The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To co...
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ISBN:
(数字)9798331521691
ISBN:
(纸本)9798331521707
The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To combat the exponential rise in infections, medical researchers have primarily relied on RT-PCR testing to distinguish between COVID-19 positive and negative cases. However, this approach faces significant hurdles, particularly in low-income countries where these tests are often too expensive and inaccessible in rural areas. Another critical challenge is the shortage of skilled healthcare professionals, especially doctors and radiologists, whose expertise is in high demand for diagnosing and treating COVID-19 patients. As a result, healthcare resources have been stretched to their limits. To address these issues, innovative solutions are essential, and artificial intelligence (ai) and machine learning (ML) offer promising possibilities. In medical diagnostics, ai, ML, and deep learning (DL) have demonstrated remarkable accuracy. This study explores the application of DL models, specifically ResNet-34 and ResNet-50, for COVID-19 detection using X-ray images. The results indicate an impressive accuracy of 96.4% with ResNet-34. To comprehensively evaluate the performance of these models, the study employs various quantitative metrics, including Matthews Correlation Coefficient (MCC), F1 Score, Precision, and Recall. By contributing to both the effective detection of COVID-19 and addressing the shortage of healthcare professionals, this research provides a hopeful step forward in the ongoing fight against the pandemic.
Researchers are driven to complete their work in sentiment analysis by the ever-increasing demands placed on government agencies and commercial companies. The way people express themselves on social media is a reflect...
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ISBN:
(数字)9798350369748
ISBN:
(纸本)9798350369755
Researchers are driven to complete their work in sentiment analysis by the ever-increasing demands placed on government agencies and commercial companies. The way people express themselves on social media is a reflection of their opinions on various products, services, and events. As a subfield of NLP, sentiment analysis aims to extract positive or negative polarities from text found in social media platforms. Three cutting-edge machine learning classifiers-Naive Bayes, SVM, and OneR-are showcased in this study for the purpose of optimizing sentiment analysis. Two benchmark datasets are used in the studies; one dataset is derived from Amazon, while the other is derived from IMDB movie reviews. We compare and analyze the results of these classification methods. The Naive Bayes learned rather quickly, but OneR shows more promise with a precision of 92.6%, an F-measure of 96%, and a properly categorized occurrence rate of 93.4%.
This research introduces SmartFit, a state-of-the-art technology that seamlessly integrates biometric data and ai-powered training recommendations. SmartFit monitors the user's pulse rate, oxygen saturation, and m...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
This research introduces SmartFit, a state-of-the-art technology that seamlessly integrates biometric data and ai-powered training recommendations. SmartFit monitors the user's pulse rate, oxygen saturation, and muscle activity in real time. Subsequently, it implements machine learning algorithms to develop customized training regimens. This ongoing adjustment to the user's evolving fitness levels and objectives guarantees that the workout regimens are both highly effective and customized to their unique requirements. The proposed system is designed to optimize exercise efficacy, promote extended engagement in fitness activities, and improve user satisfaction. The novel method significantly enhanced the fitness ratings and overall satisfaction of participants in comparison to more conventional forms of exercise, as evidenced by a three-month pilot study. The results demonstrate the potential for SmartFit's data-driven, individualized training recommendations to transform the fitness industry.
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