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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 *** diagnosing pneumonia from chest X-ray images,many deep learning models have been put *** goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia *** 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 *** 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 *** guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO *** test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and ***,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 *** 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

Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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Computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

The optimum conditions for methanol conversion to dimethyl ether over modified sulfated zirconia catalysts prepared by different methods

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燃料化学学报 2021年第1期49卷 63-71页

作者： doaa S.EL-DESOUKI Amina H.IBRAHIM Samira M.ABDELAZIM Noha A.K.ABOUL-GHEIT Dalia R.ABDEL-HAFIZAR Processes Development Department EPRICairo 11727Egypt Refining Department EPRICairo 11727Egypt

Sulfated zirconia(SZ)and two promoted 1% Mn/SZ catalysts which have been prepared via sol gel(Mn/SZ-S)and impregnation(Mn/SZ-I)methods were *** morphology of the catalysts was characterized by XRD,BET,NH3-TPD,ICP,SEM and FT-IR *** conversion of methanol to dimethyl ether and hydrocarbons was carried out in the temperature range of 120−300℃.The Mn/SZ-S showed the highest activity due to the high surface area with suitable *** optimum condition of Mn/SZ-S catalyst was investigated at 200℃ and LHSV of 0.02 h^−1 in a time range from 30 to 210 *** was found that the total conversion decreased from 80.18% to 53.26% at 210 *** reusability of this catalyst was studied at the optimum condition up till four cycles for 1 *** characterization of the reused catalyst showed a significant change in the structure and surface acidity due to the blockage of the surface acid sited by carbonaceous materials.

关键词： catalysts dehydration DME sulfate content sulfated zirconia

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

Comparative metabolomics of the different fractions of two saltwort(Salsola L.)species in relation to their anti-inflammatory activity

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Food Bioscience 2023年第1期51卷 937-949页

作者： Dina A.Selim Eman Shawky doaa A.Ghareeb Shaymaa A.Abdulmalek Rasha M.Abu El-Khair Department of Pharmacognosy Faculty of PharmacyAlexandria UniversityEgypt Bio-screening and Preclinical Trial Lab Biochemistry DepartmentFaculty of ScienceAlexandria UniversityAlexandriaEgypt Biochemistry Department Faculty of ScienceAlexandria UniversityAlexandriaEgypt Center of Excellence for Drug Preclinical Studies(CE-DPS) Pharmaceutical and Fermentation Industry Development CenterCity of Scientific Research&Technological Applications New Borg El ArabAlexandriaEgypt College of PharmacyArab Academy for ScienceTechnology and Maritime TransportAlexandriaEgypt

Many of the plants of genus Salsola are edible halophytes which are known for their use in treatment of inflammation as well as their nutritional *** study aims at comprehensive chemical profiling of two Salsola species that are widely distributed in arid areas;*** and ***,using UHPLC-QqQ-MS in an attempt to decipher the bioactive constituents of the aerial parts of the selected plants and their ex-vivo antiinflammatory activity.73 chromatographic peaks were annotated where phytosterols dominated the metabolites in *** while phenylalkylamines were the major constituents detected in ***-DA model coefficients plot indicated that tetradecanoic acid,beta sitosterol and pentacosane were positively correlated to discrimination of *** species while N-feruloyloctopamine,N-feruloyltyramine,and ferulic acid,were positively correlated to discrimination of *** ***,fractions of ***,reduced the 5 upregulation of TNF-αcaused by LPS to levels lower than those produced by piroxicam while the petroleum ether and n-butanol fraction of *** were more effective than piroxicam in reducing IL-6 gene *** coefficients plot depicted that methyl palmitate,aegicin,cleomiscoside,beta sitosterol and sitostanol possessed strong positive correlation to the down regulation of the pro-inflammatory marker TNF-α,IL-1βand IFN-γwhile only norepinephrine derivatives were the major metabolites that showed strong positive correlation to the inhibition of IL-6 pro-inflammatory *** results obtained may help to explain chemically,the potential anti-inflammatory effect of the various Salsola species fractions increasing the value of the Salsola species as a putative functional foods.

关键词： Salsola jordanicola Salsola imbricata Chemometrics Anti-inflammatory UPLC/MS/MS Metabolomics

Altered gene expression in human brain microvascular endothelial cells in response to the infection of influenza H1N1 virus

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Animal Diseases 2023年第1期3卷 1-20页

作者： doaa Higazy Xianwu Lin Tanghui Xie Ke Wang Xiaochen Gao Min Cui Microbiology Department Faculty of AgricultureCairo UniversityGiza 12613Egypt Key Laboratory of Preventive Veterinary Medicine in Hubei Province The Cooperative Innovation Center for Sustainable Pig ProductionWuhan 430070HubeiChina Key Laboratory of Development of Veterinary Diagnostic Products Ministry of Agriculture of the People’s Republic of ChinaWuhan 430070HubeiChina International Research Center for Animal Disease Ministry of Science and Technology of the People’s Republic of ChinaWuhan 430070HubeiChina State Key Laboratory of Agricultural Microbiology College of Veterinary MedicineHuazhong Agricultural UniversityNo.1 Shizishan St.Huazhong Agricultural UniversityWuhan 430070HubeiChina College of Informatics Huazhong Agricultural UniversityWuhan 430070HubeiChina

