Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging ...
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Diabetic retinopathy (DR), a type of eye disease, is a danger for diabetics. Manual labour, which is prone to inaccuracy and time consuming, makes dealing with this illness considerably more difficult. Normally comput...
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Diabetic retinopathy (DR), a type of eye disease, is a danger for diabetics. Manual labour, which is prone to inaccuracy and time consuming, makes dealing with this illness considerably more difficult. Normally computer-assisted diagnosis has appeared as a promising tool for the early identification and severity grading of DR. As technologies are revolutionizing day by day, in which the most advance technology deep learning's algorithm gives a tremendous support for healthcare fields. This article proposes an efficient classification of DR models for categories the DR into different grades and to identify the severity. There various prediction techniques employed in DR detection. Radial Basics Network, Multilayer Perceptron and Recurrent Neural Network are binary classifiers employed for DR classification. Further the Bag of Visual Words and Convolutional Neural Networks implements for the stages of 3. The performance shows that Convolutional Neural Network perform superior over other methods and attains 98.3%. It is of great significance to apply deep-learning techniques for DR recognition. However, deep-learning algorithms often depend on large amounts of labeled data, which is expensive and time-consuming to obtain in the medical imaging area. In addition, the DR features are inconspicuous and spread out over high-resolution fundus images. Therefore, it is a big challenge to learn the distribution of such DR features. To overcome this, This research work proposes a multichannel-based generative adversarial network (M-GAN) for data augmentation as well as classification to grade DR The usefulness and effectiveness of GAN for classification of fundus images are explored for the first *** medical data is also a tedious and challenging one because it is quite expensive and confidential, to overcome this proposed model is acts data augmentation model, moreover the features in the input data’s are reduced by Dimensionality reduction Module (DRM) based on Pri
Joint detection and decoding (JDD) achieves rates based on information theory but is too complex to implement for many channels with memory or nonlinearities. Successive interference cancellation (SIC) at the receiver...
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The use of management by objectives (MBOs) methodologies, particularly the objectives and key results (OKRs) framework, has gained widespread attention in recent years as a means of improving organizational performanc...
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In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movemen...
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In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.
Bleurt a recently introduced metric that employs Bert, a potent pre-trained language model to assess how well candidate translations compare to a reference translation in the context of machine translation outputs. Wh...
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Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddresse...
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The paper proposed a secured and efficient data aggregation mechanism leveraging the edge computing paradigm and homomorphic data encryption technique. The paper used a unique combination of Paillier cryptosystem and ...
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The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernie...
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The primary goal of this paper is to introduce a novel method for mining frequent and interesting items by incorporating correlation analysis between two items in an uncertain transactional database using the OWA oper...
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