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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Al-Shargabi, Amal A.
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Educational impact and ethical considerations in using Chatbots in Academia Ibrahim, Dina M.; Al-harbi, Njood K.; Al-Shargabi, Amal A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1150-1167

Abstract

Chatbots are getting better every day due to the advancements in their capabilities in today’s technological age. This study aims to assess the efficacy of ChatGPT-4 and Gemini in producing scientific articles. Two types of prompts are given: direct questions and complete scenarios. Subsequently, we evaluate the educational and ethical aspects of the produced material by employing statistical analysis. We verify the credibility of references, detect any instances of plagiarism, and ensure the precision of the articles generated by the chatbot. In addition, we utilize topic modeling to assess the extent to which the content of the articles corresponds to the specified topic. According to the findings, Gemini outperformed ChatGPT-4, specifically in scenario questions, where it achieved an accuracy rate of 85%, while ChatGPT-4 only achieved 35% accuracy.
Recognizing geographical locations using a GAN-based text-to-image approach Ibrahim, Dina M.; Al-Shargabi, Amal A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1168-1182

Abstract

Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN’s generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
Co-Authors Al-harbi, Njood K. Ibrahim, Dina M.