检索结果-南通市图书馆

The impact of foot reflexology on fatigue and sleep quality in school-aged children undergoing hemodialysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Integrative Nursing 2024年第2期6卷 76-82页

作者： Amal Hashem MOHAMED Mostafa SHABAN Huda Hamdy mohammed Randa Mohamed ABOBAKER Salman Hamdan ALSAQRI Rania abd-elnaby mohammed ALLAM Faculty of Nursing Minia UniversityMinyaEgypt College of Nursing Jouf UniversitySakakaSaudi Arabia Faculty of Nursing Cairo UniversityGizaEgypt Northern College of Nursing ARARSaudi Arabia College of Nursing University of HailHailSaudi Arabia

Objective:This study aimed to evaluate the effects of foot reflexology on fatigue and sleep quality in school-aged children receiving hemodialysis.Methods:A quasi-experimental pretest-posttest design was utilized.Thirty children of ages 6-12 undergoing in-center hemodialysis were recruited.The Pittsburgh Sleep Quality Index(PSQI)and Inventory of Fatigue Symptom(IFS)scales were administered at baseline.Participants then received 30 min of foot reflexology massage before hemodialysis sessions 3 days per week for 12 weeks.Posttest administration of the sleep and fatigue scales occurred after the intervention period.Results:Reflexology massage led to significant improvements in sleep quality components,including duration(0%-30%normal sleepers),efficiency(0%-50%>85%),latency(50%-0%>60 min),disturbances,and daytime dysfunction.The mean PSQI score decreased from 18.2 to 9.7(P<0.05).Fatigue severity substantially decreased,with the mean IFS score improving from 105.7 to 64.1(P<0.05).Conclusion:Foot reflexology is an effective nursing intervention for reducing fatigue and improving sleep quality in children on hemodialysis,warranting integration into routine care.

关键词： Fatigue hemodialysis massage pediatric reflexology sleep

Capacity and Fairness Maximization-Based Resource Allocation for Downlink NOMA Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第10期69卷 521-537页

作者： mohammed abd-elnaby Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Non-orthogonal multiple access(NOMA)is one of the leading technologies for 5G communication.User pairing(UP)and power allocation(PA)are the key controlling mechanisms for the optimization of the performance of NOMA systems.This paper presents a novel UP and PA(UPPA)technique for capacity and fairness maximization in NOMA called(CFM-UPPA).The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated.Then,based on this investigation,the PA and UP algorithms of the CFM-UPPA technique are proposed.The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness,and its maximum value is adjusted to guarantee the successive interference cancellation(SIC)constraints.The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity.The performance evaluation of the proposed CFM-UPPA technique in terms of capacity,fairness,and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access(OMA)system and that of the NOMA system with random UP.Also,the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms,such as the random UP algorithm.

关键词： 5G non-orthogonal multiple access user pairing power allocation capacity fairness

Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid El-Shafai Randa Ali Ahmed Sedik Taha El-Sayed Taha mohammed abd-elnaby Fathi E.abd El-Samie Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama mohammed abd-elnaby Fathi E.abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades.It achieves safe access to systems using biometrics instead of the traditional passwords.The utilization of a biometric in its original format makes it usable only once.Therefore,a cancelable biometric template should be used,so that it can be replaced when it is attacked.Cancelable biometrics aims to enhance the security and privacy of biometric authentication.Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates.In this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems.The considered biometric in this paper is the speech signal.The speech signal is transformed into its spectrogram.Then,the spectrogram is encrypted using two cascaded optical encryption algorithms.The first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption tool.The OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is obtained.The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format.If the correlation value is larger than a threshold value,the user is authorized.The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves.The implementation of optical encryption is adopted using its software rather than the optical setup.The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results.It can improve the biometric data security without deteriorating the recognition accuracy.Simulation results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Characteristic(AROC)curve.

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

Efficient Deep CNN Model for COVID-19 Classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy mohammed abd-elnaby Fathi E.abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are

关键词： COVID-19 image classification CNN DL activation functions optimizers

Fair subcarrier-power allocation scheme for multiuser multicarrier systems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Central South University 2015年第8期22卷 3033-3041页

作者： mohammed abd-elnaby Germien G.Sedhom Nagy W.Messiha Xu Zhu Fathi E.abd El-Samie Faculty of Electronic Engineering Menoufia University Deptartment of Electrical Engineering and Electronics University of Liverpool

The main objective of multiuser orthogonal frequency division multiple access(MU-OFDM) is to maximize the total system capacity in wireless communication systems. Thus, the problem in MU-OFDM system is the adaptive allocation of the resources(subcarriers, bits and power) to different users subject to several restrictions to maximize the total system capacity. In this work, a proposed subcarrier allocation algorithm was presented to assign the subcarriers with highest channel gain to the users. After the subcarrier allocation, subcarrier gain-based power allocation(SGPA) was employed for power and bit loading. The simulation results show that the proposed subcarrier-power allocation scheme can achieve high total system capacity and good fairness in allocating the resources to the users with slightly high computational complexity compared to the existing subcarrier allocation algorithms.

关键词：多载波系统分配方案载波功率多用户子载波分配正交频分多址接入无线通信系统 OFDM系统

Smartphone-Based Wi-Fi Analysis for Bus Passenger Counting

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第4期79卷 875-907页

作者： mohammed Alatiyyah Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile devices.The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public transportation.The proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger numbers.The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of crowding.However,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual count.While acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public transportation.The implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.

关键词： Public transportation digital environment passenger estimation signal capturing Wi-Fi

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar Reyad mohammed abd-elnaby abdel-Haleem abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound

Human dental pulp stem/stromal cells in clinical practice

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Stem Cells 2024年第2期16卷 54-57页

作者： mohammed E Grawish Department of Oral Biology Faculty of DentistryMansoura UniversityMansoura 35561Egypt Department of Oral Biology Faculty of Oral and Dental MedicineDelta University for Science and TechnologyMansoura 11152Egypt

Dental pulp stem/stromal cells(DPSCs)are fibroblast-like,neural crest-derived,and multipotent cells that can differentiate into several lineages.They are relatively easy to isolate from healthy and inflamed pulps,with little ethical concerns and can be successfully cryopreserved and thawed.The therapeutic effects of DPSCs derived from animal or human sources have been extensively studied through in-vitro and in-vivo animal experiments and the findings indicated that DPSCs are effective not only for dental diseases but also for systemic diseases.Understanding that translational research is a critical step through which the fundamental scientific discoveries could be translated into applicable diagnostics and therapeutics that directly benefit humans,several clinical studies were carried out to generate evidence for the efficacy and safety of autogenous or allogeneic human DPSCs(hDPSCs)as a treatment modality for use in cell-based therapy,regenerative medicine/dentistry and tissue engineering.In clinical medicine,hDPSCs were effective for treating acute ischemic stroke and human exfoliated deciduous teeth-conditioned medium(SHED-CM)repaired vascular damage of the corpus cavernous,which is the main cause of erectile dysfunction.Whereas in clinical dentistry,autologous SHED was able to rege-nerate necrotic dental pulp after implantation into injured teeth,and micrografts enriched with autologous hDPSCs and collagen sponge were considered a treatment option for human intrabony defects.In contrast,hDPSCs did not add a significant regenerative effect when they were used for the treatment of post-extraction sockets.Large-scale clinical studies across diverse populations are still lacking to provide robust evidence on the safety and efficacy of hDPSCs as a new treatment option for various human diseases including dental-related problems.

关键词： Dental pulp stem/stromal cells Human clinical studies Regenerative medicine Regenerative dentistry Cell-based therapy