|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Hepatitis B virus genotyping: current methods and clinical implications显示文摘 | Bassem S.S. Guirgis Radwa O. Abbas Hassan M.E. Azzazy | 2010 | International Journal of Infectious Diseases2010，，11： | 2 |
| 2 | Effects of Nigella sativa on outcome of hepatitis C in Egypt显示文摘AIM: To evaluate the safety, efficacy and tolerability of Nigella sativa (N. sativa ) in patients with hepatitis C not eligible for interferon (IFN)-α. METHODS: Thirty patients with hepatitis C virus (HCV) infection, who were not eligible for IFN/ribavirin therapy, were included in the present study. Inclusion criteria included: patients with HCV with or without cirrhosis, who had a contraindication to IFN-α therapy, or had refused or had a financial constraint to IFN-α therapy. Exclusion criteria included: patients on IFN-α therapy, infection with hepatitis B or hepatitis Ⅰ virus, hepatocellular carcinoma, other malignancies, major severe illness, or treatment non-compliance. Various parameters, including clinical parameters, complete blood count, liver function, renal function, plasma glucose, total antioxidant capacity (TAC), and polymerase chain reaction, were all assessed at baseline and at the end of the study. Clinical assessment included: hepato and/ or splenomegaly, jaundice, palmar erythema, flapping tremors, spider naevi, lower-limb edema, and ascites. N. sativa was administered for three successive months at a dose of (450 mg three times daily). Clinical response and incidence of adverse drug reactions were assessed initially, periodically, and at the end of the study. RESULTS: N. sativa administration significantly improved HCV viral load (380808.7 ± 610937 vs 147028.2 ± 475225.6, P = 0.001) and TAC (1.35 ± 0.5 vs 1.612 ± 0.56, P = 0.001). After N. sativa administration, the following laboratory parameters improved: total protein (7.1 ± 0.7 vs 7.5 ± 0.8, P = 0.001), albumin (3.5 ± 0.87 vs 3.69 ± 0.91, P = 0.008), red blood cell count (4.13 ± 0.9 vs 4.3 ± 0.9, P = 0.001), and platelet count (167.7 ± 91.2 vs 198.5 ± 103, P = 0.004). Fasting blood glucose (104.03 ± 43.42 vs 92.1 ± 31.34, P = 0.001) and postprandial blood glucose (143.67 ± 72.56 vs 112.1 ± 42.9, P = 0.001) were significantly decreased in both diabetic and non-diabetic HCV patients. Patients with lower-limb edema decreased significantly from baseline compared with after treatment [16 (53.30%) vs 7 (23.30%), P = 0.004]. Adverse drug reactions were unremarkable except for a few cases of epigastric pain and hypoglycemia that did not affect patient compliance. CONCLUSION: N. sativa administration in patients with HCV was tolerable, safe, decreased viral load, and improved oxidative stress, clinical condition and glycemic control in diabetic patients. | Eman Mahmoud Fathy Barakat Lamia Mohamed El Wakeel Radwa Samir Hagag | 2013 | World Journal of Gastroenterology2013，19，16： | 2 |
| 3 | Cortical excitability chan-ges correlate with fluctuations in glucose levels in patientswith epilepsy显示文摘 | Radwa A?Badawy B Simon J | 2013 | Epilepsy Behav2013，27，3： | 1 |
| 4 | Electrochemical sensor based on polyaniline nanofibers/single wall carbon nanotubes composite for detection of malathion显示文摘 | Shaker Ebrahim Radwa El-Raey Ahmed Hefnawy | 2014 | Synthetic Metals2014，190，1： | 1 |
| 5 | Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images显示文摘Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches. | Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 6 | Amphibian road mortality in Europe: a meta-analysis with new data from Poland显示文摘 | Andrzej Elzanowski Joanna Ciesio?kiewicz Mirella Kaczor Joanna Radwańska Rados?aw Urban | 2009 | European Journal of Wildlife Research2009，，1： | 1 |
| 7 | Estrogen-related MxA transcriptional variation in hepatitis C virus-infected patients显示文摘 | Radwa Y. Mekky Nabila Hamdi Wafaa El-Akel Gamal Esmat Ahmed I. Abdelaziz | 2012 | Translational Research2012，，3： | 1 |
| 8 | Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment显示文摘Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public networks.Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved.The development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT environment.It is needed to minimize security issues related to IoT gadgets effectively.Therefore,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment.The presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment.For accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful format.In addition,the RELM model receives the pre-processed data and carries out the classification process.In order to boost the performance of the RELM model,the MFO algorithm has been employed to it.The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects. | Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk | 2023 | Computer Systems Science & Engineering2023，45，4： | 1 |
| 9 | Predictive prognostic role of miR?181a with discrepancy in the liverand serum of genotype 4 hepatitis C virus patients显示文摘 | Dalia Elhelw Radwa Mekky Nada El?Ekiaby Rasha Ahmed Mohammad Mohey Eldin Mohammad El?Sayed Mahmoud Abouelkhair Ayman Salah Abdel Zekri Gamal Esmat Ahmed Abdelaziz | 2014 | Biomedical Reports2014，，6： | 1 |
| 10 | Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication显示文摘With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches. | Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，6： | 1 |
