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Urban electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

Computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel ali Alhussan Doaa Sami Khafaga el-Sayed M.el-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Evaluating germinability of eight desert halophytes under long-term seed storage: Implications for conservation

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Plant Diversity 2019年第4期41卷 229-236页

作者： Sanjay Gairola Hatem A. Shabana Tamer Mahmoud ali el-Keblawy Andrea Santo Sharjah Seed Bank and Herbarium Sharjah Research AcademyP.O.Box 60999SharjahUnited Arab Emirates Department of Applied Biology College of SciencesUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Independent Researcher SelargiusCagliariItaly

Ex situ conservation in seed banks is a potential complementary conservation strategy for native plant *** is well established that ex situ seed banking of native wild plants prolongs seed viability and thereby preserves genetic and species diversity for future *** evaluated ex situ storage potential of eight halophytic species from deserts in the United Arab Emirates(UAE)by studying seed ***,we examined the germinability of freshly collected seeds and seeds stored for three years in a seed *** also examined the effect of light conditions on fresh and stored seed *** seeds of seven of the eight species tested had a higher germination rates under 12/12 h light/dark fluctuations than did those exposed to total *** reduced light sensitivity in Halocnemum strobilaceum,Suaeda aegyptiaca,Salsola drummondii and Salsola imbricata,but increased the requirement for light in Arthrocnemum *** Anabasis setifera,storage decreased germination percentage when there was a 12-hour light/dark fluctuation,but increased germination rate when exposed to the dark *** significantly reduced germination in both the light/dark and dark treatments in Suaeda vermiculata and *** speed also responded differently to storage;whereas Timson's index significantly increased in *** and ***,it significantly decreased for ***,*** and *** of these species at a range of temperatures requires further testing;additionally,we strongly suggest that these laboratory findings be complemented by field studies.

关键词： Ex situ conservation Halophytes Seed bank collection Seed germination Salt tolerance SSBH

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Holocene variations of wildfire occurrence as a guide for sustainable management of the northeastern Canadian boreal forest

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Forest Ecosystems 2015年第3期2卷 190-196页

作者： Ahmed el-Guellab Hugo Asselin Sylvie Gauthier Yves Bergeron Adam A.ali NSERC/UQAT/UQàM Industrial Chair in Sustainable Forest Management Université du Québec en Abitibi-Témiscamingue Natural Resources Canada Canadian Forest Service – Laurentian Forestry Centre Centre for Bio-Archeology and Ecology (UMR5059 CNRS) Université Montpellier 2

Background: Cumulative impacts of wildfires and forest harvesting can cause shifts from closed-crown forest to open woodland in boreal ecosystems. To lower the probability of occurrence of such catastrophic regime shifts,forest logging must decrease when fire frequency increases, so that the combined disturbance rate does not exceed the Holocene maximum. Knowing how climate warming will affect fire regimes is thus crucial to sustainably manage the forest. This study aimed to provide a guide to determine sustainable forest harvesting levels, by reconstructing the Holocene fire history at the northern limit of commercial forestry in Quebec using charcoal particles preserved in lake ***: Sediment cores were sampled from four lakes located close to the northern limit of commercial forestry in Quebec. The cores were sliced into consecutive 0.5 cm thick subsamples from which 1 cm3 was extracted to count and measure charcoal particles larger than 150 microns. Age-depth models were obtained for each core based on accelerator mass spectroscopy(AMS) radiocarbon dates. Holocene fire histories were reconstructed by combining charcoal counts and age-depth models to obtain charcoal accumulation rates and, after statistical treatment,long-term trends in fire occurrence(expressed as number of fires per 1000 years).Results: Fire occurrence varied between the four studied sites, but fires generally occurred more often during warm and dry periods of the Holocene, especially during the Holocene Thermal Maximum(7000–3500 cal. BP), when fire occurrence was twice as high as at ***: The current fire regime in the study area is still within the natural range of variability observed over the Holocene. However, climatic conditions comparable to the Holocene Thermal Maximum could be reached within the next few decades, thus substantially reducing the amount of wood available to the forest industry.

关键词： Fire occurrence Holocene Boreal forest Northern li

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Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models

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Computers, Materials & Continua 2022年第1期70卷 1141-1157页

作者： Walid el-Shafai Anas M.ali el-Sayed M.el-Rabaie Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

In developing countries,medical diagnosis is expensive and time ***,automatic diagnosis can be a good cheap *** task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of *** deep CNNs allow direct learning from the medical ***,the accessibility of classified data is still the largest challenge,particularly in the field of medical *** learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image ***,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust ***-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image ***,it helps in showing the main features of *** by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT *** depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction ***,Transfer learning with CNN(TCNN)is adopted for the classification of *** different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.

关键词： Medical images SIGTra GAN CT and X-ray images SISR TCNN

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Tailored pancreatic reconstruction after pancreaticoduodenectomy: a single-center experience of 892 cases

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Hepatobiliary & Pancreatic Diseases International 2017年第5期16卷 528-536页

作者： Ayman el Nakeeb Ahmad M Sultan Ehab Atef ali Salem Mostaffa Abu Zeid Ahmed Abu el Eneen Gamal el Ebidy Mohamed Abdel Wahab Gastroenterology Surgical Center Mansoura UniversityMansoura 35516 Egypt

