False news spreads quickly due to the extensive distribution of incorrect or misleading information across digital channels, which is a global problem. This bias undermines the credibility of information, promotes the...
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Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target *** this paper,we focus on a problem ...
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Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target *** this paper,we focus on a problem that is very specific yet we believe *** is,from the vision measurements,we can formulate various *** and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make *** by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be *** estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical *** is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based *** the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can si...
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In response to the growing demand for safe data exchange in modern digital ecosystems, the study analyzes the combination of blockchain with machine learning, proposing a unique framework to solve the limitations of e...
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The segmentation of medical images is crucial, particularly in brain tumor MR imaging, as it aids doctors in accurate diagnosis and treatment planning. However, conventional UNet models often face limitations due to t...
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Mushroom categorization is a difficult process since there are so many different species and they all have different aesthetic qualities. In this paper, we are to investigate the use of transfer learning techniques fo...
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Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple ...
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Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate ...
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Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.
Irretrievable loss of vision is the predominant result of Glaucoma in the ***,multiple approaches have paid attention to the automatic detection of glaucoma on fundus *** to the interlace of blood vessels and the herc...
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Irretrievable loss of vision is the predominant result of Glaucoma in the ***,multiple approaches have paid attention to the automatic detection of glaucoma on fundus *** to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed *** Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary *** research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting *** SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed *** is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood *** ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative *** terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.
The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machine learning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a stro...
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