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Multi-scale characterizations of colon polyps via computed tomographic colonography

Visual Computing for Industry,Biomedicine,and Art 2019年第1期2卷 245-256页

作者： Weiguo Cao Marc J.Pomeroy Yongfeng Gao Matthew A.Barish almas f.abbasi Perry J.Pickhardt Zhengrong Liang The Department of Radiology Stony Brook UniversityStony BrookNY 11794USA The Departments of Radiology and Biomedical Engineering Stony Brook UniversityStony BrookNY 11794USA The Department of Radiology School of MedicineUniversity of WisconsinMadisonWI 53792USA

Texture features have played an essential role in the field of medical imaging for computer-aided *** gray-level co-occurrence matrix(GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these *** study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture *** this study,we first introduce a new parameter-stride,to explore the definition of *** we propose three multi-scaling GLCM models according to its three parameters,(1)learning model by multiple displacements,(2)learning model by multiple strides(LMS),and(3)learning model by multiple *** models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick *** further analyze the three parameters,we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign ***,the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning *** obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

关键词： Colon cancer Computed tomographic colonography Polyp characterization Texture feature

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Merging bound states in the continuum in an open acoustic resonator

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 124-131页

作者： Lujun Huang Bin Jia Artem S.Pilipchuk Sibo Huang Chen Shen almas f.Sadreev Yong Li Andrey E.Miroshnichenko School of Physics and Electronic Sciences East China Normal University Institute of Acoustics Tongji University L.V.Kirensky Institute of Physics Department of Mechanical Engineering Rowan University School of Engineering and Technology University of New South Wales at Canberra

Bound states in the continuum （BICs） are perfectly localized resonances despite embedding in the continuum ***,an isolated BIC is very sensitive to the structure ***,we report merging acoustic BICs in a single open resonator,robust against the structure *** find that both symmetry-protected BIC and friedrich-Wintgen BIC are sustained in a single coupled waveguide-resonator *** varying the height and length of the resonator,these two BICs move toward each other and merge into a single one at a critical *** to an individual BIC,the merged BIC is robust against fabrication error because its Q-factor is proportional to ΔL-4,whereΔL embodies the structure *** essence of this extraordinary phenomenon is perfectly explained by the two-and three-level approximations of the effective nonHermitian ***,we present direct experimental demonstrations of the moving and merging of BICs in a coupled 3D waveguide-resonator,which are evidenced by the vanishing of the linewidth of fano resonance in the transmission *** results may find exciting applications in designing high-quality acoustic sources,sensors and filters.

关键词： acoustic resonances bound state in the continuum high-Q resonances

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A Mobile Cloud-Based eHealth Scheme

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Computers, Materials & Continua 2020年第4期63卷 31-39页

作者： Yihe Liu Aaqif Afzaal abbasi Atefeh Aghaei almas abbasi Amir Mosavi Shahaboddin Shamshirband Mohammed A.A.Al-qaness Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AINeijiang Normal UniversityNeijiang641100China Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Communication Faculty of Social SciencesUniversity of TehranTehran***Iran Department of Computer Science International Islamic UniversityIslamabad46000Pakistan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Kalman Kando faculty of Electrical Engineering Obuda UniversityBudapest1034Hungary School of the Built Environment Oxford Brookes UniversityOxfordOX30BPUK Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam School of Computer Science Wuhan UniversityWuhan430072China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid ***,the field of health informatics is also considered as an extremely important *** work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing *** developed system has two front ends,the first dedicated for the user to perform the photographing of the trace *** the photographing is complete,mobile computing is used to extract the *** the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the *** this is done,the second interface,intended for the use of the physician,can download and view the trace from the *** data is securely held using a password-based authentication *** system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

关键词： Cloud computing electrocardiograms health-care signal analysis signal processing

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Optimal cost and feasible design for gridconnected microgrid on campus area using the robust-intelligence method

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Clean Energy 2022年第1期6卷 59-76页

作者： Mohamad almas Prakasa Subiyanto Subiyanto Department of Electrical Engineering Faculty of EngineeringUniversitas Negeri SemarangSemarang 50229Indonesia

In this paper,a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum *** campus microgrid needs to be optimized with further investigation,especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply.A modified combination of genetic algorithm and particle swarm optimization(MGAPSO)is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel ***,a sustainable energy-management system is also defined to analyse the characteristics of the *** optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional *** MGAPSO optimization reduces the system cost by up to 11.99%compared with the conventional *** the rest of the paper,the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation *** investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

关键词： distributed energy and smart grid renewable-energy system optimization MGAPSO algorithm

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Application of the fictitious Domain Method for Navier-Stokes Equations

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Computers, Materials & Continua 2022年第10期73卷 2035-2055页

作者： almas Temirbekov Zhadra Zhaksylykova Yerzhan Malgazhdarov Syrym Kasenov Al-farabi Kazakh National University AlmatyKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan Sarsen Amanzholov East Kazakhstan University Ust-KamenogorskKazakhstan

