthcare10030454

Cited by 12 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[44] Convolutional-neural-network-based action recognition [47][48][49][50][51] CNN adapted in the agriculture, defense, and medicine sectors. [52][53][54][55][56][57][58] Artificial Neural Networks [44,54] Layered CNN [60][61][62][63]76,77] Inceptionv3 [64,65] Super-Resolution of Images (SRCNet) [66,67] Residual Networks [68][69][70][71] Model for detecting people don't wear masks [72][73][74] Mobile Networks (MobileNet v1 and MobileNetv2) Deep learning, TensorFlow, Keras, and OpenCV [78][79][80][81] Sensors Sensor Fusion (SF) approach with MobileNetv2, deep learning [82][83][84][85][86] As shown in Table 3, CNNs are the most diffused tool for face mask and face-masked recognition detection, given the several offered advantages like spatial invariance, parameter sharing, translation invariance, and scalability [90][91]…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

Al-Nabulsi

Turab

Owida

et al. 2023

Sensors

View full text Add to dashboard Cite

A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In [ 82 ], a novel Sensor Fusion (SF) method for detecting COVID-19 suspects was proposed. Also, the proposed system combines the SF algorithm with the MobileNetV2 model for face mask detection, improving the prediction accuracy.…”

Section: Face Mask Detection Sensorsmentioning

confidence: 99%

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

Al-Nabulsi

Turab

Owida

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In contrast to MobileNetV1, the major development of MobileNetV2 has two themes such as inverted ResNet block in the network and the implementation of linear bottleneck [17]. The fundamental concept of MobileNetV2 is a depth separable convolution that depends on an inverted ResNet with a linear bottleneck.…”

Section: Feature Extractionmentioning

confidence: 99%

Biometric Verification System Using Hyperparameter Tuned Deep Learning Model

Yamin¹,

Bajaba²,

Basahel³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Deep learning (DL) models have been useful in many computer vision, speech recognition, and natural language processing tasks in recent years. These models seem a natural fit to handle the rising number of biometric recognition problems, from cellphone authentication to airport security systems. DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems. Iris recognition was considered the more reliable and accurate biometric detection method accessible. Iris recognition has been an active research region in the last few decades due to its extensive applications, from security in airports to homeland security border control. This article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition (PODTL-BIR) model. The presented PODTL-BIR technique recognizes the iris for biometric security. In the presented PODTL-BIR model, an initial stage of pre-processing is carried out. In addition, the MobileNetv2 feature extractor is utilized to produce a collection of feature vectors. The PODTL-BIR technique utilizes a bidirectional gated recurrent unit (BiGRU) model to recognise iris for biometric verification. Finally, the political optimizer (PO) algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique's recognition efficiency. A wide-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR system. The experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

show abstract

“…In terms of dermatology, various diagnostic models using medical images have been performed as well as clinicians [6]. Recently, deep learning has provided end-to-end solutions to detect COVID-19 infection, lung cancer, skin lesions, brain and breast tumors, stomach ulcers, and colon cancer; predict blood sugar levels and heart disease; and detect face masks [7][8][9][10][11][12]. Machine learning also contributes to enhancing the mathematical prediction of cancer cell spreading rate [13].…”

Section: Introductionmentioning

confidence: 99%

Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning

Shinde

Alam

Hossain

et al. 2022

Cancers

Self Cite

View full text Add to dashboard Cite

Cancer remains a deadly disease. We developed a lightweight, accurate, general-purpose deep learning algorithm for skin cancer classification. Squeeze-MNet combines a Squeeze algorithm for digital hair removal during preprocessing and a MobileNet deep learning model with predefined weights. The Squeeze algorithm extracts important image features from the image, and the black-hat filter operation removes noise. The MobileNet model (with a dense neural network) was developed using the International Skin Imaging Collaboration (ISIC) dataset to fine-tune the model. The proposed model is lightweight; the prototype was tested on a Raspberry Pi 4 Internet of Things device with a Neo pixel 8-bit LED ring; a medical doctor validated the device. The average precision (AP) for benign and malignant diagnoses was 99.76% and 98.02%, respectively. Using our approach, the required dataset size decreased by 66%. The hair removal algorithm increased the accuracy of skin cancer detection to 99.36% with the ISIC dataset. The area under the receiver operating curve was 98.9%.

show abstract

Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model

Cited by 12 publications

References 27 publications

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

Biometric Verification System Using Hyperparameter Tuned Deep Learning Model

Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning

Contact Info

Product

Resources

About