Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our s...
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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.
This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approache...
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This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.
The emergence of interconnected UAVs has given rise to the creation of flying ad hoc networks (FANETs) aimed at efficiently facilitating network-dependent services. However, FANET encountered considerable challenges i...
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The Telecare Medicine Information System (TMIS) revolutionizes healthcare delivery by integrating medical equipment and sensors, facilitating proactive and cost-effective services. Accessible online, TMIS empowers pat...
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Delineating the boundaries of the optic disc and cup regions is a critical pre-requisite for glaucoma screening because it allows for precise measurement of key parameters, such as cup-to-disc ratio, which is a critic...
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The growing prevalence of Internet of Things (IoT) devices has heightened vulnerabilities to botnet-based cyberattacks, necessitating robust detection mechanisms. This paper proposes DenseRSE-ASPPNet, an advanced deep...
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This article proposes a novel idea for creating a sentiment-based stock market index forecasting model by amalgamating price and sentiment data hidden in the price pattern itself. The state-of-the-art methodologies us...
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Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end ***,existing CDNs based on infrastructure cannot be employed in special cases,such as military ...
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Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end ***,existing CDNs based on infrastructure cannot be employed in special cases,such as military ***,a temporary CDN without an existing infrastructure is *** achieve this goal,we introduce a new CDN for drone-aided ad hoc networks,whereby multiple drones form ad hoc networks and quickly store specific content according to new caching *** the typical CDN server,the content-caching algorithm in the proposed architecture considers the limited storage capacity of the *** present three content distribution algorithms that consider the constraints and mobility of *** main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of *** proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through ***,acquisition time in CDN to support drone movement is improved by approximately 50%and 40%rather than one in the proposed naive greedy approach as a function of content request interval and size,respectively.
To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentat...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object ***,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient ***,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training *** results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,*** detection performance surpasses that of other single-task or multi-task algorithm models.
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