Neural network-enhanced machine learning is revolutionizing cybersecurity by enabling real-time detection of anomalous activities and proactive prevention of unauthorized access in large-scale networks. Traditional security measures often prove ineffectual in the face of the fast-developing threats, as they depend on unchanging rules and signature detections, which can be bypassed by the advanced cyber adversaries. In contrast, neural networks apply deep learning techniques to several data sets including user behavior, network traffic, and system activity, which helps them to spot small irregularities that may mean a potential threat. By feed-forwarding new information on the high-quality tr... More >

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 >

Currently, a considerable amount of people are sending messages on social networks such as Twitter, Amazon and Facebook. These media is colossal with data and information. Bearing in mind the need for these social media platforms to extract the appropriate negative or positive emotions from users and even news articles, opinion mining is required. Opinion mining provides the ability to assess social media users' opinions as well as the provided knowledge that assists in emotion detection. Some issues that have been more prevalent, in social media, include the lack of sentiment accuracy, transparency, and accuracy in measuring the users' sentiments. In social media, a variety of solutions bas... More >

The inaugural editorial of the IECE Transactions on Neural Computing (IECE-TNC) presents the revolutionary influence of neural computing that incorporates artificial intelligence (AI), machine learning (ML), and next-gen computation models in cognitive systems, robotics, and healthcare. Although there have been tremendous developments, some problems remain including computational scalability, model interpretability, ethical considerations, and data security. IECE-TNC is dedicated to resolving these issues by facilitating high-impact research, interdisciplinary collaboration, and real-world applications. The magazine covers the following trends such as federated learning, explainable deep lea... More >