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Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords cooccurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details. INDEX TERMS Time-series classification, deep learning, remote sensing, signal processing, bibliometrics, research productivity.

The United Nations (UN) has launched several initiatives to promote the role of education in Sustainable Development Goals (SDGs) and set Goal 4 for quality education among other SDGs.The integrated Science, Technology, Engineering, and Mathematics (STEM) approach is a promising educational framework for sustainable development that improves education quality. In this study, a bibliometric analysis was conducted to evaluate the scientific results of the role of integrated STEM education specifically in improving the quality of education (SDG 4). A hundred and fifty publications, with an increasing trend in the number of documents each year, out of the total number of 74,879 documents related to "education quality" and 5,430 documents related to "STEM education" were chosen from the SCOPUS database. The study analyzes the growth and development of research activities in the area of "STEM education" and "Quality education" as reflected in the publications output in the time span of 27 years from 1993 to 2020. The publication

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Today's learners will face increasing global competition for college entrance and jobs after graduation. They need to succeed in work, life and citizenship, as well as skills that are necessary for 21st century. Teaching and learning's in the 21st century has to put the emphasis on the 4Cs, collaboration, critical thinking, creativity, and communication in the gender equality environments. Science, Technology, Engineering, and Math (STEM) Project Based Learning (PjBL) provides students to learn 4Cs skills. However, the role of gender in STEM PjBL is not clear well. So, this experimental study, run a project within an experimental group of 50 students and compare with another 57 students in the control group by employing Mixed-Design ANOVA, to evaluate changes in achievement performance differences between the genders? We observe there were not significant differences in performance of girls and boys in the high school by running a project and measuring students' achievement. Future study should cover the effect of gender differences on the STEM PjBL teaching method in the high school for preparing students for 21st century challenges in a broad range.