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 >