One of the computationally most intensive tasks of an Network Intrusion Detection System (NIDS) is string/pattern matching where the payload of incoming packet is compared against predefined rule-set in order to detec...
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Robust exam monitoring solutions are now essential in light of the most notable Covid-19 outbreak and the increasing inclination towards virtual learning. The automated method for supervising online exams that is sugg...
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With rapid advancements in technology, the process of recruitment has evolved from traditional methods to online and automated methods known as e-recruitment. While e-recruitment streamlines the initial screening proc...
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Today's society heavily depends on online communities, which host a wide variety of social networks and public and private services. These demands led to the creation of Android, the world's most popular mobil...
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Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categ...
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This study addresses healthcare provider fraud in Medicare, employing advanced machine learning models on a diverse dataset to predict potential fraud. The goal is to contribute insights for effective fraud detection ...
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The need for efficient disease management in crop production is highlighted by the economic relevance of agriculture. Various leaf diseases cause major losses for tea, a staple product in many places. In this paper, a...
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Medical information cannot be easily shared and exchanged between various healthcare systems and providers due to a lack of interoperability, delays, inefficiencies, and above all lack of security. This paper provides...
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Fetal health care is one of the most crucial concerns in today's world. Evaluating fetal well-being has always been challenging. Proper development and monitoring of the fetus are indispensable for its growth and ...
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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
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