22 research outputs found

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

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    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

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    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

    Hide Secret Information in Blocks: Minimum Distortion Embedding

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    In this paper, a new steganographic method is presented that provides minimum distortion in the stego image. The proposed encoding algorithm focuses on DCT rounding error and optimizes that in a way to reduce distortion in the stego image, and the proposed algorithm produces less distortion than existing methods (e.g., F5 algorithm). The proposed method is based on DCT rounding error which helps to lower distortion and higher embedding capacity.Comment: This paper is accepted for publication in IEEE SPIN 2020 conferenc

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

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    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

    Enhancing GI Cancer Radiation Therapy: Advanced Organ Segmentation with ResECA-U-Net Model

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    This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the U-Net and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Center, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely. Doi: 10.28991/ESJ-2024-08-03-012 Full Text: PD
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