An automated approach to identify sarcasm in low-resource language.

Sarcasm detection has emerged due to its applicability in natural language processing (NLP) but lacks substantial exploration in low-resource languages like Urdu, Arabic, Pashto, and Roman-Urdu. While fewer studies identifying sarcasm have focused on low-resource languages, most of the work is in English. This research addresses the gap by exploring the efficacy of diverse machine learning (ML) algorithms in identifying sarcasm in Urdu.

AI based predictive acceptability model for effective vaccine delivery in healthcare systems.

Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability.

Detecting refactoring type of software commit messages based on ensemble machine learning algorithms.

Refactoring is a well-established topic in contemporary software engineering, focusing on enhancing software's structural design without altering its external behavior. Commit messages play a vital role in tracking changes to the codebase. However, determining the exact refactoring required in the code can be challenging due to various refactoring types.

Investigating Students' Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach.

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia.

A Hybrid Approach Toward COVID-19 Pandemic Modeling in Saudi Arabia Using the Modified Susceptible-Exposed-Infectious-Recovered Model and Open Data Sources.

The coronavirus disease 2019 (COVID-19) pandemic has caused the world to operate uncharacteristically for almost the last two years. Governments across the globe have taken different control measures to eradicate it. The Oxford COVID-19 Government Response Tracker (OxCGRT) provides open access data for different countries on 20 control measures, including numerous aggregated indices.

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm.

Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time.

Implementing Fuzzy AHP and FUCOM to evaluate critical success factors for sustained academic quality assurance and ABET accreditation.

The Accreditation Board for Engineering and Technology (ABET) accredits the tertiary education programs in the areas of applied and natural science, computing, engineering, and engineering technology. ABET offers accreditation in the United States and other regions in the world that lack such entities such as Gulf Cooperative Counties (GCC). Though ABET accreditation is voluntary, graduates of the ABET-accredited programs are considered equivalent in knowledge, behaviors, and attitude with global standards.