One of the most concerning environmental issues is water pollution, which is mostly brought on by plastic waste that is dumped into the aquatic region from land. These plastics pose a threat to the marine ecosystem...
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Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)envi...
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Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and ***,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and *** issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous *** address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based *** SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop *** approach reduces broadcast storms,optimizes overall energy consumption,and extends network *** system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed *** SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare *** demonstrated that SEF significantly enhanced NDN-based IoHT ***,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated *** forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.
In today's world predicting stock prices remains a challenge due to markets being volatile as it is driven by multiple factors. In the past, investors and business men depended on traditional methods of prediction...
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Efficient transmission of prioritized data packets is of utmost importance for wireless body area networks (WBAN). In this paper, the problem of inter-WBAN communication with multiple edges is investigated to maximize...
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Alzheimer's Disease (AD), which is characterized by cognitive decline and memory loss, affects millions of people worldwide and poses a significant challenge to modern healthcare. Detecting AD at its earliest stag...
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Accurate detection and classification of road faults such as cracks is critical for transportation infrastructure maintenance. Road cracks impede comfortable traveling, endanger passenger safety, and create incidents....
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The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and appli...
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Objective Hypertension is a critical medical condition that increases the risks of many fatal *** detection of hypertension can be crucial to lead a healthy *** learning(ML)can be useful for the early prediction of a ...
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Objective Hypertension is a critical medical condition that increases the risks of many fatal *** detection of hypertension can be crucial to lead a healthy *** learning(ML)can be useful for the early prediction of a patient’s likelihood of having a blood pressure abnormality and preventing *** artificial intelligence(XAI)is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML *** research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using *** This study utilized the“Blood Pressure Data for Disease Prediction”dataset from *** were collected from medical reports of random participants in 2019 based on the presence of blood pressure abnormality,chronic kidney disease,and adrenal and thyroid *** have used several ML algorithms(extreme gradient boosting(XGBoost),random forest(RF),support vector machine(SVM),decision tree(DT),and logistic regression(LR))to predict blood pressure abnormality based on patient’s *** component analysis(PCA)and recursive feature elimination(RFE)algorithms were used as feature *** outcome metrics included receiver operating characteristic(ROC)curve analysis and *** performance measurement techniques,such as precision,recall,specificity,F1-score,and kappa were calculated to identify the model with the best ***,several XAI methods,namely permutation feature importance(PFI),partial dependence plots(PDP),Shapley additive explanations(SHAP),and local interpretable model-agnostic explanations(LIME)were implemented for additional exploration of our best *** The combination of RFE and XGBoost provides the most significant *** results of the study show that the algorithm has an AUC of 0.95,indicating good discriminatory power in detecting abnormal blood pressur
The process of bringing criminals to justice can be complicated when reporter information and sensitive data related to the case are revealed and may involve international law enforcement cooperation, especially when ...
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Medical image segmentation becomes increasingly important for identifying and delineating anatomical structures, diseases, and abnormalities within medical images. However, existing large pre-trained foundation models...
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