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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

Computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed Shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide daily.AlthoughChest X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently.In this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary classifier.Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested images.Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe.This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients.The results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent studies.Consequently,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

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Computers, Materials & Continua 2023年第8期76卷 1689-1700页

作者： Omar Reyad Hazem Ibrahim Shehata Mohamed Esmail Karar College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Engineering Zagazig UniversityZagazig44519Egypt Faculty of Electronic Engineering Menoufia UniversityMenouf32952Egypt

Falling is among the most harmful events older adults may encounter.With the continuous growth of the aging population in many societies,developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential.This paper presents a new healthcare Internet of Health Things(IoHT)architecture built around an ensemble machine learning-based fall detection system(FDS)for older people.Compared to deep neural networks,the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters.The number of cascaded random forest stages is automatically optimized.This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system.The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch.Each scenario in this dataset is classified and labeled as a fall or a non-fall.In comparison to the three machine learning models—K-nearest neighbors(KNN),decision tree(DT),and standard random forest(SRF),the proposed ensemble classifier outperformed the other models and achieved 98.4%accuracy.The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

关键词： Elderly population fall detection wireless sensor networks Internet of health things ensemble machine learning

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer Systems Science & Engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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

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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

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Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

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Computers, Materials & Continua 2021年第12期69卷 4095-4111页

作者： Mohamed Esmail Karar Omar Reyad Abdel-Haleem Abdel-Aty Saud Owyed Mohd F.Hassan College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt Faculty of Science Sohag UniversitySohag82524Egypt Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Mathematics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Computer and Information Science CeRDaSUniversiti Teknologi PetronasMalaysia

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes.Therefore,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm trees.In this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely InceptionResNet-V2.The sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm.Palm trees are labeled based on the sensor node number to identify the infested cases.Then,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,InceptionResNet-V2.The proposed infestation classifier has been successfully validated on the public TreeVibes database.It includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,respectively.Compared to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio recordings.The resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,respectively.Applying the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

关键词： Red palm weevils smart precision agriculture internet of things artificial intelligence

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Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

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Computer Systems Science & Engineering 2023年第10期47卷 721-739页

作者： Mohamed Esmail Karar Amged Sayed A.Mahmoud College of Computing and Information Technology(CCIT) Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportP.O.Box 12577Smart VillageSmart Village-B 2401Egypt

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide.This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions.The infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good performance.The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)algorithm.This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully.To avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller.The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

关键词： Intelligent medical systems telemedicine fuzzy control teaching learning-based optimization

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The content of some selected metabolites of Hypnea valentiae(Turner)Montagne from the Red Sea coast of Sudan

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Journal of Coastal Life Medicine 2016年第6期4卷 458-464页

作者： Nahid Abdel Rahim Osman Mona mohamed Alhassan Braa karar Abas Faculty of Marine Science and Fisheries Red Sea UniversityPort SudanP.O.box 24Sudan

Objective:To investigate some selected metabolic constituents of Hypnea valentiae(Turner)Montagne collected from the Red Sea coast of Sudan to evaluate the economic potential of the alga.Methods:Healthy thalli of the alga under investigation were collected,thoroughly cleaned,air dried and milled.Authentic analytical methods were used to determine moisture,ash,protein lipid and carrageenan contents.Fatty acids profile was revealed with gas chromatography instrumentation.Testing of phytochemical groups of compounds was based on the development of colouration and precipitation upon addition of certain chemical reagents to the extracts.Results:The ash content of the alga[(40.3±0.2)%of dry weight]was comparatively the highest among the parameters tested.The protein,lipid,and carrageenan constituted(9.30±0.70)%,(6.50±0.34)%,and(33.70±0.01)%,respectively of the alga dry matter.Hypnea valentiae(Turner)Montagne from Sudan Red Sea coast composed of 12 fatty acids,8 of which were unsaturated fatty acids and 4 were saturated fatty acids.The major unsaturated fatty acids in the alga was the trans-isomer of linoleic acid and linolelaidic acid(36.32%)followed by palmitoleic acid(13.64%).The major saturated fatty acids was the heptadecanoic acid amounting to 19.10%of the total fatty acids.Phytochemically the alga contained alkaloids,flavonoids and tannins.Conclusions:This alga may represent a promising source of functional food and therapeutic metabolites.Further investigation and critical evaluation of the bioactivity of the phytochemical compounds is required to assure the therapeutic potential of this alga.

