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An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

Computers, materials & Continua 2021年第10期69卷 1323-1341页

作者： Walid El-Shafai Noha A.El-Hag Ghada m.El-Banby Ashraf A.m.Khalaf Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of EngineeringMinia UniversityEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the *** has dramatically affected daily lives,public health,and the world *** paper presents a segmentation and classification framework of COVID-19 images based on deep ***,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT ***,the resulting segmented images are used to identify the infected region,whether COVID-19 or *** proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and *** maxpooling layers are employed with a stride of 2 and a 2×2 *** classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT *** acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

关键词： Classification segmentation COVID-19 CNN deep learning diagnosis applications

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Optical Neural Networks:Analysis and Prospects for 5G Applications

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Computers, materials & Continua 2023年第12期77卷 3723-3740页

作者： Doaa Sami Khafaga Zongming Lv Imran Khan Shebnam m.Sefat Amel Ali Alhussan Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia School of Information and Finance Xuancheng Vocational&Technical CollegeXuancheng242000China Department of Electrical Engineering University of Engineering and TechnologyP.O.Box 814PeshawarPakistan Islamic University Centre for Scientific Research The Islamic UniversityNajafIraq Department of Computer Science Independent UniversityDhaka1208Bangladesh

With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredible advances and accomplished broad application over the final *** one of the foremost conspicuous methods for fake insights,neural systems are growing toward high computational speed and moo control *** to the inborn impediments of electronic gadgets,it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions *** neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the *** paper outlines optical neural networks of feedforward repetitive and spiking models to give a clearer picture of history,wildernesses,and future optical neural *** framework demonstrates neural systems in optic communication with the serial and parallel *** graphene-based laser structure for fiber optic communication is *** comparison of different balance plans for photonic neural systems is made within the setting of hereditary calculation and molecule swarm *** expansion,the execution comparison of routine photonic neural,time-domain with and without extending commotion is additionally *** challenges and future patterns of optical neural systems on the growing scale and applications of in situ preparing nonlinear computing will hence be uncovered.

关键词： Optoelectronics 5G networks free-space optical transducer neural networks

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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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Computer Systems Science & Engineering 2023年第4期45卷 329-342页

作者： Nada m.Elfatih Elmustafa Sayed Ali maha Abdelhaq Raed Alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection *** optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold ***,these methods do not take into account the effect of sample size and its effect on improving CoR *** general,a large sample size results in more reliable detection,but takes longer sensing time and increases ***,the locally sensed sample size is an optimization ***,optimizing the local sample size for each cognitive user helps to improve CoR *** this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)*** the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： Cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

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Intelligent Fuzzy Based High Gain Non-Isolated Converter for DC micro-Grids

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Computers, materials & Continua 2022年第5期71卷 4069-4084页

作者： m.Bharathidasan V.Indragandhi Ramya Kuppusamy Yuvaraja Teekaraman Shabana Urooj Norah Alwadi School of Electrical Engineering Vellore Institute of TechnologyVelloreIndia Department of Electrical and Electronics Engineering Sri Sairam College of EngineeringBangaloreIndia mobility Logisticsand Automotive Technology Research CentreFaculty of EngineeringETECDepartment of Electrical Engineering and Energy TechnologyVrije Universiteit BrusselBrussel1050Belgium Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Physics College of SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Renewable electricity options, such as fuel cells, solar photovoltaic,and batteries, are being integrated, which has made DC micro-grids *** DC micro-grid systems, a multi input interleaved non-isolated dc-dcconverter is suggested by the use of coupled inductor techniques. Since itcompensates for mismatches in photovoltaic devices and allows for separateand continuous power flow from these sources. The proposed converter hasthe benefits of high gain, a low ripple in the output voltage, minimal stressvoltage across the power semiconductor devices, a low ripple in inductorcurrent, high power density, and high efficiency. Soft-switching techniquesare used to realize that the reverse recovery issue of the diodes is moderated, the leakage energy is reused, and no new scheme is appropriated. Toreduce conduction losses, minimum voltage rating mOSFETs with a low ONresistance can be utilized. The converter can supply the required power fromthe load in the absence of one or two resources. Furthermore, due to the highgain of boosting voltage, the converter works in an Adaptive Neuro-FuzzyInference System (ANFIS). The operation principle, steady-state analysis ofthe proposed converter, is given and simulated utilizing mATLAB/Simulinksimulation software.

