4 citations · 4 across the 10 of their papers we have counts for
10 papers
Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI
Meysam Shirdel Bilehsavar, Negin Mahmoudi, Mohammad Jalili Torkamani +1
Sentiment analysis focuses on identifying the emotional polarity expressed in textual data, typically categorized as positive, negative, or neutral. Hate speech detection, on the o…
Automated Tinnitus Detection Through Dual-Modality Neuroimaging: EEG Microstate Analysis and Resting-State fMRI Classification Using Deep Learning
Kiana Kiashemshaki, Sina Samieirad, Sarvenaz Erfani +3
Objective: Tinnitus affects 10-15% of the population yet lacks objective diagnostic biomarkers. This study applied machine learning to EEG and fMRI data to identify neural signatur…
Secure and Scalable Blockchain Voting: A Comparative Framework and the Role of Large Language Models
Kiana Kiashemshaki, Elvis Nnaemeka Chukwuani, Mohammad Jalili Torkamani +1
Blockchain technology offers a promising foundation for modernizing E-Voting systems by enhancing transparency, decentralization, and security. Yet, real-world adoption remains lim…
Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets
Meysam Shirdel Bilehsavar, Negin Mahmoudi, Mohammad Jalili Torkamani +1
Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentim…
Simulating a Bias Mitigation Scenario in Large Language Models
Kiana Kiashemshaki, Mohammad Jalili Torkamani, Negin Mahmoudi +1
Large Language Models (LLMs) have fundamentally transformed the field of natural language processing; however, their vulnerability to biases presents a notable obstacle that threat…
Automated Bug Triaging using Instruction-Tuned Large Language Models
Kiana Kiashemshaki, Arsham Khosravani, Alireza Hosseinpour +1
Bug triaging, the task of assigning new issues to developers, is often slow and inconsistent in large projects. We present a lightweight framework that instruction-tuned large lang…