关键词： Hypnea valentiae Sudan Red Sea Carrageenan Metabolites

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Men’s Perception of Domestic Violence, Rural Minia, Egypt

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Open Journal of Preventive Medicine 2016年第2期6卷 106-114页

作者： Ebtesam esmail Hassan Amany Edward Seedhom Eman mohamed Mahfouz Public Health Department Faculty of Medicine Minia University Minia Egypt

Background: Domestic violence is an important prevalent, yet hidden and ignored health concern with significant impact on women’s health and wellbeing. This research was guided by the belief that men could play a role in preventing domestic violence as the majority of domestic violence is perpetrated by men. Aim: To assess attitudes of men towards domestic violence and factors affecting violence supportive attitude. Methods: A cross-sectional community-based study conducted in a rural area, Minia District, 2015. A systematic random sample was used. An interview-administered questionnaire was used including questions related to violence supportive environments, and opinion of men of causes and existence of domestic violence within families. Gender equity score (GES) was applied. Results: A total of 440 males participated. 57.3% of the participants reported that domestic violence was sometimes justified and needed in certain cases, and nearly two third (69%) of them practiced violence against their wives mostly in the form of physical one (64%). Small proportion of males reported high support for gender equity (7%). The study identified reasons for domestic violence, as perceived by men: The commonest was marital disputes (48.9%), followed by financial problems (36.4%) and social and economic disparity between the spouses (12.7%). Regarding the effect of violence on families, 30.9% of males reported that it led to lack of proper education. Conclusion and Recommendations: There is a tendency to describe this community as abusive. There is a need for development of relevant and culturally sensitive domestic violence education/prevention program in rural Egypt.

关键词： Domestic Violence Male Perception Rural Minia Egypt

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埃及学生使用汉语名量词的偏误分析

埃及学生使用汉语名量词的偏误分析

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作者：委思柔（Esraa esmail mohamed Ali）北京外国语大学

学位级别：硕士

汉语量词在现代汉语中具有非常重要的地位,也是一个对外汉语教学的难点。对外国人而言汉语量词的用法比较复杂。本研究以埃及大学生为研究对象,采用问卷调查、访谈和作文分析法,对埃及大学生汉语名量词的情况进行调查研究。本文由五部... 详细信息

汉语量词在现代汉语中具有非常重要的地位,也是一个对外汉语教学的难点。对外国人而言汉语量词的用法比较复杂。本研究以埃及大学生为研究对象,采用问卷调查、访谈和作文分析法,对埃及大学生汉语名量词的情况进行调查研究。本文由五部分组成。第一章是选题意义、研究目的、研究方法及研究计划等。第二章有两个部分,分别是埃及大学生名量词学习情况的调查与埃及大学生的汉语名量词偏误类型分析。第一部分介绍问卷调查的目的、问卷的设计、调查对象和内容与方法,第二部分则对埃及大学生汉语名量词偏误类型调查结果进行详细分析,归纳出埃及学生汉语名量词使用中常出现的偏误类型,即包括量词误代、量词错序、量词缺漏和量词多余。第三章对埃及大学生使用汉语名量词的偏误的原因进行分析。第四章对埃及学生的汉语名量词教学提出一些建议,希望能对汉语教材的编写有所帮助。第五章是论文的结语。总之,埃及学生在汉语名量词使用上存在一些困难,本研究理解学生的困难,也试图帮助他们克服这些困难,以便他们更好地理解与使用汉语名量词。

关键词：埃及学生汉语名量词偏误分析教学建议

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prevention of renal dysfunction by nutraceuticals prepared from oil rich plant foods

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Asian Pacific Journal of Tropical Biomedicine 2014年第8期4卷 618-626页

作者： Sahar Y.Al-Okbi Doha A.mohamed Thanaa E.Hamed Reham SH.esmail Souria M.Donya Food Sciences and Nutrition Department National Research CentreDokkiCairoEgypt Pathology Department National Research CentreDokkiCairoEgypt Cytogenetic Department National Research CentreDokkiCairoEgypt

Objective:To investigate the protective effect of extracts prepared from avocado,walnut,flaxseed and Eruca sativa seeds in a rat model of kidney dysfunction induced by intraperitoneal cisplatin.Methods:Ethanol and petroleum ether extracts mixture was prepared from each plant.Six groups of rats were conducted:control healthy,cisplatin group and four test groups where rats were given daily oral dose of each extract mixture before cisplatin injection.Different biochemical and cytogenetic parameters and kidney histopathology were determined.Acute toxicity was tested for the nutraceuticals.Total phenolic contents,faity acids(FA) and unsaponifiable matter were assessed in the extracts.Results:Walnut ethanol extract showed the highest content of total phenolic.FA analysis revealed that all the studied plants were rich in unsaturated FA.Gas-liquid chromatographic investigation of the unsaponifiable matter showed the presence of campeslerol.stigmaslerol and β—sitosterol in all the studied plants.Cisplatin treatment induced significant increase in plasma urea,creatinine and malondialdehyde along with significant reduction of plasma albumin,total protein,calalase and total antioxidant as well as reduction in creatinine clearance.Histopathological examination proved the induction of kidney dysfunction.Some sorts of chromosomal aberration and spermshape abnormalities were noticed after cisplatin treatment.Administration of extracts mixtures produced improvements in biochemical,histopathological and cytogenetic parameters.Conclusions:Administration of the studied nutraceuticals proved to possess protective role against cisplatin—induced nephrotoxicity,chromosomal aberration and abnormal sperms.All studied uutraceuticals showed complete safety.

关键词： Nutraccutical Cisplatin Kidney dysfunction Rats Oxidative stress

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