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http://dx.doi.org/10.22937/IJCSNS.2022.22.1.10

Evaluating AI Techniques for Blind Students Using Voice-Activated Personal Assistants  

Almurayziq, Tariq S (Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il)
Alshammari, Gharbi Khamis (Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il)
Alshammari, Abdullah (Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il)
Alsaffar, Mohammad (Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il)
Aljaloud, Saud (Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.1, 2022 , pp. 61-68 More about this Journal
Abstract
The present study was based on developing an AI based model to facilitate the academic registration needs of blind students. The model was developed to enable blind students to submit academic service requests and tasks with ease. The findings from previous studies formed the basis of the study where functionality gaps from the literary research identified by blind students were utilized when the system was devised. Primary simulation data were composed based on several thousand cases. As such, the current study develops a model based on archival insight. Given that the model is theoretical, it was partially applied to help determine how efficient the associated AI tools are and determine how effective they are in real-world settings by incorporating them into the portal that institutions currently use. In this paper, we argue that voice-activated personal assistant (VAPA), text mining, bag of words, and case-based reasoning (CBR) perform better together, compared with other classifiers for analyzing and classifying the text in academic request submission through the VAPA.
Keywords
Voice-activated personal assistant; Text mining; Classification;
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