The rapid advancement of technology has set higher standards for the next generation of wireless communication networks, known as 6G. These networks go beyond the simple task of connecting devices and aim to establish a self-sustaining system within society. One of the key factors in achieving this goal is the integration of AI services and apps through the Internet of Things (IoT), which will be made possible with the support of 6G technology. The advancement of artificial intelligence (AI) will play a crucial role in enhancing the protocols, architectures, and operations of 6G networks. To achieve collaborative AI in IoT applications, Federated Learning (FL) has emerged as a popular method. FL enables AI training without the need for data sharing, ensuring privacy and security. However, FL also faces challenges, such as the presence of malicious data and the risk of single-point failure. To address these concerns, blockchain technology (BCT) offers a secure and efficient solution. By leveraging blockchain, these issues can be effectively tackled, providing a reliable framework for implementing FL-IoT applications.

Purpose: The objective of this study was to compare the strategies adopted by the United Kingdom, Italy, China, India, and Saudi Arabia to contain the spread of the COVID-19 pandemic. Materials and Methods: A review of the literature was carried out to collect data on the strategies used by China, Italy, India, the United Kingdom, and Saudi Arabia to contain the spread of the COVID-19 virus. The global analysis of 65 published literature references allowed observing the effectiveness and efficiency of the strategies used by these countries to control the spread of the COVID-19 virus. Results: Both mitigation and suppression strategies were adopted by the United Kingdom, India, Italy, China, and Saudi Arabia to control the spread of the COVID-19 pandemic. It was observed that China has achieved a greater success in flattening the curve compared to the other countries. In China, few new daily cases have occurred since March, and it has been the only country that has managed to keep the COVID-19 pandemic under control. On the other hand, reductions in the number of daily cases (since May 2020) were detected in the United Kingdom, Italy, and Saudi Arabia (since July 2020). Also, during the last 3 months (June, July and August) India has shown the highest growth in the total number of confirmed cases and in the number of new daily cases, compared to the mentioned countries. Conclusion: The review of the strategies adopted by China, India, the United Kingdom, Italy and Saudi Arabia to combat the COVID-19 pandemic can guide countries in the design and development of mitigation and suppression approaches to control the spread of the COVID-19 virus. Containment strategies such as lockdowns cannot continue in the long term. Therefore, countries must adopt mitigation and prevention strategies to protect people from infection and learn to live with the virus.

The progress made in Information and Communication Technologies (ICT) has played a crucial role in turning the Internet of Things (IoT) into a reality. IoT is an emerging technology that refers to networks of interconnected and Internet-enabled objects equipped with sensors, processors, and actuators that interact with each other to create significant collaboration and interaction environments. The field of education is one of the areas where IoT can be applied. However, the implementation of IoT poses security and privacy risks, such as unauthorized access, denial-of-service (DoS) attacks, and interference with wireless signals where IoT devices collect a significant amount of data, including user’s personal information like identity, location, and daily behavior. Therefore, it is crucial to protect users’ privacy in IoT applications. The innovative Ubiquitous Learning Environments (ULEs) have been created by ubiquitous computing technologies (mobile, wireless, network), which provide learners with learning experiences beyond the traditional classroom in both the real and virtual worlds. Ubiquitous learning (U-learning) is an emerging technology as a result of the tremendous technological revolution of ICT. U-learning is a novel learner-centered approach that aims to enhance learning, motivation, and creativity by utilizing innovative technology and IoT. U-learning enables learners to access the appropriate learning content, collaborate with the right learning partners, and engage in self-learning at the right time and place in a ubiquitous computing environment. To support learners in developing their social skills, in this study a framework for implementing the ULE based on the Internet of Things is designed, which consists of three main layers: perception, network, and application. The article explores the effects of IoT on education and how U-learning, which incorporates IoT to enhance learning experiences, has the potential to replace traditional classroom learning. Furthermore, the article addresses privacy preservation measures for different layers within the IoT environment and ULE. A framework for implementing the ULE model is in progress, which is a part of our future work.

Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy.

Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimers disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimers disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the k-means algorithm. This results in the segmentation of images and identification of region of interest. Useful features are extracted using PCA algorithm. Finally, images are classified using machine learning algorithms.

Some previous studies have investigated the attitudes of healthcare professionals towards certain aspects of the COVID-19 outbreak. In addition, some general frameworks have been proposed to manage the pandemic. The purpose of this article was to analyze the attitudes of healthcare practitioners in Saudi Arabia towards the treatment of patients with COVID-19, work planning of practitioners, leadership approaches to manage the pandemic, sharing information strategies, medical errors, compliance with procedures, and challenges faced by the practitioners. Furthermore, another objective was to propose a general framework for managing the COVID-19 outbreak in Saudi Arabia. To achieve these purposes, a survey was designed based on an online questionnaire that was initially sent via WhatsApp, Twitter, Facebook, and email to 336 healthcare practitioners working in 7 hospitals in Saudi Arabia. The response rate was 30.4%. The outcomes indicated that healthcare practitioners in Saudi Arabia had positive attitudes towards effective communication and interaction between health professionals and patients, leadership and maintenance of team coordination, work planning, communication and cooperation between team members, training and skills development of healthcare professionals, implementing strict procedures to avoid errors and control the spread of the COVID-19 pandemic, maintaining an adequate supply of medicines and medical equipment, and obtaining the support of the government, the community, and the people. Based on the findings, it was possible to suggest that the management of health care operations related to the COVID-19 outbreak in Saudi Arabia requires effective collaboration and information sharing among various stakeholders. In this sense, communication, effective leadership, coordination and work planning, adequate treatment for patients, strict compliance with hospital rules and procedures, preventive and regulatory measures, and training and support for health professionals, were parameters considered in the general qualitative framework suggested in this study for managing the COVID-19 pandemic in Saudi Arabia. The propositions presented in this study can help the Saudi Arabian government implement an effective plan to control the spread of the COVID-19 pandemic in this country.