This study presents a hybrid analytical framework that enhances project selection by achieving reasonable predictive accuracy through the integration of expert judgment and modern artificial intelligence (AI) techniques. Using an enterprise-level dataset of 10,000 completed softw…
Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is propose…
The detection of abusive language in Roman Urdu is important for secure digital interaction. This work investigates machine learning (ML), deep learning (DL), and transformer-based methods for detecting offensive language in Roman Urdu comments collected from YouTube news channel…
Hate speech is a complex topic that transcends language, culture, and even social spheres. Recently, the spread of hate speech on social media sites like Facebook has added a new layer of complexity to the issue of online safety and content moderation. This study seeks to minimiz…
With the rapid expansion of social media, detecting offensive language has become critically important for healthy online interactions. This poses a considerable challenge for low-resource languages such as Roman Urdu which are widely spoken on platforms like Facebook. In this pa…
Thisstudy introduces a new model for detecting insults in Roman Urdu, filling an important gap in natural language processing (NLP) for low-resource languages. The transliterated nature of Roman Urdu also poses specific challenges from a computational linguistics perspective, inc…