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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

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

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

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

3D Semantic Deep Learning Networks for Leukemia Detection

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Computers, Materials & Continua 2021年第10期69卷 785-799页

作者： Javaria Amin Muhammad Sharif Muhammad almas Anjum ayesha Siddiqa Seifedine Kadry Yunyoung Nam Mudassar Raza University of Wah Wah CanttPakistan COMSATS University Islamabad Wah CampusPakistan National University of Technology(NUTECH) IJP Road IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and *** cell growth destroys the body’s immune system,and computerized methods play a vital role in detecting abnormalities at the initial *** this research,a deep learning technique is proposed for the detection of *** proposed methodology consists of three *** I uses an open neural network exchange(ONNX)and YOLOv2 to localize *** localized images are passed to Phase II,in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception *** segmented images are used in Phase III,in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify *** proposed methodology is validated on three publically available benchmark datasets,namely ALL-IDB1,ALL-IDB2,and LISC,in terms of different metrics,such as precision,accuracy,sensitivity,and dice *** results of the proposed method are comparable to those of recent existing methodologies,thus proving its effectiveness.

关键词： YOLOv2 darknet53 Bhattacharyya separately criteria ONNX

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

Malaria Parasite Detection Using a Quantum-Convolutional Network

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Computers, Materials & Continua 2022年第3期70卷 6023-6039页

作者： Javaria Amin Muhammad almas Anjum Abida Sharif Mudassar Raza Seifedine Kadry Yunyoung Nam University of Wah Wah CanttPakistan National University of Technology(NUTECH) IslamabadPakistan COMSATS University Islamabad Vehari CampusVehariPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Malaria is a severe illness triggered by parasites that spreads via mosquito *** underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through ***-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected ***,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria *** methods comprise three core steps:localization,segmentation,and *** the first core step,an improved FRCNN model is proposed for the localization of the infected malaria ***,the RGB localized images were converted into YCbCr channels to normalize the image intensity ***,the actual lesion region was segmented using a histogram-based color thresholding *** segmented images were employed for classification in two different *** the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria ***,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for ***,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to *** proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.

关键词： Quantum convolutional RGB YCbCr histogram Malaria

Vector textures derived from higher order derivative domains for classification of colorectal polyps

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 187-200页

作者： Weiguo Cao Marc J.Pomeroy Zhengrong Liang almas FAbbasi Perry J.Pickhardt Hongbing Lu Department of Radiology State University of New York at Stony BrookStony Brook NY 11794USA Department of Biomedical Engineering State University of New York at Stony BrookStony Brook NY 11794USA Department of Radiology University of Wisconsin Medical SchoolMadison WI 53705USA Department of Biomedical Engineering the Fourth Medical UniversityXi’an 710032ShaanxiChina

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion *** this study,higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging *** make good use of the derivative information,a novel concept of vector texture is firstly introduced to construct and extract several types of polyp *** widely used differential operators,i.e.,the gradient operator and Hessian operator,are utilized to generate the first and second order derivative *** derivative volumetric images are used to produce two angle-based and two vectorbased(including both angle and magnitude)***,a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp *** evaluate the performance of our method,experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic *** compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13%evaluated by the area under the receiver operating characteristics curves.

关键词： Machine learning Gradient Hessian matrix Haralick feature Random forest Image texture

Precision position measurement of single atom

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Chinese Optics Letters 2012年第10期10卷 75-78页

作者： Ziauddin Rahmatullah almas Khan Centre for Quantum Physics COMSATS Institute of Information TechnologyIslamabadPakistan Department of Physics Virginia TechBlacksburg VA 24061USA

Atom localization in a five-level atomic system under the effect of three driving fields and one standing wave field is suggested. A spontaneously emitted photon from the proposed system is measured in a detector. Precision position measurement of an atom is controlled via phase and vacuum field detuning without considering the parity violation.

关键词： Precision position measurement of single atom