Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, ...
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Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE
With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of ...
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Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameter...
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Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.
This paper proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3....
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To suppress the resonance in an LCL filter, the passive damping method is often favored over the active damping due to its simplicity and robustness. However, the passive damping suffers from decreasing LCL filter'...
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Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor ...
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This document is a model with an accurate estimation of the battery’s State Of Charge (SOC), which is pivotal for prime performance and optimal lifespan of the rechargeable batteries. Though traditional methods, like...
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Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the ma...
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The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it af...
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