An analysis of publishing trends and bbibliographic analysis data was conducted to critically analyse the research landscape regarding the utilisation of machine learning techniques in the field of ...energy and power production (EPP). The Elsevier Scopus database and the PRISMA methodology were utilised to locate and evaluate the published papers. The bibliometric analysis software package RStudio (Biblioshiny) was employed to examine the most significant sources, authors, and institutions with the highest productivity. The findings indicated that a total of 653 documents were published on the subject, consisting of conference proceedings (41.8%) and articles (50.8%), spanning the years 2012 to 2023. An review of publishing patterns indicated a significant increase in the number of publications, rising from 3 to 190, representing a growth of 5,000% over the same period. This surge can be attributed to the expanding scientific interest and the influence of research on the subject. The stakeholder analysis identified Boumaiza A, Sanfilippo A, and Wang X as the leading authors/researchers in the field. Additionally, it revealed that China, India, and the United States are the most actively involved nations in this area. In contrast, the primary financial organisation that actively supports study on the subject is the National Natural Science Foundation of China (NSFC). Overall, the study demonstrated that the utilisation of machine learning in evidence-based policy and practice (EPP) is a dynamic and interdisciplinary field of research that has the potential to make significant contributions to both research and society.
The current research set out to find out how different kinds of collaborative training affected students' ability to make socioscientific decisions and their metacognitive abilities in a science ...classroom setting. Both presenting socioscientific issues and proposing and evaluating solutions to such difficulties are part of socioscientific decision making. We looked at two different collaborative training techniques that were built using the IMPROVE method and had different embedded metacognitive instructions. Three hundred and thirty-five seniors from three different high schools participated: one in a traditional collaborative learning context (COLED), another in which metacognitive questions were embedded (COLED+EMB), and a control group that did not receive any kind of intervention. On both aspects of socioscientific decision making, the results show that students in the two training circumstances fared better than students in the control group. Students in the COLED+EMB condition were not more successful than those in the COLED condition, nevertheless. All conditions showed an improvement in students' learning outcomes on the regulatory component of metacognition over time. The posttest results showed that students in the COLED+EMB condition performed the best on average, but these findings were not statistically significant. The discussion centres on the potential consequences of incorporating metacognitive instruction into scientific courses.
The objective of this paper is to critically examine the publication's trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis ...(BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of Technology (India). However, the top country and research funder are; India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics.
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