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result(s) for
"Kalota, Faisal"
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A Primer on Generative Artificial Intelligence
2024
Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.
Journal Article
Beyond Books: Student Perspectives on Emerging Technologies, Usability, and Ethics in the Library of the Future
by
Allam, Hesham
,
Witty, Grace
,
Schisler, Tyler
in
Academic libraries
,
academic performance
,
Artificial intelligence
2025
This research aims to understand the evolving role of academic libraries, focusing on student perceptions of current services and their vision for the future. Data was collected using a survey at a midwestern research university in the United States. The survey contained both quantitative and qualitative questions. The objective of the survey was to understand the current utilization of library services and students’ future visions for academic libraries. Qualitative and quantitative analysis techniques were utilized as part of the study. Thematic analysis was employed as part of the qualitative analysis, while descriptive and inferential analysis techniques were utilized in the quantitative analysis. The findings reveal that many students use libraries for traditional functions such as studying and accessing resources. There is also an inclination toward digitalization due to convenience, accessibility, and environmental sustainability; however, print materials remain relevant as well. Another finding was a lack of awareness among some students regarding available library services, indicating a need for better marketing and communication strategies. Students envision future libraries as technology-driven spaces integrating artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and innovative collaborative environments. Ethical considerations surrounding AI, including privacy, bias, and transparency, are crucial factors that must be addressed. Some of the actionable recommendations include integrating ethical AI, implementing digital literacy initiatives, conducting ongoing usability and user experience (UX) research within the library, and fostering cross-functional collaboration to enhance library services and student learning.
Journal Article
AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques
by
Allam, Hesham
,
Davison, Chris
,
Lazaros, Edward
in
Artificial intelligence
,
Computational linguistics
,
Forecasts and trends
2025
As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), to identify suicidal ideation from Twitter data. A predictive model was developed to process social media posts in real time, using NLP and sentiment analysis to detect textual and emotional cues associated with distress. The model aims to identify potential suicide risks accurately, while minimizing false positives, offering a practical tool for targeted mental health interventions. The study achieved notable predictive performance, with an accuracy of 85%, precision of 88%, and recall of 83% in detecting potential suicide posts. Advanced preprocessing techniques, including tokenization, stemming, and feature extraction with term frequency–inverse document frequency (TF-IDF) and count vectorization, ensured high-quality data transformation. A random forest classifier was selected for its ability to handle high-dimensional data and effectively capture linguistic and emotional patterns linked to suicidal ideation. The model’s reliability was supported by a precision–recall AUC score of 0.93, demonstrating its potential for real-time mental health monitoring and intervention. By identifying behavioral patterns and triggers, such as social isolation and bullying, this framework provides a scalable and efficient solution for mental health support, contributing significantly to suicide prevention strategies worldwide.
Journal Article