In medical informatics, developing efficient image retrieval methods is vital for the research and development of diagnosis and treatment processes. This study evaluates three different feature extraction methods that...
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This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity i...
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Interoperability is a crucial aspect of the effective functioning of Internet of Things (IoT) devices, particularly in the healthcare industry. Although the use of IoT devices in healthcare has brought numerous benefi...
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Machine uptime is highly important as the repairing time takes longer which affects the production and the manufacturing industry focus on new ways of being competitive. Manufacturing and assembly parts of the machine...
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In severe cases, diabetic retinopathy can lead to blindness. For decades,automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep lear...
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In severe cases, diabetic retinopathy can lead to blindness. For decades,automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. Toenhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequentlyoptimized using gradient (GD) based techniques. Vanishing gradient is the maindrawback of GD algorithms. In this paper, we suggest an innovative algorithm, tosolve the above problem, Hypergradient Descent learning rate based Quasi hyperbolic (HDQH) gradient descent to optimize the weights and biases. The algorithms only use first order gradients, which reduces computation time andstorage space requirements. The algorithms do not require more tuning of thelearning rates as the learning rate tunes itself by means of gradients. We presentempirical evaluation of our algorithm on two public retinal image datasets such asMessidor and DDR by using Resnet18 and Inception V3 architectures. The findings of the experiment show that the efficiency and accuracy of our algorithm outperforms the other cutting-edge algorithms. HDQHAdam shows the highestaccuracy of 97.5 on Resnet18 and 95.7 on Inception V3 models respectively.
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is *** ambiguity set considering the inherent uncertainties...
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A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is *** ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the *** power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)*** the exchange of information and energy flow,each microgrid can achieve its local supply-demand ***,the closed-loop stability and recursive feasibility of the proposed algorithm are *** comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
Accurate and efficient multi-object localization and categorization is one of the key needs for applications of robotic vision, intelligent military surveillance systems, security, and ADAS. It is a significant and co...
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Capacitive pressure sensors (CPSs) have attracted considerable interest due to their high sensitivity, low energy consumption, and potential for miniaturization, making them suitable for applications in automotive sys...
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Driver identification is a key factor in attributing liability for car accident insurance claims and assessing driver competency. Existing driver recognition systems use mechanisms based on identity keys (e.g., car ke...
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Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands subst...
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