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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.

To better understand the latest developments in global science, technology, engineering, and mathematics (STEM) education research, this study collected STEM education research materials to sort out the development of STEM education as a whole, so as to get a clearer path and trend of STEM education development. This study conducted a visualization and quantitative analysis of the literature on STEM education research in Science Citation Index Extended (SCI-E) and Social Science Citation Index (SSCI) using the CiteSpace (5.8.R3) tool. First, the basic information of STEM education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents and research hotspots of this research were analyzed by keyword co-occurrence and keyword time zone mapping. Finally, the research frontiers and development trends are presented through co-citation clustering and high-frequency keyword bursts. The research hotspots are focused on engineering education, teachers’ professional development, and gender differences. The research frontiers are mainly related to teacher professional development, 21st century skills, early childhood creativity, and gender differences. This study systematically analyzes the latest developments in global STEM education research, which is beneficial for readers to understand the full picture of STEM education research so that researchers can conduct more in-depth studies and promote better development of STEM education. The number of analyzed literature is limited. We only analyzed articles from SSCI and SCI-E databases, and the articles were written in English. In addition, we only analyzed the literature and lacked empirical studies on the findings of the literature.

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.

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Traditional texture cluster algorithms are frequently used in engineering; however, despite their widespread application, these algorithms continue to suffer from drawbacks including excessive complexity and limited universality. This study will focus primarily on the analysis of the performance of a number of different texture clustering algorithms. In addition, the performance of traditional texture classification algorithms will be compared in terms of image size, clustering number, running time, and accuracy. Finally, the performance boundaries of various algorithms will be determined in order to determine where future improvements to these algorithms should be concentrated. In the experiment, some traditional clustering algorithms are used as comparative tools for performance analysis. The qualitative and quantitative data both show that there is a significant difference in performance between the different algorithms. It is only possible to achieve better performance by selecting the appropriate algorithm based on the characteristics of the texture image.