Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.

Intelligent education research has become a research hotspot in recent years. The Citespace software that operates a graph visualization function was used to clarify the current situation, hot spots, and evolutionary trends of intelligent education research development; the authors, institutions, and countries engaged in intelligent education research, as well as the basic knowledge structure, main keywords, citation clustering, dual-map overlay of journals and citation emergence of intelligent education research. The results show that the annual number of publications in the field has shown an upward trend since 2010, with strong communication among research institutions and countries, but weak communication among researchers. Among them, the United States is the center of the global collaborative network of intelligent education research. The basic knowledge structure of intelligence education research is mainly composed of Classroom Management, Evaluation Index, 5G Network, and Big Data Analytics. The dual-map overlay analysis of journals shows that the core areas of intelligence education are increasing, and the analysis of keywords and cited literature shows that Intelligence Tutoring System, AI system, Students and Education, Model, and System are high-frequency words with high-intensity burstness. In addition, research on intelligent education is characterized by multi-country, multi-field, and multi-disciplinary integration, and the adoption of Big Data, Distance Education Technology and Artificial Intelligence Technology to provide scientific support for teaching and learning will become the key research content in this field in the future.

Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.

With the rapid development of the global digital knowledge economy, educational activities are facing more challenges. Sustainable development education aims to cultivate students’ thinking ability to better integrate with the contemporary world view, so classroom practice should involve innovative teaching and learning. The goal of sustainable development education is to cultivate talents with high-level thinking and sustainable development abilities. The concept of deep learning emphasizes mobilizing students’ internal motivation, focusing on problem-solving ability, improving students’ critical thinking level, and developing students’ lifelong learning ability. The concept of deep learning has evolved with the times. The introduction of the concept of deep learning in teaching can enhance students’ understanding of the nature of knowledge, cultivate students’ high-level thinking, and enable students to achieve better learning results. Integrating the concept of deep learning into teaching has extremely important significance and value for sustainable development education. It has become a hot topic in the world to comprehensively analyze the research status of deep learning and explore how deep learning can help education achieve sustainable development. In this study, CiteSpace (6.1.R2) visualization analysis software was used to visualize and quantitatively analyze the literature on deep learning in the Social Science Citation Index (SSCI). The visualized analysis is conducted on the annual publication amount, authors, institutions, countries, keywords, and high-frequency cited words of deep learning, to obtain the basic information, development status, hot spots, and evolution trends of deep learning research. The results show that the annual publication volume of deep learning is on the rise; deep learning research has entered a rapid growth stage since 2007; the United States has published the most papers and is the center of the global deep learning research collaboration network; the countries involved in the study were often interconnected, but the institutions and authors were relatively dispersed; research in the field of deep learning mainly focuses on concept exploration, influencing factors, implementation strategies and effectiveness of deep learning; learning method, learning strategy, curriculum design, interactive learning environment are the high-frequency keywords of deep learning research. It can be seen that deep learning research has the characteristics of transnationality, multidisciplinary nature and multi-perspective. In addition, this paper systematically analyzes the latest progress in global deep learning research and objectively predicts that using intelligent technology to design appropriate teaching and learning scenarios and evaluation methods may become the future development trend of deep learning. The research results of this paper will help readers to have a comprehensive understanding of deep learning research, provide deeper and more targeted resources for integrating deep learning concepts into teaching, and promote better sustainable development of education.