In order to improve the level of cross-space transmission and sharing of multi-source network information, it is necessary to effectively predict the process risk of fusion, and a risk prediction method of cross-space...
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The COVID-19 epiphytotic highlighted the necessity for speedy and accessible identification instruments. Chest X-rays offer a readily available and relatively inexpensive Tomography proficiency. This stuff explores th...
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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, ...
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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
A decentralized computing system distributed among data-generating hardware and the cloud is called fog computing (FC). The ability to place resources to improve performance is given to users by such a flexible struct...
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With this work, a technical method for identifying Indian Sign Language is attempted to be developed. This work attempted to create a system for recognizing the hand movements from Indian Sign Language using machine l...
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In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any t...
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This research introduces a sophisticated framework tailored for the proactive identification and management of viral afflictions in plants, concentrating on five primary culprits: Yellow Curl Virus, Mosaic Virus, Late...
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Objective: The objective is to develop a generalized deep learning system for sentiment analysis in a feedback system. This implies an intention to create a model that can effectively analyze sentiment across differen...
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The Wilson loop is indicative of the pathway encompassed within the viral replication process, which carries the coherent gauge field behavior present in the genetic coding of the virus. We enhance the capabilities of...
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Closed-circuit television, or CCTV, is recognized as a crucial element in security infrastructure;however, its effectiveness faces challenges due to increasing storage demands. The synthesis evaluates a range of resea...
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