Artificial intelligence (AI) has recently become the focus of academia and practitioners, reflecting the substantial evolution of scientific production in this area, particularly during the COVID-19 era. However, there is no known academic work exploring the major trends and the extant and emerging themes of scientific research production of AI leading journals. To this end, this study is to specify the research progress on AI among the top-tier journals by highlighting the development of its trends, topics, and key themes. This article employs an integrated bibliometric analysis using evaluative and relational metrics to analyze, map, and outline the key trends and themes of articles published in the leading AI academic journals, based on the latest CiteScore of Scopus-indexed journals between 2020 and 2021. The findings depict the major trends, conceptual and social structures, and key themes of AI leading journals’ publications during the given period. This paper represents valuable implications for concerned scholars, research centers, higher education institutions, and various organizations within different domains. Limitations and directions for further research are outlined.
Although several prior studies have outlined and examined models associated with knowledge and innovation in different fields, the literature lacks any solid insights combining the Triple Helix model and the Spinner Innovation model and ascertaining their relevance to innovation. This article correspondingly presents an unprecedented alternative based on two innovation models, analyzing and structuring a process to innovate in different economic sectors. In doing so, this paper seeks to explore how this integration between Spinner Innovation and Triple Helix models could have a significant influence to improve system innovation. We collected data from the Scopus database spanning the period between 2012 and 2021 to study the integration of the models. The analysis identifies how these models differ but are nevertheless of complementary importance for developing regional and national economies through combining the “helices”, the “fidgets” and the framework integrating both models and their components to system innovation.
This study aims to develop a machine-learning-based attendance management system using face recognition and Raspberry Pi. The proposed system is composed of two main subsystems. The first is a Raspberry Pi, to be installed in each class, and the second is a web application fed by data from the Raspberry Pi. To take attendance, an instructor commands a Raspberry Pi camera through a web-based subsystem. Then, the camera takes a picture of the whole class and detects faces using trained Haar Cascades. It sends back a file with the class picture and Cartesian coordinates of the detected faces. The web application parses the file, looking for the coordinates of faces. For each Region of Interest, it uses the Support Vector Machine algorithm to recognize faces based on their HOG (Histogram of Oriented Gradients) features. The recognizer uses a pre-built dataset of that particular class containing the students’ personal photos, names and ID numbers. Features of each face were extracted using HOG and trained to construct the model over a given class of students. Once every detected face is recognized, the application generates a report for the instructor showing the list of students’ names and attendance status.
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