IoT Cloud‐Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model

Einy, Sajad; Öz, Cemil; Navaei, Yahya Dorostkar

doi:10.1155/2021/5047808

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…Multilayered feature extracted to enhance classifcation is input data. Face recognition experiments recognize faces from images [13]. Tis is because typical data processing technology cannot handle enormous amounts of data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Al-Abboodi,

Al-Ani

2024

Journal of Electrical and Computer Engineering

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Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

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Section: Introductionmentioning

confidence: 99%

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Al-Abboodi,

Al-Ani

2024

Journal of Electrical and Computer Engineering

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“…Therefore, the use of machine learning in improving routing in VANET can provide an effective solution. The application of machine learning methods has been proven in various fields of science such as wireless networks [3,4], social networks [5], network security [6][7][8], and pattern recognition [9][10][11][12][13][14]. One of the most important challenges in VANET is the delay in sending and receiving messages between vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

Xie

Navaei

Einy

2023

Wireless Communications and Mobile Computing

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In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly improved compared to the previous methods in terms of package delivery rate. Also, the proposed method with a total delay of 24.566 ms has a shorter delay than the previous methods in terms of message sending delay in the network.

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“…Recommended systems have been increasingly used by many applications such as e-commerce and e-health due to their ability to automatically extract useful information and predict and recommend appropriate results to consumers [7,8,20]. Therefore, a referral system for chronic disease management is needed to minimize both the risk of such a disease and its cost [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System

Nanehkaran

Licai

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Due to the growth of IoT applications, especially health care, the information of patients’ health records using data collection from IoT-connected devices has been considered. Biological data of patients in the health record helps to monitor the patient’s status and identify various diseases. Chronic diseases are a type of silent disease that, if not diagnosed in time, can cause irreparable damage to patients. The use of patients’ medical record data for early diagnosis of chronic diseases has recently attracted the attention of many researchers. On the other hand, the application of machine learning methods in the form of recommender systems has taken an important step in improving medical services and health care. In this paper, a medical recommender system was presented to identify and treat chronic diseases using an IoT device. In the present method, the electronic patient health record dataset that is loaded in the PhysioNet data repository has been used. In the present dataset, patients’ health records have been recorded according to the identified diseases and the physician’s diagnosis. In the proposed method, the K -nearest neighbor classification method is used to identify the type of disease, and the collaborative filtering method is used to find the appropriate treatment for patients. The results of the implementation of the proposed method show that this approach, based on the use of symptom similarity among patients, has good accuracy in diagnosing and predicting chronic diseases and has provided higher results than previous methods.

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IoT Cloud‐Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model

Cited by 9 publications

References 46 publications

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

Diagnosis of Chronic Diseases Based on Patients’ Health Records in IoT Healthcare Using the Recommender System

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