| 11 | Development of Primary Percutaneous Coronary Intervention as a National Reperfusion Strategy for Patients with ST-Elevation Myocardial Infarction and Assessment of Its Use in Egypt显示文摘Objective:Early treatment of acute ischemia of the heart by performing immediate percutaneous coronary intervention(PCI)to restore blood fl ow in patients with the clinical presentation of an acute coronary syndrome and more specifi cally with ST-elevation myocardial infarction(STEMI)can save lives.This study aims to identify the mean time(door to balloon time and fi rst contact to balloon time)to primary PCI for STEMI patients and to assess the percentage of primary PCI and its success rate in Egypt.Methods:A registry study of patients presenting to cardiac centers in Egypt was designed,where patients’basic characteristics,the treatment strategy,and the door to balloon time and the fi rst contact to balloon time were assessed.Results:One thousand six hundred fi fty STEMI patients with a mean age of 57 years were included in the study.Immediate transfer for primary PCI was the most used treatment strategy,representing 74.6%of all treatment strategies used.The door to balloon time and the fi rst contact to balloon time were 50 and 60 minutes,respectively,with a primary PCI success rate of 65.1%.Conclusion:The registry study results showed a marked improvement by implementation of the best treatment strategy with respect to the time factor to achieve a better outcome for STEMI patients in Egypt. | Mohamed Sobhy Ahmed Elshal Noha Ghanem Hosam Hasan-Ali Nabil Farag Nireen Okasha El Sayed Farag Mohamed Sadaka Hisham Abo El Enein Sameh Salama Hazem Khamis Khaled Shokry Hany Ragy Amany Elshorbagy Radwa Mehanna | 2020 | Cardiovascular Innovations and Applications2020，，2： | 1 |
| 12 | Evaluation of dose area product vs. patient dose in diagnostic X-ray units显示文摘 | K. Kisielewicz A. Truszkiewicz S. Wach M. Wasilewska–Radwańska | 2010 | Physica Medica2010，，2： | 1 |
| 13 | Flavonoid chemical composition and antidiabetic potential of Brachychiton acerifolius leaves extract显示文摘Objective:To evaluate Brachychiton acerifolius leaf extracts as antidiabetic potential agent and to identify the main active constituents using bioactivity guided fractionation.Methods:In vitro antioxidant activity was evaluated for B.acerifolius different extracts using DPPH assay and vitamin C as control.Antidiabetic activity was then determined using STZ-induced rats treated daily with ethyl acetate and 70% ethanol leaf extracts for4 weeks at a dose of 200 g/kg body weight against gliclazide reference drug.Blood glucose,a-amylase,lipid profile,liver function enzymes and oxidative stress markers were assessed along with histopathological study for liver and pancreatic tissues.Isolation and structural elucidation of active compounds were made using Diaion and Sephadex followed by spectral analyses.Results:The results indicated that ethyl acetate and ethanol leaf extracts exhibited the strongest antioxidant activity compared to that of vitamin C(IC500.05,0.03 and 12 mg/m L,respectively).Both extracts showed potent anti-hyperglycemic activity evidenced by a significant decrease in serum glucose levels by 82.5% and 80.9% and a-amylase by45.2% and 53.6%,as compared with gliclazide 68% and 59.4%,respectively.Fractionation of ethanol extract resulted in the isolation of 9 flavonoids including apigenin-7-O-arhamnosyl(1/2)-b-D-glucuronide,apigenin-7-O-b-D-glucuronide,apigenin-7-O-b-Dglucoside and luteolin-7-O-b-D-glucuronide.Conclusions:This study highlights the potential use of B.acerifolius leaf extract enriched in flavones for the treatment of diabetes that would warrant further clinical trials investigation. | Aisha Hussein Abou Zeid Mohamed Ali Farag Manal Abdel Aziz Hamed Zeinab Abdel Aziz Kandil Radwa Hassan El-Akad Hanaa Mohamed El-Rafie | 2017 | Asian Pacific Journal of Tropical Biomedicine2017，7，5： | 1 |
| 14 | Lipopeptide biosurfactant pseudofactin II induced apoptosis of melanoma A 375 cells by specific interaction with the plasma membrane显示文摘 | Janek T Krasowska A Radwańska A | 2013 | PLoS One2013，8，57： | 1 |
| 15 | Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model显示文摘Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction withothers.Advanced information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern detection.Recent advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the disease.This study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches. | Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 16 | Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks显示文摘Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,etc.At the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor nodes.WSN comprises a massive number of SNs and records the position of the nodes,which becomes a tedious process.Besides,the SNs might be subjected to node mobility and the position alters with time.So,a precise node localization(NL)manner is required for determining the location of the SNs.In this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for WSN.The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes.The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season.In addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown SNs.For demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques. | Hanan Abdullah Mengash Radwa Marzouk Siwar Ben Haj Hassine Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 17 | Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis显示文摘Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%. | Abdelwahed Motwakel Najm Alotaibi Eatedal Alabdulkreem Hussain Alshahrani MohamedAhmed Elfaki Mohamed K Nour Radwa Marzouk Mahmoud Othman | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 18 | Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition显示文摘Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes.When the number of labels is limited to one,the task becomes single-label text categorization.The Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical value.In recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so on.This paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model onArabicCorpus.The presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text.To achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible format.Next,the ELM model is utilized for the representation and recognition of the Arabic text.At last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results.The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%. | Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 19 | Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment显示文摘Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques. | R.Joshua Samuel Raj V.Ilango Prince Thomas V.R.Uma Fahd N.Al-Wesabi Radwa Marzouk Anwer Mustafa Hilal | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 20 | Integration of Fog Computing for Health Record Management Using Blockchain Technology显示文摘Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000. | Mesfer AI Duhayyim Fahd N.Al-Wesabi Radwa Marzouk Abdalla Ibrahim Abdalla Musa Noha Negm Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员