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Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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Computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood syed muhammad usman Musaed Alhussein Khursheed Aurangzeb Shehzad Khalid muhammad Zubair Department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating *** presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease *** advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a *** challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease *** this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data *** study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural *** paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate *** proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens *** future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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Computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： muhammad Akram muhammad Waseem Iqbal syed Ashraf Ali muhammad usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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Exploration of uniform heat flux on the flow and heat transportation of ferrofluids along a smooth plate： Comparative investigation

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International Journal of Biomathematics 2018年第4期11卷 57-75页

作者： muhammad usman muhammad Hamid syed Tauseef Mohyud Din Asif Waheed Wei Wang School of Mathematical Sciences Peking University Beijing 100871 P. R. China Department of Mathematics Faculty of Sciences HITEC University Taxila Cantt Pakistan Department of Mathematics COMSATS Institute of Information Technology Attock Pakistan

The paper is devoted to a new extension in Gegenbauer wavelet method （GWM） to investigate the transfer of heat and MHD boundary-layer flow of ferrofluids beside a flat plate with velocity slip. A homogenous model study is conducted in which we assumed the heat transfer and forced convective flow of ferrofluids along a flat plate with a uniform wall heat flux. In the direction of transverse to plate, a magnetic field is imposed. Three various magnetic nanoparticle types including Mn-ZnFe204, CoFe204, Fe3O4 are incorporated inside the base fluid. Two types of base fluids （water and kerosene） with bad thermal conductivity as compared to nanoparticles of solid magnetic have been assumed. The mathematical model is tackled via modified Gegenbauer wavelet method （MGWM）. A simulation is accomplished for individual ferrofluid mixture by assuming the prevailing impacts of uniform and slip heat fluxes. The variation of heat transfers and skin friction were also observed at the surface of the plate and we analyzed the better heat transfer for every mixture. Kerosene-based magnetite （Fe304） delivers the better rate of heat transfer at wall due to its association with the kerosene-based Mn-Zn and cobalt ferrites. The slip velocity and magnetic field effects on the temperature, dimensionless velocity, rate of heat transfer and skin friction are examined for various magnetic nanoparticles inside the kerosene oil and water. We observed that the primary influence of magnetic field reduces the dimensionless surface temperature and accelerates the dimensionless velocity as compared to the hydrodynamic case, thus enhancing the rate of heat transfer and skin friction ferrofluids. Moreover, a detailed evaluation of outcomes obtained by MGWM, already published work and numerical RK-4 were found to be in excellent agreement. The error and convergence analysis are presented. Comparison of results,graphical plots, error and convergence analysis reveal the appropriateness of proposed method. T

关键词： Gegenbauer polynomials collocation method ferrofluids heat flux numericalsolution.

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On the Constant Metric Dimension of Generalized Petersen Graphs P(n,4)

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Acta Mathematica Sinica,English Series 2014年第7期30卷 1145-1160页

作者： Saba NAZ muhammad SALMAN usman ALI Imran JAVAID syed Ahtsham-ul-Haq BOKHARY Center for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University

In this paper, we consider the family of generalized Petersen graphs P（n,4）. We prove that the metric dimension of P（n, 4） is 3 when n = 0 （mod 4）, and is 4 when n = 4k ＋ 3 （k is even）.For n = 1,2 （mod 4） and n = 4k ＋ 3 （k is odd）, we prove that the metric dimension of P（n,4） is bounded above by 4. This shows that each graph of the family of generalized Petersen graphs P（n, 4） has constant metric dimension.

关键词： Metric dimension constant metric dimension generalized Petersen graphs

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Early-Stage Segmentation and Characterization of Brain Tumor

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Computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： syed Nauyan Rashid muhammad Hanif usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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Ontological Model for Cohesive Smart Health Services Management

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Computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： muhammad Raza Naqvi muhammad Waseem Iqbal syed Khuram Shahzad M.usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

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Computers, Materials & Continua 2023年第9期76卷 3671-3689页

作者： Saima Yasmeen muhammad usman Yaseen syed Sohaib Ali Moustafa M.Nasralla Sohaib Bin Altaf Khattak Department of Computer Sciences Comsats UniversityIslamabadPakistan Department of Research and Development Shearwater GeoservicesCrawleyWest SussexUK Department of Communications&Networks Engineering Prince Sultan UniversityRiyadhSaudi Arabia

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and *** convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle ***,these CNN-basedmethods have a *** do not decouple complex background information in ***,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile ***,extracting key speckle information in the presence of complex background is also a major problem with *** proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)*** primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex *** PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image *** utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling ***,the attention mechanism selectively focuses on key speckle features and facilitates complex background *** network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high *** comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration *** an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR *** source code for the PAN-DeSpeck network is available on GitHub.