To apply the fictitious domain method and conduct numericalexperiments, a boundary value problem for an ordinary differential equation is considered. The results of numerical calculations for different valuesof the iterative parameter τ and the small parameter ε are presented. Astudy of the auxiliary problem of the fictitious domain method for NavierStokes equations with continuation into a fictitious subdomain by highercoefficients with a small parameter is carried out. A generalized solutionof the auxiliary problem of the fictitious domain method with continuationby higher coefficients with a small parameter is determined. After all theabove mathematical studies, a computational algorithm has been developedfor the numerical solution of the problem. Two methods were used to solvethe problem numerically. The first variant is the fictitious domain methodassociated with the modification of nonlinear terms in a fictitious *** model problem shows the effectiveness of using such a modification. Theproposed version of the method is used to solve two problems at once that arisewhile numerically solving systems of Navier-Stokes equations: the problem ofa curved boundary of an arbitrary domain and the problem of absence of aboundary condition for pressure in physical formulation of the internal flowproblem. The main advantage of this method is its universality in developmentof computer programs. The second method used calculation on a uniform gridinside the area. When numerically implementing the solution on a uniformgrid inside the domain, using this method it’s possible to accurately take intoaccount the boundaries of the curved domain and ensure the accuracy of thevalue of the function at the boundaries of the domain. Methodical calculationswere carried out, the results of numerical calculations were obtained. Whenconducting numerical experiments in both cases, quantitative and qualitativeindicators of numerical results coincide.

关键词： fictitious domain method Navier-Stokes equations difference schemes approximation computational algorithm numerical experiment

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Detailed analysis of the relativistic configuration of Bardeen anisotropic spheres in modified f(G)gravity

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Communications in Theoretical Physics 2024年第6期76卷 45-56页

作者： Adnan Malik Ayesha almas Tayyaba Naz Rubab Manzoor M Z Bhatti School of Mathematical Sciences Zhejiang Normal UniversityJinhuaZhejiangChina Department of Mathematics University of Management and TechnologySialkot CampusLahorePakistan National University of Computer and Emerging Sciences Lahore CampusPakistan Department of Mathematics University of Management and TechnologyJohar Town CampusLahore 54782Pakistan Department of Mathematics University of the PunjabQuaid-i-Azam CampusLahore-54590Pakistan

The aim of this work is to investigate anisotropic compact objects within the framework of f(G)modified theory of *** our present work,we utilize Krori-Barua metrics,i.e.,λ(r)=Xr^(2)+Y andβ(r)=Zr^(2).We use some matching conditions of spherically symmetric spacetime with Bardeen's model as an exterior ***,we establish some expressions of energy density and pressure components to analyze the stellar configuration of Bardeen compact stars by assuming viable f(G)*** examine the energy conditions for different stellar structures to verify the viability of our considered ***,we also investigate some other physical features,such as equilibrium condition,equation of state parameters,adiabatic index,stability analysis,mass function,surface redshift,and compactness factor,*** is worthwhile to mention here for the current study that our stellar structure in the background of Bardeen's model is more viable and stable.

关键词： Bardeen compact stars f(G)modified gravity Krori-Barua potential

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fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

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Computers, Materials & Continua 2022年第1期70卷 619-635页

作者： Javaria Amin Muhammad almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMSATS University Islamabad Wah CampusPakistan faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

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Lattice distortion generates bound states in the continuum

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Science China(Physics,Mechanics & Astronomy) 2024年第3期67卷 166-166页

作者： almas Sadreev Kirensky Institute of Physics Federal Research Center KSC Siberian BranchRussian Academy of SciencesKrasnoyarsk 660036Russia

High-Q optical cavity is an indispensable component of many photonic devices, such as lasers, sensors, harmonic generation and photon emission, and chiral *** way of realizing ultrahigh Q-factor relies on fabry-Pérot... 详细信息

关键词： Lattice distortion cavity

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CNN Based features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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Computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

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Computers, Materials & Continua 2021年第8期68卷 2451-2467页

作者： Javaria Amin Muhammad Sharif Muhammad almas Anjum Yunyoung Nam Seifedine Kadry David Taniar Department of Computer Science University of Wah47040Pakistan Department of Computer Science Comsats University IslamabadWah Campus47040Pakistan College of Electrical and Mechanical Engineering National University of Sciences&Technology(NUST)Islamabad44000Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab University115020Lebanon faculty of Information Technology Monash UniversityClaytonVictoria3800Australia

Coronavirus 19(COVID-19)can cause severe pneumonia that may be *** diagnosis is *** tomography(CT)usefully detects symptoms of COVID-19 *** this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and *** the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung *** the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification ***3 is an effective decoder that helps to refine segmentation of lesion *** model was trained using fine-tuned hyperparameters selected after extensive ***,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented *** computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for *** performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POf Hospital *** results were better than those of existing methods.

关键词： Deeplabv3 fusion genetic algorithm gabor watershed

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