Influenza viruses not only cause respiratory illness,but also have been reported to elicit neurological manifestations following acute viral *** central nervous system(CNS)has a specific defense mechanism against pathogens structured by cerebral microvasculature lined with brain endothelial cells to form the blood–brain barrier(BBB).To investigate the response of human brain microvascular endothelial cells(hBMECs)to the Influenza A virus(IAV),we inoculated the cells with the A/WSN/33(H1N1)*** then conducted an RNAseq experiment to determine the changes in gene expression levels and the activated disease pathways following *** analysis revealed an effective activation of the innate immune defense by inducing the pattern recognition receptors(PRRs).Along with the production of proinflammatory cytokines,we detected an upregulation of interferons and interferon-stimulated genes,such as IFN-β/λ,ISG15,CXCL11,CXCL3 and IL-6,***,infected hBMECs exhibited a disruption in the cytoskeletal structure both on the transcriptomic and cytological *** RNAseq analysis showed different pathways and candidate genes associated with the neuroactive ligand-receptor interaction,neuroinflammation,and neurodegenerative diseases,together with a predicted activation of the ***,some genes linked with the mitochondrial structure and function displayed a significantly altered *** masse,this data supports that hBMECs could be infected by the IAV,which induces the innate and inflammatory immune *** results suggest that the influenza virus infection could potentially induce a subsequent aggravation of neurological disorders.

关键词： Blood-brain barrier Influenza A virus(IAV) hBMECs CNS Neurodegenerative diseases RNAseq

Changing dietary n-6:n-3 ratio using different oil sources affects performance,behavior,cytokines mRNA expression and meat fatty acid profile of broiler chickens

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Animal Nutrition 2018年第1期4卷 -页

作者： doaa Ibrahim Rania El-Sayed Safaa I.Khater Enas N.Said Shefaa A.M.El-Mandrawy Department of Nutrition and Clinical Nutrition Faculty of Veterinary MedicineZagazig University Department of Biochemistry Faculty of Veterinary MedicineZagazig University Department of Veterinary Public Health Faculty of Veterinary MedicineZagazig University Department of Clinical Pathology Faculty of Veterinary MedicineZagazig University

Typical formulated broiler diets are deficient in n-3 poly-unsaturated fatty acids(PUFA) due to widening n-6:n-3 PUFA ratio which could greatly affect performance,immune system of birds and,more importantly,meat *** study was conducted to evaluate the effect of modifying dietary n-6:n-3 PUFA ratio from plant and animal oil sources on performance,behavior,cytokine m RNA expression,antioxidative status and meat fatty acid profile of broiler ***(n=420) were fed 7 diets enriched with different dietary oil sources and ratios as follows: sunflower oil in control diet(C); fish oil(FO); 1:1 ratio of sunflower oil to FO(C1 FO1); 3:1 ratio of sunflower oil to fish oil(C3 FO1); linseed oil(LO); 1:1 ratio of sunflower oil to linseed oil(C1 LO1); 3:1 ratio of sunflower oil to linseed oil(C3 LO1),resulting in dietary n-6:n-3 ratios of approximately 40:1,1.5:1,4:1,8:1,1:1,2.5:1 and 5:1,*** best final body weight,feed conversion ratio as well as protein efficiency ratio of broilers were recorded in the C1 FO1 and C1 LO1 *** with the control group,the dressing percentage and breast and thigh yield were highest in the C1 FO1 and C1 LO1 *** the dietary n-6:n-3 ratio increased(P < 0.05) n-3 PUFA content of breast ***,the breast meat contents of eicosapentaenoic acid and docosahexaenoic acid increased(P < 0.05) with increasing dietary FO whereas a-linolenic acid content was higher with LO ***,enriching the diets with n-3 PUFA from FO and LO clearly decreased(P < 0.05) serum total cholesterol,triglycerides and very low-density lipoproteins and enhanced antioxidative *** feeding frequency was decreased(P < 0.05) in the C1 FO1 and C1 LO1 ***,n-3 PUFA-enriched diets enhanced the frequency of preening,wing flapping and *** oil source addition,compared to plant oil,to broiler diets enhanced the relative m RNA expression of interferon gamma,interleukin-1 beta,interleukin-2 and

关键词： Broiler n-6:n-3 PUFA ratio Performance Antioxidant status Meat Immunity

Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing

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Journal of Computer and Communications 2023年第10期11卷 120-134页

作者： doaa E. Elmatary doaa M. Maher Areeg Tarek Ibrahim Department of Communications and Computers AlSafwa High Institute of Engineering Qalyubia Egypt

A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.

关键词： Hand Gesture Translator Sign Multi-Language Machine Learning Models Deaf-Mute Community

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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Computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture