Ghada S. Mohammed

Background: Blockchain, mainly known for supporting cryptocurrencies, has a much broader role, as seen in this paper. These fundamental features of decentralization and immutability guarantee improved security and transparency in multiple spheres of human life. Objective: The article seeks to review current literature on new prospects of using blockchain as a secure way of sharing data with the purpose of establishing its advantages and disadvantages in this field. Methods: Relevant academic articles and papers published in the last 5 years were considered, and research cases of blockchain applications in numerous fields including healthcare, finance, supply chain, etc. This incorporates a review of blockchain within the capacity of data integrity, confidentiality, and availability. Results: The results show that blockchain can greatly improve the security and credibility of data in data sharing by reducing common vulnerability and offering reliable traceability. The technology ensures safe transactions of data and minimizes the possibilities of manipulation of data in fields which involve sensitive data processes. Conclusion: Opportunities for the blockchain for secure data sharing are demonstrated across several industries through current advancements. However, it also has limitations that includes size ability, compatibility and legislation issues which still has to be solved. The study should therefore consider the following recommendations about the barriers outlined above in order to enhance the application of blockchain in secure data sharing in the future.

Background: Machine learning (ML) has developed significantly over the years, changing several industries through the use of automation and Big Data. By building better next-generation machine learning models, AI’s future has the potential of improving on existing problematic methods such as scalability, interpretability, and generalization. Objective: This article examines about how new generation of ML models are developed and used to explain about the capabilities of AI in different fields. In particular, it is focused on changes in structural models, certain methods of training them, and the application of brand-new technologies as quantum computing. Methods: A review of the state of the art and several case studies were carried out with regard to the latest work being done on different types of ML algorithms such as transformer models, reinforcement learning, and Neural Architecture Search. Moreover, the given models were tested in experiments concerning the applicability of these models in tasks including image recognition, natural language processing, and in autonomous systems. Results: The next-gen models, thereby outperformed the traditional models in terms of accuracy, computational speed, and flexibility. The identified benefits were decreased training time, better interpretability, and better performance with multi-modal and cross-domain tasks. Conclusion: These new generation of ML models are the game changers in AI development solving previous challenges while providing opportunities across numerous sectors. In this vein, further research in this field is needed to achieve AI’s solving of problems.