In recent years, the use of the Internet of Medical Things (IoMT) and electronic health records (EHRs) has created exhaustive sensitive healthcare data. If this data is analyzed in an effective way, it will improve the prediction of diseases, the recovery of patients, and the personalization of medicine. However, the collection of data in a central manner brings with it some serious problems related to privacy, security, and rules. Federated Learning (FL), the machine learning approach that is decentralized, seems to be a solution in which model training is carried out in a collaborative way without sharing any raw data. The application of FL in distributed health data analysis is the subjec... More >

Mental health poses a growing concern in metropolitan areas where the speedy urbanization and societal demands are the chief causes of psychological discomfort. The context of intelligent cities, through their capabilities of advanced technologies and interconnecting networks, facilitates the approach of predictive analytic resolution of such issues. This paper is research regarding the implementation of machine learning in conjunction with Artificial Intelligence (AI) inter-operation for the prompt identification and management of mental health anomalies in smart cities. By using information from wearable gadgets, social networks, and the Internet of Things (IoT) based health monitoring sys... More >

Infectious disease outbreaks pose significant challenges to public health infectious disease outbreaks create for the local population, the economy, and the world order. To be successful in early intervention and resource allocation, the prediction of such outbreaks should be as accurate as possible. This study describes the most successful approaches for epidemic prediction through the application of Artificial Intelligence (AI), which utilizes machine-learning and deep-learning models to assess various epidemiological, environmental, and socio-economic factors. Identification of urban patterns, prediction of the spread of diseases, and generation of actionable hypotheses might be performed... More >

The quick rate at which healthcare has accepted digital technologies has generated several sensitive medical record data. Though this data has huge potential for innovative medical research and personalized things in health care, it also brings with it a huge future worry for the safety and privacy of patients. Protection of sensitive patient data access is the main obstacle of our time; thanks to Federated Learning (FL) companies in these fields are not only reviewing data for fraud control but also are using this data for planning cancer treatment. This paper narrated FL to be the core technology used in solving the most important privacy and security issues with the health information-sha... More >

Neural computing, as an influential factor of artificial intelligence, is an industry that has managed to achieve an extensive array of innovations. This paper presents an overview of the recent advancements in the field of neural computing, which are focused on state-of-the-art architectures, novel computational paradigms, and their applications in intelligent systems. The paper traces the development of neural networks, from the original artificial neural network (ANN) through deep learning models and on to neuromorphic computing. In other words, the main points of emphasis are breakthroughs in hardware acceleration, hybrid models, and bio-inspired computing, which are responsible for inte... More >

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 >