Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet...
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At present, recommendation systems have become pivotal in personalized education learning management systems, where there is a growing need for location-based suggestions. Our problem addresses the inefficiency of cur...
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This paper investigates dynamic anomaly detection in resource-constrained environments by leveraging Robust Random Cut Forests (RRCF). Anomaly detection is crucial for maintaining the integrity and security of data st...
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This paper investigates dynamic anomaly detection in resource-constrained environments by leveraging Robust Random Cut Forests (RRCF). Anomaly detection is crucial for maintaining the integrity and security of data streams in Internet of Things (IoT) environments, where data is continuously generated and often subject to noise and fluctuations. We begin with a comprehensive exploration of resilient random cut data structures tailored for analyzing incoming data streams, highlighting their effectiveness in adapting to the dynamic nature of *** methodology encompasses extensive experimentation with diverse datasets, including real-time Arduino data and benchmark datasets such as IoT-23 and CIC-IoT. Through this approach, we assess the performance of the RRCF algorithm under various scenarios, focusing on its capability to accurately identify trends and anomalies over time. Notably, we achieve significant performance improvements, with an average Area Under the Curve (AUC) of 95.6 and an F1 score of 0.86, demonstrating RRCF’s effectiveness in real-time anomaly *** further enhance detection accuracy, we introduce dynamic thresholds that adapt to changing data characteristics, allowing our model to maintain robust performance even in the presence of noise. Detailed evaluations reveal that our approach consistently outperforms existing state-of-the-art methods, particularly in terms of handling noisy data and ensuring computational efficiency under resource *** findings underscore the potential of RRCF as a powerful tool for real-time applications within IoT systems, providing a solid theoretical foundation for future advancements in dynamic anomaly detection. By investigating non-parametric anomalies and analyzing the influence of external factors on data integrity, we uncover hidden patterns amidst dynamic fluctuations. This research emphasizes the need for adaptive strategies in evolving data landscapes, laying the groundwork for enhanced resil
Pancreatic cancer's devastating impact and low survival rates call for improved detection methods. While Artificial Intelligence has shown remarkable progress, its increasing complexity has led to "black box&...
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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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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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As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classif...
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As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical *** this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN *** existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the *** solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each *** order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in *** conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force *** experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 ***-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).
In this article, a novel method is proposed to facilitate the design of compact, low-profile, pattern reconfigurable antennas with fixed or switchable circular polarization (CP) for Internet of Vehicles (IoV) applicat...
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Several approaches can detect early heart problems. Electrocardiograms (ECGs) are better and more affordable for early heart disease prediction. ECG data can better predict heart diseases and abnormalities. Standard m...
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With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting ***,supervised deep learning models require labe...
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With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting ***,supervised deep learning models require labelled datasets for *** such a huge amount of labelled data requires considerable human effort and *** this scenario,self-supervised models are becoming popular because of their ability to learn even from unlabelled ***,the efficient transfer of knowledge learned by self-supervised models into a target task,is an unsolved *** paper proposes a method for the efficient transfer of know-ledge learned by a self-supervised model,into a target *** such as the number of layers,the number of units in each layer,learning rate,and dropout are automatically tuned in these fully connected(FC)layers using a Bayesian optimization technique called the tree-structured parzen estimator(TPE)approach *** evaluate the performance of the proposed method,state-of-the-art self-supervised models such as SimClr and SWAV are used to extract the learned *** are carried out on the CIFAR-10,CIFAR-100,and Tiny ImageNet *** proposed method outperforms the baseline approach with margins of 2.97%,2.45%,and 0.91%for the CIFAR-100,Tiny ImageNet,and CIFAR-10 datasets,respectively.
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