A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff.

Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.

Traffic sign recognition and autonomous vehicles computing are a few of the innovative applications which are emerging in the domain of mobile edge computing. Distributed machine learning in the form of Federated Learning (FL) has been applied to mobile edge computing through a range of methodologies and techniques for intelligent feature classification approaches. The challenges that research on such FL methods is facing is twofold: identify an optimal distributed architecture and algorithm components to each side to meet the demand of heavy data processing, and enhance the algorithm components with heuristics that fit to the problem domain and optimize the key parameters of the algorithms. In this prospect, we present a Federated Learning implementation based on a neural network architecture with emphasis to traffic sign image recognition. Our benchmark was tested with two FL strategies seeking an optimal performance model and in reference to a corresponding data set. We present the results of this work while we define the scope of future improvements to our model.

Today, healthcare system models should have high accuracy and sensitivity so that patients do not have a misdiagnosis. For this reason, sufficient knowledge of the area is required, with the medical staff being able to validate the correctness of their decisions. Therefore, artificial intelligence (AI) in combination with other emerging technologies could provide many benefits in the medical sector. In this paper, we demonstrate the combination of Internet of Things (IoT) and cloud computing (CC) with AI-related techniques such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and neural networks (NN) in order to provide a useful approach for scientists and doctors. Our proposed model makes use of these immersive technologies so as to provide epidemic forecasting and help accelerate drug and antibiotic discovery.

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems.

Federated Learning (FL) has emerged as a promising approach for training machine learning models on decentralized data, but it poses significant challenges in terms of model optimization and performance evaluation. To address these challenges, this paper introduces an application generator/tailoring tool designed specifically for FL analysts. The tool provides a framework for configuring Federated Averaging algorithms and tailoring them to suit a related set of applications, such as image recognition. The application generator/tailoring tool employs a dynamic gradient evaluation strategy to evaluate the performance of the FL model at runtime. This approach enables the analyst to adjust the FL model's behavior to achieve the desired objectives in terms of communication costs and data convergence requirements. By providing such flexibility, the tool empowers analysts to optimize FL models according to their specific needs and priorities. To demonstrate the effectiveness of the application generator/tailoring tool, the authors conducted experiments with traffic sign images. The results showed that the tool is highly effective in improving the performance of FL models for image recognition tasks. This finding suggests that the tool has significant potential to accelerate the development of FL models and enhance their performance in real-world applications. Overall, the application generator/tailoring tool presented in this paper provides a powerful and flexible approach to FL model optimization and evaluation. By enabling analysts to dynamically evaluate and optimize FL models, the tool has the potential to accelerate the adoption of FL in a wide range of real-world applications, from healthcare to transportation and beyond. Impact Statement — This paper presents a novel application generator/tailoring tool that allows Federated Learning analysts to dynamically evaluate and optimize FL models for image recognition tasks, while balancing communication costs and data convergence requirements. The tool's effectiveness is demonstrated through experiments with traffic sign images, highlighting its potential to accelerate FL development and improve performance in real-world applications.