The rapid evolution of digital threats is a major hurdle to the security of vital infrastructure, driving the need for advanced cybersecurity methods like those based on the use of new technologies. This research seeks to assess the use of neural networks in cybersecurity and especially the role of these technologies in proactive risk management and threat intelligence. Neural networks, mainly deep learning models, had excellent success in detecting, analyzing, and mitigating all cyber threats with no time delay. Through the integration of sophisticated components such as pattern recognition, anomaly detection, and predictive analytics, these models improve threat detection accuracy while mi... More >

The global healthcare systems have faced unprecedented challenges due to the COVID-19 pandemic, necessitating innovative neural computing solutions to inform critical decision-making. In this study, we introduce a neural-inspired machine learning framework to predict COVID-19 mortality risk, utilizing a dataset comprising over one million records. We developed and evaluated a suite of advanced models—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Gradient Boost Classifier, and a neural ensemble-based Voting Classifier—to analyze the influence of demographics, symptoms, and preexisting conditions on mortality predictions. Through meticulous feature engineering... More >