collaborators

6 papers

cs.CV2025

Heart Disease Prediction using Case Based Reasoning (CBR)

Mohaiminul Islam Bhuiyan, Chan Hue Wah, Nur Shazwani Kamarudin +2

This study provides an overview of heart disease prediction using an intelligent system. Predicting disease accurately is crucial in the medical field, but traditional methods rely…

cs.CL2025

Enhanced Suicidal Ideation Detection from Social Media Using a CNN-BiLSTM Hybrid Model

Mohaiminul Islam Bhuiyan, Nur Shazwani Kamarudin, Nur Hafieza Ismail

Suicidal ideation detection is crucial for preventing suicides, a leading cause of death worldwide. Many individuals express suicidal thoughts on social media, offering a vital opp…

cs.CL2025

Detecting Suicidal Ideation in Text with Interpretable Deep Learning: A CNN-BiGRU with Attention Mechanism

Mohaiminul Islam Bhuiyan, Nur Shazwani Kamarudin, Nur Hafieza Ismail

Worldwide, suicide is the second leading cause of death for adolescents with past suicide attempts to be an important predictor for increased future suicides. While some people wit…

cs.CL2025

Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content

Adi Danish Bin Muhammad Amin, Mohaiminul Islam Bhuiyan, Nur Shazwani Kamarudin +2

The rapid evolution of the gaming industry, driven by technological advancements and a burgeoning community, necessitates a deeper understanding of user sentiments, especially as e…

cs.CY2025

Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation

Mohaiminul Islam Bhuiyan, Nur Shazwani Kamarudin, Nur Hafieza Ismail

This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to…

cs.CV2024

Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation

Niful Islam, Mohaiminul Islam Bhuiyan, Jarin Tasnim Raya +4

Brain tumors are one of the most common diseases that lead to early death if not diagnosed at an early stage. Traditional diagnostic approaches are extremely time-consuming and pro…