BACKGROUND: Pancreatic reconstruction following pancreaticoduodenectomy(PD) is still debatable even for pancreatic surgeons. Ideally, pancreatic reconstruction after PD should reduce the risk of postoperative pancreatic fistula(POPF) and its severity if developed with preservation of both exocrine and endocrine pancreatic functions. It must be tailored to control the morbidity linked to the type of *** study was to show the best type of pancreatic reconstruction according to the characters of pancreatic stump. METHODS: We studied all patients who underwent PD in our center from January 1993 to December 2015. Patients were categorized into three groups depending on the presence of risk factors of postoperative complications: low-risk group(absent risk factor), moderate-risk group(presence of one risk factor) and high-risk group(presence of two or more risk factors). RESULTS: A total of 892 patients underwent PD for resection of periampullary tumor. BMI >25 kg/m~2, cirrhotic liver, soft pancreas, pancreatic duct diameter elopment of postoperative complications. POPF developed in 128(14.3%) patients. Delayed gastric emptying occurred in 164(18.4%) patients, biliary leakage developed in 65(7.3%) and pancreatitis presented in 20(2.2%). POPF in low-, moderate-and high-risk groups were 26(8.3%), 65(15.7%) and 37(22.7%) patients, respectively. Postoperative morbidity and mortality were significantly lower with pancreaticogastrostomy(PG) in high-risk group, while pancreaticojejunostomy(PJ) decreases incidence of postoperative steatorrhea in all groups. CONCLUSIONS: Selection of proper pancreatic reconstruction according to the risk factors of patients may reduce POPF and postoperative complications and mortality. PG is superior to PJ as regards short-term outcomes in high-risk group but PJ provides better pancreatic function in all groups and therefore, PJ is superior in l

关键词： periampullary tumor pancreaticogastrostomy pancreaticojejunostomy pancreaticoduodenectomy postoperative pancreatic fistula

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Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

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Computers, Materials & Continua 2022年第3期70卷 6107-6125页

作者： Walid el-Shafai Samy Abd el-Nabi el-Sayed Mel-Rabaie Anas M.ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd el-Samie Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis

关键词： Medical images CADTra AD CT and X-ray images autoencoder

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Exact Solution of Space-Time Fractional Coupled EW and Coupled MEW Equations Using Modified Kudryashov Method

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Communications in Theoretical Physics 2017年第7期67卷 49-56页

作者： K. R. Raslan Talaat S. el-Danaf Khalid K. ali Mathematics Department Faculty of Science Al-Azhar University Mathematics Department Faculty of Science Ai-Azhar University Nasr City 11884 Cairo Egypt Faculty of Science Menoufia University Shebein E1-Koom Egypt

In the present paper, we established a traveling wave solution by using modified Kudryashov method for the space-time fractional nonlinear partial differential equations. The method is used to obtain the exact solutions for different types of the space-time fractional nonlinear partial differential equations such as, the space-time fractional coupled equal width wave equation(CEWE) and the space-time fractional coupled modified equal width wave equation(CMEW), which are the important soliton equations. Both equations are reduced to ordinary differential equations by the use of fractional complex transform and properties of modified Riemann–Liouville derivative. We plot the exact solutions for these equations at different time levels.

关键词： fractional coupled EW fractional coupled MEW equations modified Kudryashov method

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Outcomes of pancreaticoduodenectomy in elderly patients

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Hepatobiliary & Pancreatic Diseases International 2016年第4期15卷 419-427页

作者： Ayman el Nakeeb Ehab Atef Ehab el Hanafy ali Salem Waleed Askar Helmy Ezzat Ahmed Shehta Mohamed Abdel Wahab Gastroenterology Surgical Center Mansoura University

BACKGROUND： Although the mortality and morbidity of pancreaticoduodenectomy（PD） have improved significantly over the past years, the concerns for elderly patients undergoing PD are still present. Furthermore, the frequency of PD is increasing because of the increasing proportion of elderly patients and the increasing incidence of periampullary tumors. This study aimed to analyze the outcomes of PD in elderly ***： We studied all patients who had undergone PD in our center between January 1995 and February 2015. The patients were divided into three groups based on age： group I（patients aged 〈60 years）, group II（those aged 60 to 69 years） and group III（those aged ≥70 years）. The primary outcome was the rate of total postoperative complications. Secondary endpoint included total operative time, hospital mortality, length of postoperative hospital stay, delayed gastric emptying, re-exploration, and survival ***： A total of 828 patients who had undergone PD for resection of periampullary tumor were included in this study. There were 579（69.9%） patients in group I, 201（24.3%） in group II, and 48（5.8%） in group III. The overall incidence of complications was higher in elderly patients（25.9% in group I, 36.8% in group II, and 37.5% in group III; P=0.006）. There were more patients complicated with delayed gastric emptying in group II compared with the other two groups. There was no significant difference in the incidence of postoperative pancreatic fistula, biliary leakage, pancreatitis, pulmonary complications and hospital ***： PD can be performed safely in selected elderly patients. Advanced age alone should not be a contraindication for PD. The outcome of elderly patients who have undergone PD is similar to that of younger patients, and the increased rate of complications is due to the presence of associated comorbidities.

关键词： pancreaticoduodenectomy elderly pancreatic fistula delayed gastric emptying

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Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2117-2132页

作者： Doaa Sami Khafaga el-Sayed M.el-kenawy Amel ali Alhussan Marwa M.Eid Department of Computer Sciences 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 TechnologyMansouraEgypt

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and ***,the accurate prediction can be realized using the recent advances in machine learning and predictive *** research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)*** proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting *** optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic *** prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed *** results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other *** addition,statistical experiments are conducted to prove the statistical difference of the proposed *** results of these tests confirmed the expected outcomes.

关键词： Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models

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Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

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Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid el-Shafai Randa ali Ahmed Sedik Taha el-Sayed Taha Mohammed Abd-elnaby Fathi E.Abd el-Samie Department electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart ***-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare *** AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to *** are several modalities of imaging performed on the brain for the pur-pose of tumor *** paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)*** proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal *** dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:*** main contribution of this paper is introdu-cing an optimum deep learning structure of CNN *** simulation results are obtained for 50 epochs in the training *** simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

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