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Towards hepatitis C virus elimination:Egyptian experience,achievements and limitations

World Journal of Gastroenterology 2018年第38期24卷 4330-4340页

作者： Dalia Omran mohamed Alboraie Rania A Zayed mohamed-Naguib Wifi Mervat Naguib mohamed Eltabbakh mohamed Abdellah Ahmed Fouad Sherief Sahar Maklad Heba Hamdy Eldemellawy Omar Khalid Saad doaa mohamed khamiss mohamed El Kassas Department of Endemic Medicine and Hepatology Faculty of MedicineCairo UniversityCairo 11651Egypt Department of Endemic Medicine Faculty of MedicineHelwan UniversityCairo 11599Egypt Department of Internal Medicine Al-Azhar UniversityCairo 11651Egypt Department of Clinical and Chemical Pathology Faculty of MedicineCairo UniversityCairo 11651Egypt Department of Internal Medicine Faculty of MedicineCairo UniversityCairo 11599Egypt Department of Tropical Medicine Faculty of MedicineAin Shams UniversityCairo 11566Egypt National Hepatology and Tropical Medicine Research Institute Cairo 11599Egypt Department of Internal Medicine Faculty of MedicineBeni-Suef UniversityBeni-Suef 62511Egypt Faculty of Medicine Ain Shams UniversityCairo 11566Egypt Department of Clinical and Chemical Pathology El-monera hospitalMinistry of HealthCairo 11562Egypt

Worldwide, more than one million people die each year from hepatitis C virus(HCV) related diseases, and over 300 million people are chronically infected with hepatitis B or C. Egypt used to be on the top of the countries with heavy HCV burden. Some countries are making advances in elimination of HCV, yet multiple factors preventing progress; remain for the majority. These factors include lack of global funding sources for treatment, late diagnosis, poor data, and inadequate screening. Treatment of HCV in Egypt has become one of the top national priorities since 2007. Egypt started a national treatment program intending to provide cure for Egyptian HCV-infected patients. Mass HCV treatment program had started using Pegylated interferon and ribavirin between 2007 and 2014. Yet, with the development of highly-effective direct acting antivirals(DAAs) for HCV, elimination of viral hepatitis has become a real possibility. The Egyptian National Committee for the Control of Viral Hepatitis did its best to provide Egyptian HCV patients with DAAs. Egypt adopted a strategy that represents a model of care that could help other countries with high HCV prevalence rate in their battle against HCV. This review covers the effects of HCV management in Egyptian real life settings and the outcome of different treatment protocols. Also, it deals with the current and future strategies for HCV prevention and screening as well as the challenges facing HCV elimination and the prospect of future eradication of HCV.

关键词： Hepatitis C virus Egypt Direct acting antivirals screening elimination Limitations

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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Computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel doaa Sami Khafaga Ahmed mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial operations.Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization.Network infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node distribution.This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and design.By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation.Through implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network operation.We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world problems.Being the most efficient in the optimization process,Biruni displays the lowest error rate at 0.00032.The two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific problems.Due to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made.Through delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Prevelance of Bovine Cysticercosis in Egypt and the Cysticidal Effect of Two Extracts Obtained from Balanites aegyptiaca and Moringa oleifera on Mice Model Affected with T. saginata Cysticerci

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Open Journal of Animal Sciences 2024年第2期14卷 39-55页

作者： Omnia M. Kandil Noha M. F. Hassan doaa Sedky Hatem A. Shalaby Heba M. Ashry Nadia M. T. Abu El Ezz Sahar M. Kandeel mohamed S. Abdelfattah L. Ying Ebtesam M. Al-Olayan Parasitology and Animal Diseases Department Veterinary Research Institute National Research Centre Dokki Cairo Egypt Chemistry of Natural Compounds Department Pharmaceutical and Drugs Industries Research Institute National Research Centre Dokki Cairo Egypt Chemistry Department Faculty of Science Helwan University Cairo Egypt State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining China College of Agriculture and Animal Husbandry Qinghai University Xining China Department of Zoology Collage of Science King Saud University Riyadh Saudi Arabia

The aim of the present study was to determine the prevalence of bovine cysticercosis in both cattle and buffloas, in Egypt and to assess the cysticidal efficacy of Balanites aegyptiaca fruits (B. aegyptiaca) and Moringa oleifera seeds (M. oleifera) extracts in experimentally infected mice. The study detected the level of tumor necrosis factor (TNF-α) to monitor the immune and inflammatory responses of experimentally infected mice. Through meat inspection, a total number of 2125 male bovine, 2 to 5 years old, (1125 cattle and 1000 buffloes) were examined under the authority of Albsatine and Alwaraq official abattoirs in Cairo Governorate, Egypt covering the period extended from March 2022 to April 2023. The overall prevalence of the disease among bovine was 7.8% (6.31% of cattle and 9.5% of buffloes). Besides, B. aegyptiaca and M. oleifera extracts showed cysticidal activity in experimentally infected mice. A decrease in the numbers of cysticerci was found in all treated mice groups, and up to 88% reduction was achieved in the B. aegyptiaca-treated group;higher than that was recorded in both M. oleifera (72.23%) and albendazole-treated ones (80.56%). Postmortem findings proved that M. oleifera and B. aegyptiaca reduced cysticerci numbers comparable to a commercial anthelmintic. The study showed a significant decrease (P 0.001) in TNF-α levels after treatment with Balanites and Moringa extracts, compared with the untreated control and the albendazole-treated groups.

