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A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

Computers, Materials & Continua 2024年第6期79卷 4063-4080页

作者： Mahatir Ahmed Tusher Saket Choudary Kongara sagar dhanraj pande Seong Ki Kim Salil Bharany School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522237India School of Engineering&Technology Pimpri Chinchwad UniversityPuneMaharashtra412106India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea Independent Researcher AmritsarPunjab143001India

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human *** majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting *** is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly *** resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting *** network excels in generating conditional text by extracting style vectors from a series of style *** model performs admirably on a range of handwriting synthesis tasks,including the production of text that is *** works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting *** fields include digital forensics,creative writing,and document security.

关键词： Recurrent neural network generative adversarial network style encoder fréchet inception distance geometric score character error rate mixture density network word error rate

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Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

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Computers, Materials & Continua 2021年第3期66卷 2939-2955页

作者： Mangena Venu Madhavan Dang Ngoc Hoang Thanh Aditya Khamparia sagar pande RahulMalik Deepak Gupta School of Computer Science Engineering Lovely Professional UniversityPunjab144411India University of Economics Ho Chi Minh City Ho Chi Minh City70000Vietnam Maharaja Agrasen Institute of Technology 110086India

Disease recognition in plants is one of the essential problems in agricultural image *** article focuses on designing a framework that can recognize and classify diseases on pomegranate plants *** framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature *** image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test *** the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and *** image classification will then be implemented by combining a supervised learning model with a support vector *** proposed framework is developed based on MATLAB with a graphical user *** to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy ***,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

关键词： Image enhancement image segmentation image processing for agriculture K-means multi-class support vector machine

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A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing

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Computers, Materials & Continua 2021年第5期67卷 2647-2663页

作者： Sohan Kumar pande Sanjaya Kumar Panda Satyabrata Das Kshira sagar Sahoo Ashish Kr.Luhach N.Z.Jhanjhi Roobaea Alroobaea Sivakumar Sivanesan Veer Surendra Sai University of Technology Burla Burla768018India National Institute of Technology Warangal Warangal506004India VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad500090India The Papua New Guinea University of Technology Lae-41PapuaNew Guinea School of Computer Science and Engineering Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart *** studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are *** a circumstance,the energy consumption of overloaded vehicles is drastically *** the other hand,underloaded vehicles are also drawing considerable energy in the underutilized ***,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC *** proper and efcient utilization of the vehicle’s resources can reduce energy consumption *** of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded *** the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the *** paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is ***-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM *** ensures that any vehicles’resource utilization should not exceed the threshold before or after the ***-VMM also tries to avoid unnecessary VM migrations between the ***-VMM is extensively simulated and tested using nine *** results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and

关键词： Resource management virtual machine migration vehicular cloud computing resource utilization source vehicle destination vehicle

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