Use of Twitter among College Students for Academics: A Mixed-Methods Approach

For almost a decade, Twitter use and its impact on students\u27 academic performance have been explored by many researchers. Despite growing scholarly interest, studies have been mostly quantitative in nature. The findings of previous studies are conflicting; thus, an in-depth study is needed to determine how and what impacts college students\u27 academic performance (i.e., GPA) when they spend time on Twitter. The purpose of this study was to understand the effects of Twitter use on college students\u27 academic performance. The present study shows that individual analysis techniques, such as quantitative or qualitative tools, are not enough to understand the underlying relationship. Therefore, a mixed-method approach (i.e., correlation and discourse analysis) was used to analyze the research data. Undergraduate students responded (N = 498) to a set of items along with some open-ended questions (n = 121). The results of this study indicate that how students use Twitter matters more than the amount of time they spend using it for their studies

An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables

This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities

An AI-Based Framework for Translating American Sign Language to English and Vice Versa

Abstract: In this paper, we propose a framework to convert American Sign Language (ASL) to English and English to ASL. Within this framework, we use a deep learning model along with the rolling average prediction that captures image frames from videos and classifies the signs from the image frames. The classified frames are then used to construct ASL words and sentences to support people with hearing impairments. We also use the same deep learning model to capture signs from the people with deaf symptoms and convert them into ASL words and English sentences. Based on this framework, we developed a web-based tool to use in real-life application and we also present the tool as a proof of concept. With the evaluation, we found that the deep learning model converts the image signs into ASL words and sentences with high accuracy. The tool was also found to be very useful for people with hearing impairment and deaf symptoms. The main contribution of this work is the design of a system to convert ASL to English and vice versa