A Bibliometric Analysis of Generative Design, Algorithmic Design, and Parametric Design in Architecture

This research aims to display, compare, and analyze the keywords related to parametric design, generative design, and algorithmic design. Digital design has become increasingly inseparable from architects; thus, 3D modeling software has become a necessity for architects. The digital workflow has put computational design—generative design, algorithmic design, and parametric design—into importance. There are emerging trends for the past decade, and a bibliometric analysis can display information about trends in the literature. Literature trends may provide insight into the direction of computational design development. This study uses a bibliometric analysis with VOSviewer and data from Lens to identify the trends from 2011 to 2021. The result indicates several trends: artificial intelligence, computation, machine learning, visualization, and internet technology. The trend analysis needs to be continued in other computational design categories to find continuity in the findings

Application of semantic analysis and LSTM-GRU in developing a personalized course recommendation system

The selection of elective courses based on an individual's domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual's domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student's domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.Web of Science1221art. no. 1079