关键词： Synthetic Aperture Radar(SAR) SAR image despeckling speckle noise deep learning pyramid networks multiscale image despeckling

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 932-947页

作者： syed Konain Abbas muhammad usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Impact of temperature on yield and related traits in cotton genotypes

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Journal of Integrative Agriculture 2016年第3期15卷 678-683页

作者： Kalim Ullah Niamatullah Khan Zahid usman Rehmat Ullah Fazal Yazdan Saleem syed Asif Imran Shah muhammad Salman Pakistan Central Cotton Committee Cotton Research Station Department of Agricultural Extension Education & Communication The University of Agriculture Oil Seed Programme National Agriculture Research Center

Cotton growth and development is influenced by various uncontrollable environmental conditions. Temperature variations in the field can be created by planting at different dates. The objective of the present study was to evaluate the effect of planting dates and thermal temperatures（growing degree days） on yield of 4 cotton genotypes, viz., CIM-598, CIM-599, CIM-602 and Ali Akbar-703. Plants were subjected to 6 planting dates during 2013 and 2014 in a trial conducted in randomized complete block design with four replications. For boll number, boll weight and seed cotton yield, cotton genotypes exhibited significant differences, CIM-599 produced the highest seed cotton yield of 2 062 kg ha^（–1） on account of maximum boll number and boll weight. The highest seed cotton yield was recorded in planting dates from 15 th April to 1st May whereas early and delayed planting reduced the yield due to less accumulation of heat units. Regression analysis revealed that increase of one unit（15 days） from early to optimum date（15th March to 15 th April） increased yield by 93.58 kg ha^–1. Delay in planting also decreased the seed cotton yield with the same ratio. Thus it is concluded that cotton must be sown from 15 th April to 1st May to have good productivity in this kind of environment.

关键词： Gossypium hirsutum planting dates growing degree days genotypes

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Parametic Model for Optimization of Battery Capacity and Power Transmitters of On-line Electric Vehicles in Closed/Open Environments

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 185-196页

作者： Fatima Nisar syed Haider Imtiaz Alam Asad Waqar Toqeer Ahmed Mudassar usman Department of Electrical Engineering School of Engineering&Applied Sciences(BSEAS)Bahria UniversityIslamabadPakistan Department of Centre for Advanced Electronics and Photovoltaic Engineering(CAEPE) International Islamic Universityand School of Interdisciplinary Engineering and Science(SINES)National University of Science and Technology(NUST)IslamabadPakistan

An on-line electric vehicle(OLEV)uses a wireless charging phenomenon,in which power transmitters are installed beneath the road and the OLEV’s battery is charged *** paper deals with the optimization of two key economic and design parameters,i.e.,the size of the battery and the power transmitters allocation.A complete model configuration of the OLEV system,including the vehicle design and power transmitter,is implemented using MATLAB/*** battery’s state of charge(SOC)rises and drops according to the vehicle’s velocity and power collection and *** mixed integer programming(MIP)model is used for cost ***,with the help of the SOC graph and MIP model,the battery size and the number of power transmitters,along with their placements,are *** proposed model is applicable to both closed and open environments as it accepts both regulated and deregulated *** test cases are performed for this *** first test case deals with regulated velocity for which we have applied the KAIST campus OLEV’s velocity along with its 13 kWh battery size and 4 power transmitters,and then applied the suggested solution with the same velocity and route i.e.,8 power transmitters with shorter lengths and reduced battery size(3.25 kWh;one-fourth of the first case).SOC is found within limits at the end of the route,saving$1600 and validating the proposed model in this *** the second test case,we use deregulated velocity and optimize both parameters,using the same approach.

关键词： Closed and open environments mixed integer programming on-line electrical vehicle power transmitters state of charge wireless charging

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