关键词： Prevalence Balanites aegyptiaca Moringa oleifera Mice T. saginata Cysticerci

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Deep Learning for Depression Detection Using Twitter Data

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Intelligent Automation & Soft Computing 2023年第5期36卷 1301-1313页

作者： doaa Sami Khafaga Maheshwari Auvdaiappan KDeepa mohamed Abouhawwash Faten Khalid Karim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Today social media became a communication line among people to share their happiness,sadness,and anger with their end-users.It is necessary to know people’s emotions are very important to identify depressed people from their messages.Early depression detection helps to save people’s lives and other dangerous mental diseases.There are many intelligent algorithms for predicting depression with high accuracy,but they lack the deﬁnition of such cases.Several machine learning methods help to identify depressed people.But the accuracy of existing methods was not satisfactory.To overcome this issue,the deep learning method is used in the proposed method for depression detection.In this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression data.Initially,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and lemmatization.The Adaptive Particle and grey Wolf optimization methods are used for feature selection.The MDHAN classiﬁes the Twitter data and predicts the depressed and non-depressed users.Finally,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and MDHAN.The suggested MDH-PWO architecture gains 99.86%accuracy,more signiﬁcant than frequency-based deep learning models,with a lower false-positive rate.The experimental result shows that the proposed method achieves better accuracy,precision,recall,and F1-measure.It also mini-mizes the execution time.

关键词： Depression detection twitter data tweets deep learning swarm intelligence multi-aspect depression detection prediction

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An Improved Deep Structure for Accurately Brain Tumor Recognition

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Computer Systems Science & Engineering 2023年第8期46卷 1597-1616页

作者： mohamed Maher Ata Reem N.Yousef Faten Khalid Karim doaa Sami Khafaga Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Electronics and Communications Delta Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain surgery.By using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive procedure.This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure.The proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor images.Moreover,a deep neural network(DNN)model has been proposed to select the most salient features from the GLCM.Moreover,it manipulates the extraction of the additional high levels of salient features from a proposed CNN model.Finally,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process.Two common datasets have been applied and tested,Br35H and FigShare datasets.The first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no cancer.Moreover,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training time.Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies.The proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

关键词： Brain tumor convolutional neural network gray level co-occurrence matrix noninvasive FigShare dataset Br35H dataset

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Wireless Network Security Using Load Balanced Mobile Sink Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2135-2149页

作者： Reem Alkanhel mohamed Abouhawwash S.N.Sangeethaa K.Venkatachalam doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent surroundings.Among the most signiﬁcant resources in the WSN are battery power and security.Clustering stra-tegies improve the power factor and secure the WSN environment.It takes more electricity to forward data in a WSN.Though numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less security.These possibilities arise due to the cluster head’s limited life span.These cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster interactions.The proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of CH.In addition,the LCLBM was added with an assistant cluster head(ACH)for load balancing.Power consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating LCLBM.CEOMS will choose a cluster leader based on the inﬂuence of the fol-lowing parameters on the energy balance of WSNs.According to simulatedﬁnd-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

关键词： Wireless sensor network load balancing mechanism optimization power consumption network’s lifetime

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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam mohamed Abouhawwash doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identiﬁcation as an access authority into these applications.Still,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spooﬁng detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

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Computers, Materials & Continua 2022年第11期73卷 2371-2387页

作者： Hussah Nasser AlEisa El-Sayed M.El-kenawy Amel Ali Alhussan mohamed Saber Abdelaziz A.Abdelhamid doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.

关键词： Metaheuristic pneumonia dipper throated optimization KNN

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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1571-1583页

作者： Reem Alkanhel Kalaiselvi Chinnathambi C.Thilagavathi mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efﬁcient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difﬁcult areas of study.As every sensor node has aﬁnite amount of energy.Battery power is the most signiﬁcant source in the WSN.Clustering is a well-known technique for enhan-cing the power feature in WSN.In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing opti-mization.By using distributed data transmission modiﬁcation,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area.To begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption.The Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efﬁcient Cluster Head(CH).It also improves the network’s longevity and optimizes the routing.As a result of the study’sﬁndings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

关键词： Clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering routing cluster head

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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Computer Systems Science & Engineering 2023年第5期45卷 1469-1482页

作者： doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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