|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Laparoscopic excision of rectovaginal endometriosis:report of a prospective study and review of literature显示文摘 | Maytham G Dowson H Levv B | 2009 | Colorectal Dis2009，3，： | 1 |
| 2 | Laparoscopic excision of rectovaginal endometriosis: report of a prospective study and review of the literature 显示文摘 | Maytham GD Dowson HM Levy B | 2010 | Colorectal Dis2010，12，11： | 1 |
| 3 | Laparoscopic excision of rectovaginal endometriosis:report of a prospective study and review of literature显示文摘 | Maytham G Dowson H Levv B | | 0，，03： | 1 |
| 4 | Clinical outcomes of single versus staged hybrid repair for thoracoabdominal aortic aneurysm显示文摘 | Ludovic Canaud Alan Karthikesalingam Dan Jackson Lynne Cresswell Michael Cliff S. Sheraz Markar Gary Maytham Steven Black Matt Thompson | 2013 | Journal of Vascular Surgery2013，，： | 1 |
| 5 | Genetic Biodiversity in Buffalo Population of Iraq Using Microsatellites Markers显示文摘 | Talib Ahmed Jaayid Maytham Abdulkadhim Dragh | 2013 | Journal of Agricultural Science and Technology(A)2013，3，4： | 1 |
| 6 | PECA:Power efficient clustering algorithm for wireless sensor networks显示文摘 | Safar Maytham Al-Hamadi Hasan Ebrahimi Dariush | | 0，，1： | 1 |
| 7 | Laparoscopic excision of rectovaginal endometrio sis: report of a prospective study and review of the literature显示文摘 | MAYTHAM GD DOWSON HM LEVY B | 2010 | Colorectal Disease2010，12，11： | 1 |
| 8 | Laparoscopic excision of reetovaginal endometriosis:report of a prospective study and review of the literature显示文摘 | Maytham GD Dowson HM Levy B | 2010 | Colorectal Dis2010，12，11： | 1 |
| 9 | Natural Language Inference for Ara- bic Using Extended Tree Edit Distance with Subtrees 显示文摘 | Maytham A Allan R | 2013 | Journal of Artificial Intelligence Research2013，48，5： | 1 |
| 10 | Laparoscopic excision of rectovaginal endometriosis:report of a prospective study and review of the literature显示文摘 | Maytham GD Dowson HM Levy B | 2010 | Colorectal Dis2010，12，11： | 1 |
| 11 | Two-Tier Clustering with Routing Protocol for IoT Assisted WSN显示文摘In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking applications.Wireless Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based applications.Clustering methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor nodes.Clustering and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such problems.The current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT networks.The presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and routing.In first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is used.Subsequently,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the network.Besides,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the network.In order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct measures.The experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures. | AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 12 | Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment显示文摘Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics. | R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 13 | Implications of the COVID-19 pandemic on athletes,sports events,and mass gathering events:Review and recommendations显示文摘Since the coronavirus disease 19(COVID-19),which caused several respiratory diseases,was formally declared a global pandemic by the World Health Organization(WHO)on March 11,2020,it affected the lifestyle and health of athletes,both directly through cardiorespiratory and other health related effects,and indirectly as the pandemic has forced the suspension,postponement,or cancellation of most professional sporting events around the world.In this review,we explore the journey of athletes throughout the pandemic and during their return to their competitive routine.We also highlight potential pitfalls during the process and summarize the recommendations for the optimal return to sport participation.We further discuss the impact of the pandemic on the psychology of athletes,the variance between the team and individual athletes,and their ability to cope with the changes.Moreover,we specifically reviewed the pandemic impact on younger professional athletes in terms of mental and fitness health.Finally,we shaded light on the various impacts of mass gathering events and recommendations for managing upcoming events. | Jehad Feras AlSamhori Mohammad Ali Alshrouf Abdel Rahman Feras AlSamhori Fatimah Maytham Alshadeedi Anas Salahaldeen Madi Osama Alzoubi | 2023 | Sports Medicine and Health Science2023，5，3： | 0 |
| 14 | Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework显示文摘In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963. | Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，12： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员