Portrait of Syed Tangim Pasha

Syed Tangim Pasha

  • Collaborator, HCI Wing

Lecturer, Department of Computing and Information System, Daffodil International University

Publications (9)

Jahanggir Hossain Setu, Syed Tangim Pasha, Nabarun Halder, Eshtiak Ahmed, Ashraful Islam, M Ashraful Amin

Multi-Modal Deep Learning for Myocardial Infarction Detection Using ECG Signals and Images

Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1626-1632, 2025

Conference PaperDOIURL
  • myocardial infarction detection
  • ECG signals
  • PTB-XL dataset
  • attention-based feature fusion
  • multimodal deep learning
  • LSTM
  • convolutional neural networks
  • ECG images
  • SDG 3Good Health and Well-being

Tasmia Tahmida, Syed Tangim Pasha, Mehedi Hassan, Ashraful Islam, M. Ashraful Amin

Integrating Supervised and Self-Supervised Models for an Enhanced PCOS Detection: A Data-Driven Approach with Machine Learning Perspective

2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC), pp. 499-503, 2025

Conference PaperDOIURL
  • polycystic ovary syndrome
  • BYOL
  • Autoencoder
  • self-supervised learning
  • PCOS diagnosis
  • Gaussian naive Bayes
  • Supervised learning
  • SimCLR
  • unlabeled data
  • support vector classifier
  • SDG 3Good Health and Well-being
  • SDG 5Gender Equality

Mahmudul Islam, Syed Tangim Pasha, Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, M. Ashraful Amin

Comparative Analysis of Machine Learning Models for Diabetes Prediction: A Cross-Dataset Study of Bangladeshi and Pima Indian Populations

2024 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2024), pp. 1-5, 2024

Conference PaperDOIURL
  • diabetes prediction
  • naive Bayes
  • diabetes classification
  • Bangladeshi population
  • Random forest
  • class imbalance
  • Pima Indians diabetes dataset
  • cross-dataset comparison
  • feature distribution
  • SDG 3Good Health and Well-being

Syed Tangim Pasha, Nabarun Halder, Jahanggir Hossain Setu, Ashraful Islam, Md Zahangir Hossain

Physiological Signal Data-Driven Workplace Stress Detection Among Healthcare Professionals Using BiLSTM-AM and Ensemble Stacking Models

IEEE 2024 International Advances in Science & Engineering Technology (ASET) Multi-Conferences, Dubai, pp. 1-10, 2024

Conference PaperDOIURL
  • electrodermal activity
  • skin temperature
  • nurses
  • Heart rate
  • healthcare workers
  • psychological well-being
  • stress classification
  • physiological signals
  • wearable sensors
  • stacking ensemble
  • SDG 3Good Health and Well-being