Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.publishedVersio

An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.publishedVersio

Efficient and Secure Key Management and Authentication Scheme for WBSNs Using CP-ABE and Consortium Blockchain

publishedVersio

A community-based prediabetes knowledge assessment among Saudi adults in Al-Ahsa region, 2018

Background: Prediabetes has been considered to be a reversible condition; a modification of lifestyle and other intervention can be successfully applied during the prediabetes period to prevent the development of type 2 diabetes. The purpose of the present study was to assess knowledge of prediabetes and its risk factors for the community in the Al-Ahsa region.Design and method: A cross-sectional community-based study was conducted in the Al-Ahsa region from mid-to-late December 2018. A sample size of 812 was determined using a single-proportion formula.Results: Of the 812 respondents who gave consent to participate in the interview; the male to female ratio was 1.1:1. 13.2% of the respondents reported that they had diabetes. Among the respondents, 87.1% had a high level of knowledge of prediabetes, while 12.9% had low-to-moderate knowledge. 84% of males 40 years of age or older, 88.7% (384) of people with university or higher education, and 95.1% (78) of people who worked as health practitioners had high knowledge of prediabetes.Overall, there was a statistically significant association between age and prediabetes knowledge (2 =5.006, p=0.025). Occupation also showed a significant statistical association with prediabetes knowledge (2 =9.85, p=0.02). Conclusion: Knowledge is considered an important factor in the prevention of prediabetes and diabetes. People in Al-Ahsa demonstrated a high level of knowledge regarding some risk factors for prediabetes. However, there were a number of deficiencies in the knowledge of prediabetes risk factors and preventive measures as well as in general knowledge of prediabetes, which may lead to a high prevalence of prediabetes and diabetes

A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.publishedVersio

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network

(e deaf-mutes population always feels helpless when they are not understood by others and vice versa. (is is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing “Within Blocks” and “Before Classifier” methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models’ efficiency. (e experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. (e CBAM-ResNet “Before Classifier” models are more efficient than “Within Blocks” CBAM-ResNet models. (us, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the “Before Classifier” of CBAMResNet models is more efficient in recognising MSL and it is worth for future research

Teleworking Survey in Saudi Arabia: Reliability and Validity of Arabic Version of the Questionnaire

Objectives: This study aimed to adapt the survey questionnaire designed by Moens et al. (2021) and determine the validity and reliability of the Arabic version of the survey in a sample of the Saudi population experiencing teleworking. Methods: The questionnaire includes 2 sections. The first consists of 13 items measuring the impact of extended telework during the coronavirus disease 2019 (COVID-19) crisis. The second section includes 6 items measuring the impact of the COVID-19 crisis on self-view of telework and digital meetings. The survey instrument was translated based on the guidelines for the cultural adaptation of self-administrated measures. Results: The reliability of the questionnaire responses was measured by Cronbach’s alpha. The construct validity was checked through exploratory factor analysis followed by confirmatory factor analysis (CFA) to further assess the factor structure. CFA revealed that the model had excellent fit (root mean square error of approximation, 0.00; comparative fit index, 1.0; Tucker-Lewis index, 1; standardized root mean squared residual, 0.0). Conclusions: The Arabic version of the teleworking questionnaire had high reliability and good validity in assessing experiences and perceptions toward teleworking. While the validated survey examined perceptions and experiences during COVID-19, its use can be extended to capture experiences and perceptions during different crises

Transferred Monolayer And Ab Stacked Bilayer (0001) Sic Epitaxial Graphene

Graphene is a leading two dimensional (2D) material with good technological potential. Currently, it is being scaled up in synthesis methods in order to meet future demands in technology markets. In this dissertation, a study of transferred epitaxial graphene (TEG) as a synthesis method, for large area monolayer and AB stacked bilayer Graphene, is presented. Monolayer epitaxial graphene (EG) is grown on the (0001) face of silicon carbide (SiC) in an argon atmosphere at a temperature of 1600 o C. Bilayer graphene can thereafter be synthesized if needed by intercalating the monolayer in a 100% hydrogen flow at 1050 oC to release what is known as the "buffer layer" into another graphene layer forming a bilayer. Either form of graphene can subsequently be transferred off the SiC substrate to mitigate the negative effects of the substrate. We develop a transfer process based on a gold adhesion layer and demonstrate for the first time, the transfer of high quality monolayer transferred epitaxial graphene (MTEG) and AB stacked bilayer transferred epitaxial graphene (BTEG). We use Raman characterization methods to determine the number and quality of graphene layers as well as orientation for bilayers which was made possible by contrast enhancement upon substrate transfer. We report these characteristics for the first time. Extensive structural characterization that have never been done before and were made possible by the successful transfer procedure, are presented in the Transmission Electron Microscopy (TEM) section. We successfully show suspended MTEG and BTEG samples which was never shown in literature before. We fabricate Transmission Line Measurement (TLM) structures to study the quality of the contact resistance for MTEG and BTEG. We report values in the range of 600 [OMEGA].[MICRO SIGN]m for MTEG and 2400 [OMEGA].[MICRO SIGN]m for BTEG. We also fabricate Field Effect Transistors (FETs) to study the field effect mobility and carrier concentration of MTEG and BTEG. We report average room temperature field effect mobility values of around 1700 cm2/V.s with best value of 2800 cm2/V.s for MTEG. This is over two times gain in mobility before transfer and is competitive with current leading synthesis methods. We measured the room temperature field effect mobility of BTEG to be 250 cm2/V.s on average and with a best value of 335 cm2/V.s. To the knowledge of the author, there are no reports in literature on the measured mobility of BTEG. We carry out annealing studies at argon ambient of 300 oC for TEG and show unique properties for BTEG in which a demonstrated ten orders of magnitude, higher moisture absorption than MTEG is shown. A section in this dissertation will be dedicated to related work on chemical vapor deposition (CVD) hexagonal boron nitride (h-BN) which is a complimentary 2D material to graphene. Improvements on CVD growth by electropolishing the copper substrate will be demonstrated where root mean square (RMS) surface roughness of starting material is reduced from 177 nm to 12 nm, considerably improving subsequent h-BN CVD growth. A procedure for the transfer of CVD graphene onto CVD h-BN as well as fabrication of Van der Paw structures will be presented. We show initial results of improvements in mobility when CVD h-BN is used as a substrate for CVD graphene