collaborators

10 papers

cs.CL2026

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…

eess.SP2025

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…

cs.CR2025

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…

cs.SE2025

Secure coding for web applications: Frameworks, challenges, and the role of LLMs

Kiana Kiashemshaki, Mohammad Jalili Torkamani, Negin Mahmoudi

Secure coding is a critical yet often overlooked practice in software development. Despite extensive awareness efforts, real-world adoption remains inconsistent due to organization…

cs.CL2025

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…

cs.CL2025

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…