![]() till now.(4) The emerging trends mainly include “social influence modeling”, “mobile media cloud”, “video surveillance system”, “semantic relations”, “privacy”, “internet of thing”, “precision medicine”, “parallel massive clustering”, etc. are the research hotspots (3) the research focus evolved mainly from “basic security problems” and “algorithm problems” in the early, to technical problems, then to the applications and social impacts, and to “mobile internet”, “cloud”, “data screening”, “payment security”, etc. The results show that: (1)Multimedia Big Data research has spread across the globe, especially in the United States, China and some European countries (2)"big data”, “web application”, “data mining”, “virtual screening”, “cloud service”, “structure-activity relationship”, “similarity search problems”, “concept modeling”, etc. Rexel sente software#Based on the references from SCI-EXPANDED(SCIE), SSCI, CPCI-S, CPCI-SSHSI and arXiv databases in 2008–2017, the hotspots and emerging trends of Multimedia Big Data were identified for the first time by visualizing the co-cited references network, co-occurrence keywords network, burst references, burst keywords, Dual-Map Overlays network and Timeline networks with the information visualization software CiteSpaceV, Google Fusion Tables and Carrot2. However, a visualization research on the hotspots and trends of Multimedia Big Data through scientometric is still lacking. ![]() Multimedia Big Data, known as the biggest big data, is becoming the forefront of big data research. This study is the first to provide an overall perspective of hotspots and trends in the research on AI in the cyber security domain. Five evaluation factors are used to judge the hotspots and trends of this domain and a heat map is used to identify the areas of the world that are generating research on AI applications in cyber security. This study visualizes the structural changes, hotspots and emerging trends in AI studies. Many research hotspots such as those on face recognition and deep neural networks for speech recognition may create future hotspots on emerging technology, such as on artificial intelligence systems for security. Artificial neural networks, an AI technique, gave birth to today’s research on cloud cyber security. Many countries, institutions and authors are densely connected through collaboration and citation networks. This study promotes the development of theory about AI in cyber security, helps researchers establish research directions, and provides a reference that enterprises and governments can use to plan AI applications in the cyber security industry. Structural changes have been observed in cyber security since the emergence of AI. The recent literature focuses on AI’s application to cyber security but lacks visual analysis of AI applications. This paper also highlights the potential of the graph analytics approach to explore peer learning group dynamics and interaction patterns among students to maximize their teaching and learning experience.Īrtificial Intelligence (AI) provides instant insights to pierce through the noise of thousands of daily security alerts. Based on the results, it was found that five groups were formed during the physical interaction throughout the peer learning process, with at least one student showing the potential to become a peer leader in each group. Once the model and graph visualization were developed, findings from centrality analysis and community detection were conducted to identify peer leaders who can facilitate and teach their peers. The physical interactions among students were captured through an online Google form and represented in a graph model. ![]() The experiment was conducted during a mathematics tutorial class. This paper complements existing works by proposing a framework for exploring students’ physical interaction in peer learning based on the graph analytics modeling approach focusing on both centrality and community detection, as well as visualization of the graph model for more than 50 students taking part in group discussions. ![]() Hence, more data-driven analysis needs to be performed to investigate the physical interaction in peer learning. However, observation and interviews are often used to investigate peer group learning dynamics from a qualitative perspective. Physical interaction in peer learning has been proven to improve students’ learning processes, which is pertinent in facilitating a fulfilling learning experience in learning theory. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |