Agriculture is crucial to the global economy, particularly in ensuring food security. Recent trends indicate that various plant diseases are causing substantial financial losses in the agricultural sector worldwide. T...
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The incorporation of neural networks into medical imaging has recently resulted in significant modifications to diagnosis. This article looks at the job of brain networks in clinical picture handling, featuring their ...
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Corn, Rice, and Wheat serve as primary staple foods globally, playing a pivotal role in the economies of numerous countries. Despite their paramount importance, these cereal crops face susceptibility to various diseas...
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Detecting and promptly identifying cracks on road surfaces is of paramount importance for preserving infrastructure integrity and ensuring the safety of road users, including both drivers and pedestrians. Presently, t...
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The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
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Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the ef...
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Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of *** study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural *** research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck *** also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate *** experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these *** findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.
The management of healthcare data has significantly benefited from the use of cloud-assisted MediVault for healthcare systems, which can offer patients efficient and convenient digital storage services for storin...
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Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning ...
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Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning technologies are widely used in the field of healthcare because they can analyze images rapidly and precisely. However, because each object on the image has the potential to hold illness information in medical images, it is critical to analyze the images with minimal information loss. In this context, Capsule Network (CapsNet) architecture is an important approach that aims to reduce information loss by storing the location and properties of objects in images as capsules. However, because CapsNet maintains information on each object in the image, the existence of several objects in complicated images can impair CapsNet's performance. This work proposes a new model called HMedCaps to improve the performance of CapsNet. In the proposed model, it is aimed to develop a deeper and hybrid structure by using Residual Block and FractalNet module together in the feature extraction layer. While it is aimed to obtain rich feature maps by increasing the number of features extracted by deepening the network, it is aimed to prevent the vanishing gradient problem that may occur in the network with increasing depth with these modules with skip connections. Furthermore, a new squash function is proposed to make distinctive capsules more prominent by customizing capsule activation. The CIFAR10 dataset of complex images, RFMiD dataset of retinal images, and Blood Cell Count Dataset dataset of blood cell images were used to evaluate the study. When the proposed model was compared with the basic CapsNet and studies in the literature, it was observed that the performance in complex images was improved and more accurate classification results were obtained in the field of medical image analysis. The proposed hybrid HMedCaps architecture has the potential to make more accurate dia
In the era of advancement in technology and modern agriculture, early disease detection of potato leaves will improve crop yield. Various researchers have focussed on disease due to different types of microbial infect...
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The advancement of automated number plate recognition (ANPR) systems has garnered noteworthy attention in recent times owing to their diverse applications across multiple domains, including traffic management, parking...
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