In today’s rapidly changing world, cloud service providers face numerous challenges in managing resources and meeting customer demands. To address these challenges, cloud service providers should prioritize the tasks...
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Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous t...
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Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.
It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke *** advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitati...
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It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke *** advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitations:they(i)lack flexibility to choose different art-style strokes,(ii)lose content details of images,and(iii)generate few artistic styles for *** this paper,we propose a stroke-style generative adversarial network,called Stroke-GAN,to solve the first two ***-GAN learns styles of strokes from different stroke-style datasets,so can produce diverse stroke *** design three players in Stroke-GAN to generate pure-color strokes close to human artists’strokes,thereby improving the quality of painted *** overcome the third limitation,we have devised a neural network named Stroke-GAN Painter,based on Stroke-GAN;it can generate different artistic styles of *** demonstrate that our artful painter can generate various styles of paintings while well-preserving content details(such as details of human faces and building textures)and retaining high fidelity to the input images.
Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in op...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identification also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deficiency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deficiency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sfirst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areflat-tened and given to the dense layer(DL)***,the Maximum Margin clas-sifier(MMC)is deployed and takes the input from DL for the classification process tofind *** dataset used in this work has no visible symptoms of a deficiency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deficiency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.
Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination....
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Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a ...
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In the era of multimedia technology digital images are essential and keeping them safe from unauthorised access is crucial. To address this issue, the proposed research explores the intersection of image steganography...
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The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy...
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The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in *** recognition of BT is highly significant to protecting the patient’s ***,the BT can be identified through the magnetic resonance imaging(MRI)scanning *** the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the ***,ML has prevailed against standard image processing *** studies denote the superiority of machine learning(ML)techniques over standard ***,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)*** accomplish the detection of brain tumor effectively,a computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research ***,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull ***,mayfly optimization with the Kapur’s thresholding based segmentation process takes *** feature extraction proposes,local diagonal extreme patterns(LDEP)are *** last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification *** accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research *** experimental validation of the proposed model demonstrates its promising performance over other existing methods.
The immensely increasing number of Deepfake technologies poses significant challenges to digital media integrity, leading to the immediate need for effective Deepfake detection methods. In light of the growing threat ...
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Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated me...
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