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The escalating global prevalence of chronic and lifestyle-related illnesses presents substantial societal and economic challenges. This work delves into an extensive review of healthcare monitoring systems tailored for chronic and lifestyle disorders. Subsequently, we propose a pioneering Smart Patient Monitoring and Recommendation (SPMR) framework, leveraging Deep Learning (DL) and cloud-based analytics. SPMR ensures continuous monitoring and predictive insights into a patient’s authentic health status using data from vital signs and contextual activities collected via Ambient Assisted Living devices. Within the predictive DL component of the LIP module, we employ Categorical Cross Entropy (CCE) Optimization to forecast real-world health conditions using unbalanced datasets derived from Chronic Blood Pressure Disorder case studies. Significantly, SPMR’s capability to deliver real-time preventive measures and treatments persists even without Internet or cloud connectivity. This circumvents the need to replicate Machine Learning (ML) models and associated procedures in local setups, thus streamlining operations. Comparative analysis against analogous models showcases the considerable effectiveness of our proposed model, notably enhancing accuracy by up to 8 to 18 percent. Moreover, both the overall F-score and the emergency class F-score exhibit marked improvements of 17% and 36%, respectively. These outcomes underscore SPMR’s pivotal role, especially during crises, emphasizing its significance in healthcare monitoring systems.

The Metaverse platform offers 3D immersive experiences through virtual reality, artificial intelligence, and blockchain technologies. Blockchain-based Metaverse has garnered recent interest due to its decentralization, anonymity, and other security features. Integrating blockchain into Metaverse promotes the formation of a shared ecosystem and provides digital asset tracking, privacy protection, and security in both virtual and physical environments. This paper explores the role of blockchain in creating and managing the Metaverse platform. It is intended to provide a helpful reference for researchers and practitioners in the field of the blockchain-based Metaverse. The main topics covered include the concept and characteristics of the Metaverse; its different use cases, architectures, and technologies; Metaverse as a service provider; Metaverse management; Metaverse users and data; and blockchain-based Metaverse in terms of roles, technologies, applications, and challenges. In addition, recent advances in the blockchain-based Metaverse and challenges such as scalability, interoperability, and edge resource sharing will be discussed. The paper will conclude by highlighting open research issues and future directions, including improving immersion, scalability, security, and integration with existing systems.

Backpropagation neural networks are commonly utilized to solve complicated issues in various disciplines. However, optimizing their settings remains a significant task. Traditional gradient-based optimization methods, such as stochastic gradient descent (SGD), often exhibit slow convergence and hyperparameter sensitivity. An adaptive stochastic conjugate gradient (ASCG) optimization strategy for backpropagation neural networks is proposed in this study. ASCG combines the advantages of stochastic optimization and conjugate gradient techniques to increase training efficiency and convergence speed. Based on the observed gradients, the algorithm adaptively calculates the learning rate and search direction at each iteration, allowing for quicker convergence and greater generalization. Experimental findings on benchmark datasets show that ASCG optimization outperforms standard optimization techniques regarding convergence time and model performance. The proposed ASCG algorithm provides a viable method for improving the training process of backpropagation neural networks, making them more successful in tackling complicated problems across several domains. As a result, the information for initial seeds formed while the model is being trained grows. The coordinated efforts of ASCG’s Conjugate Gradient and ASCG components improve learning and achieve global minima. Our results indicate that our ASCG algorithm achieves 21 percent higher accuracy on the HMT dataset and performs better than existing methods on other datasets(DIR-Lab dataset). The experimentation revealed that the conjugate gradient has an efficiency of 95 percent when utilizing the principal component analysis features, compared to 94 percent when using the correlation heatmap features selection approach with MSE of 0.0678.

New technologies drive educational shifts, transforming offline to online learning. This study investigates e-learning and cloud computing integration to understand synergies and their potential impact. The study addresses two primary research questions: the influence of e-learning on factors like architecture, software, performance, security, hardware, network, and virtual aspects, and the examination of cloud computing services and models such as SaaS, PaaS, IaaS, and S.O.A. The research aims to provide insights into how e-learning is incorporated in a cloud computing environment. The motivation behind this study is to investigate the intricate relationship between e-learning and cloud computing. By analyzing 154 scientific papers, the study delves into the specifics of this integration, highlighting trends and areas that have received more attention. The study examines e-learning in a cloud computing environment, focusing on architecture (27%), general topics (21%), software (19%), and performance (18%). Virtual environments have fewer security issues, while storage and network focus are more prevalent. Cloud computing services are mainly all services, with software as a service (18%), infrastructure as a service (17%), and platforms as a service (10%). Most studies are based on public clouds (74%), all other models (11%), and hybrid clouds (3%). The study examines e-learning integration in cloud computing, highlighting limitations in hybrid and private clouds, specialized infrastructure, and a gap in platforms and infrastructure offerings.