COVID-19 health data prediction: a critical evaluation of CNN-based approaches

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 17, 2025

The COVID-19 pandemic has significantly accelerated the demand for accurate and efficient prediction models to support effective disease management, containment strategies, informed decision-making. Predictive capable of analyzing complex health data are essential monitoring trends, evaluating risk factors, optimizing resource allocation during pandemic. Among various machine learning approaches, convolutional neural networks (CNNs) have emerged as powerful tools due their ability process large volumes high-dimensional data, such medical images, time-series patient demographics, with impressive precision. This research seeks systematically examine challenges limitations inherent in utilizing CNNs prediction, offering a comprehensive perspective grounded science research. Key areas investigation include issues related quality availability, incomplete, noisy, imbalanced datasets, which often hinder training robust models. Additionally, architectural constraints CNNs, including sensitivity hyperparameter tuning reliance on substantial computational resources, explored critical bottlenecks that impact scalability efficiency. A significant focus is placed generalization challenges, where trained specific datasets struggle adapt unseen from diverse populations or clinical settings, limiting applicability real-world scenarios. study further highlights reported accuracy 63%, underscoring need improved methodologies enhance model performance reliability. By addressing these this aims provide actionable insights practical recommendations optimize use prediction. In particular, emphasizes importance incorporating advanced strategies transfer learning, augmentation, regularization techniques overcome dataset robustness. integration multimodal approaches combining images auxiliary demographics laboratory results, proposed improve contextual understanding diagnostic Finally, underscores necessity interdisciplinary collaboration, leveraging domain expertise scientists, healthcare professionals, epidemiologists develop holistic solutions tackling complexities shedding light potential domain, guide researchers practitioners making decisions about design, implementation, optimization. Ultimately, it contributes advancing AI-driven diagnostics predictive modeling other public crises, fostering development scalable reliable better outcomes.

Language: Английский

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FractalNet-based key generation for authentication in Voice over IP using Blockchain

Ain Shams Engineering Journal, Journal Year: 2025, Volume and Issue: 16(3), P. 103286 - 103286

Published: Feb. 5, 2025

Language: Английский

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Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 10, 2025

This scholarly paper explores the utilization of Machine Learning (ML) and Deep (DL) methodologies to enhance cybersecurity aspects script development. Given increasing panorama threats in contemporary software creation, has ascended a critical realm concern. Traditional security measures frequently prove inadequate countering complex breaches. However, ML DL present promising solutions by facilitating automated intelligent scrutiny security-centric tasks. In this investigation, we leverage Fashion MNIST dataset, deploying Convolutional Neural Network (CNN) model underscore efficacy elevating cybersecurity. The trajectory development encompasses stages like data preprocessing, training, assessment through metrics such as accuracy loss. Our empirical findings convincingly demonstrate that proposed methodology yields significant enhancements benchmarks, thereby validating potential techniques reinforcing security. Furthermore, explore practical implications delineate application ML/DL integration within real scenarios. Through adept amalgamation development, developers can augment robustness their systems against various threats. enriches growing body research while providing invaluable insights practitioners striving bolster resilience ever-evolving landscape challenges.

Language: Английский

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Enhancing Security and Trust Using Efficient Privacy-Preserving Authentication in Vehicular Edge Computing Networks

Vehicular Communications, Journal Year: 2025, Volume and Issue: unknown, P. 100921 - 100921

Published: April 1, 2025

Language: Английский

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