关键词： Renewable energy sources DC micro-grid multi-input converter soft-switching techniques high gain

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Estimation of metformin Drug for the Diabetes Patients by Simple,Quick and Cheap Techniques within the Formation of Colored Charge Transfer Complexes

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光谱学与光谱分析 2018年第11期38卷 3622-3630页

作者： Omar B.Ibrahim El-Sayed A.manaaa m.m.AL-majthoub Ahm ed m.Fallatah Abdel majid A.Adam mha m.Alatibi Jehan Y.A l-Humaidi moamen S.Refat Department of Chemistry Faculty of ScienceTaif UniversityAl-HaweiahP.O.Box 888Zip Code 21974TaifSaudi Arabia Department of Chemistry Faculty of Applied Medical ScienceTaif Unive rsityKSA Nuclear materials Authority El-MaadiEgypt Department of Chemistry College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh 11671KSA Department of Chemistry Faculty of SciencePort Said UniversityPort SaidEgypt

metformin(met)is a drug developed for the treatment of patients with typeⅡ ***,met estimation in pharmaceutical formulations and human fluids has gained a growing *** extend requisite data that can be used to assessment of met quantitatively based on charge-transfer(CT)complexation,the present study describes the synthesis and characterization of CT complexes that formed between drug met and the organicπ-acceptors picric acid(PA),chloranilic acid(CLA),chloranil(CHL),7,7',8,8'-tetracyanoquinodimethane(TCNQ),and dichlorodicyanobenzoquinone(DDQ).The properties of the formed CT complexes were investigated by elemental,spectral(UV-visible,IR,and Raman spectroscopies),thermal(TG)and morphological(SEm)*** results indicated that the complexation of met with either PA or CLA acceptors occurs through proton transfer interaction,whereas its complexation with CHL,TCNQ,or DDQ acceptors occurs through n→π*interaction.

关键词： metformin Anti-diabetic drug Charge-t ransfer complex Acceptor Solution study

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microwave-assisted synthesis of mg:PbI2 nanostructures and their structural,morphological,optical,dielectric and electrical properties for optoelectronic technology

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Chinese Physics B 2020年第11期29卷 420-426页

作者： mohd.Shkir Ziaul Raza Khan T Alshahrani Kamlesh VChandekar m Aslam manthrammel Ashwani Kumar S AlFaify Advanced Functional materials and Optoelectronics Laboratory(AFmOL) Department of PhysicsCollege of ScienceKing Khalid UniversityAbha 61413P.O.Box 9004Saudi Arabia Department of Physics College of ScienceUniversity of HailP.O.Box 2440HailSaudi Arabia Department of Physics College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Physics Rayat Shikshan Sanstha’sKarmaveer Bhaurao Patil CollegeVashiNavi Mumbai 400703India Department of Physics IK Gujral Punjab Technical UniversityJalandhar 144603India

This work reports the cost-effective growth of mg:PbI2 nanostructures with 0,1,2.5 and 5.0 wt.%mg doping ***,vibrational,morphological properties are analyzed using x-ray diffraction(XRD),Raman spectroscopy and scanning electron microscopy(SEm).XRD and Raman studies confirm the monophasic hexagonal system of mg:PbI2,and no additional impurity peaks are *** Scherrer formula is used to determine sizes of crystallites to be in the range of 47-52 ***/SEm e-mapping analyses confirm the incorporation of mg in PbI2 matrix and its uniform distribution throughout the *** hexagonal nanosheet-and nanoplate-like morphologies are detected in SEm images for pure and mg-doped *** optical band gap of nanostructures is obtained from Tauc’s relation to be in the range 3.0-3.25 *** and electrical properties are found in significant enhancement as mg doping in PbI2 matrix,also the conduction mechanism is discussed.

关键词： mg:PbI2 nanostructures structural properties optical band gap dielectric constant ac conductivity

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Can we suppress excessive post-surgical scar formation:A case report

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World Journal of Orthopedics 2023年第3期14卷 166-170页

作者： mir Sadat-Ali Sulaiman A Al-mousa Khalid Waleed Al-Tabash mohamed m Abotaleb Fawaz m Al-Anii Department of Orthopaedic Surgery Imam Abdulrahman Bin Faisal UniversityDammam 31142Saudi Arabia

BACKGROUND Hypertrophic scars(HSs)formation is a complication that occurs after wounds heal with secondary intention and sometimes after clean surgical *** treatments are in vogue now with varying *** the mechanism or mechanisms that cause a HS to form are not clearly understood,one thing that is clear is that once scar tissue matures,any intervention will not be *** this paper,we report on a case where a patient who was known to develop HS was treated with a new combination of ingredients(Phyto-chemicals+Silicone JUmI)to suppress HS *** SUmmARY A 68-year-old female of African descent presented a severe HS post total knee replacement(TKR),which the patient describes as itchy and *** to complications caused by the scar,she was apprehensive about undergoing TKR on her other ***,after the TKR of the contralateral side post-removal of skin clips,JUmI anti-scar cream(JASC)was used to suppress excessive scar *** JASC appears potent and efficacious at suppressing excessive scar *** believe that this warrants further studies on larger patient groups and on different surgical sites.

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meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed m.El-kenawy Ali E.Takieldeen Tarek m.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid 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 Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： Ibrahim m.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled m.Alalayah Amira Sayed A.Aziz Khadija m.Alaidarous Ibrahim Abdulrab Ahmed manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed m.El-Kenawy Ghada Atteia mona m.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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