Generative AI, Imaging & IoT
FAST-NUCES Lahore provides an infrastructure based on advanced provisioning frameworks such as Kubernetes and Kubeflow. Dr. Asma Naseer, along with core team consisting of Dr. Asma Ahmad and Dr. Aamir Wali, oversees the GAIT lab that grants students access to the aforementioned infrastructures which adequately caters to students conducting research for their master's theses and Final Year Projects (FYPs). The lab focuses on the following key research domains:
- Digital Image Processing (DIP)
- Internet of Things (IoT)
- Machine learning (ML)
- Generative Artificial Intelligence (GAI)
Overall, the lab provides a valuable platform for students to delve into these research areas and gain practical experience in utilizing cutting-edge technologies for their academic pursuits.
Research Group Members
- Dr. Asma Naseer
- Dr. Aamir Wali
- Dr. Asma Ahmad
Focus of GAIT
The primary objective of the Generative AI, Imaging & IoT (GAIT) research group is to investigate and address the challenges and issues within the domains of Imaging, IoT, and machine learning. Given the growing interest among graduate and post-graduate students in pursuing research in Imaging, machine learning, and IoT, our group is dedicated to maintaining a strong foundation of research. Currently, our group focuses on various problems involving generative AI, medical imaging, IoT, NLP, and time series analysis, encompassing activity recognition, imaging, sign language recognition, EEG and ECG classification.
Current Students
- Atia Akram (PhD)
- Hafiz Hamza (PhD)
- Manaal Iftikar (MS)
- Saad Ather (MS)
- Usman Anwar (MS)
- Aruza Asif (MS)
- Mubeen Tariq (MS)
- Hassan Masood (MS)
- Mudassir Saeed (MS)
- Syed Mujtaba Haider (MS)
- Sana Ejaz (MS)
- Kinza Shahzad (MS)
- Tanzeela Shakeel (Research Assistant)
- Muhammad Shmoon (Research Assistant)
Research Projects
- Smart Agriculture
- Occupancy detection via thermal sensors for energy consumption reduction Smart activity tracking
- Technical content summarization using deep learning techniques
- Brain-computer interface to control object movement using EEG headset
- Medical images augmentation using a new GAN model for improved diagnosis of diseases
- Computer-Aided brain tumor diagnosis: Performance evaluation of deep learner CNN using augmented brain MRI
- Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs.
- Generative adversarial networks for speech synthesis.
- Revolutionizing Scoliosis assessment: U-Net empowered automated Cobb's angle estimation
- Entropy and memory aware active transfer learning in smart sensing systems
- Unobtrusive smartphone authentication
- Skin Lesion Segmentation with Generative AI
- Skin Cancer Classification
- Weed Detection and Classification
- 3D Avatar Construction using Generative AI and Deep Learning
Journal Publications
2023
1. Tamoor, M., Naseer, A., Khan, A., Zafar, K. Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods. Diagnostics 2023, 13, 2684. –Impact Factor 3.99
2. Wali, A., Naseer, A., Tamoor, M., Gilani, S. Recent progress in digital image restoration techniques: A review. Digital Signal Processing 2023, 141, 104187 – Impact Factor 3.38
3. Ur Rehman, Aziz, Naseer, A., Karima, S.; Tamoor, M.; Naz, S.; et al. Deep learning classifiers for computer-aided diagnosis of multiple lungs disease. Journal of X-Ray Science and Technology, pp. 1–Impact Factor 2.75
4. Naseer, A.; et.al., Occupancy detection via thermal sensors for energy consumption reduction. Multimedia Tools and Applications 2023, pp. 1–14. Impact Factor 2.75
5. Wali, A.; Ahmad, M.; Naseer, A.; Tamoor, M.; Gilani, S. StynMedGAN: Medical images augmentation using a new GAN model for improved diagnosis of diseases. Journal of Intelligent & Fuzzy Systems 2023, pp. 1–18. Impact factor 1.73
6. Wali, A.; Ali, S.; Naseer, A.; Karim, S.; Alamgir, Z. Computer-aided COVID-19 diagnosis: a possibility? Journal of Experimental & Theoretical Artificial Intelligence 2023, pp. 1–19. Impact factor 2.34
7. Chughtai, I.T.; Naseer, A.; Tamoor, M.; Asif, S.; Jabbar, M.; Shahid, R. Content-based image retrieval via transfer learning. Journal of Intelligent & Fuzzy Systems 2023, pp. 1–26. Impact factor 1.73
8. Raza, N.; Naseer, A.; Tamoor, M.; Zafar, K. Alzheimer Disease Classification through Transfer Learning Approach. Diagnostics 2023, 13, 801. Impact factor 3.
2022
9. Naseer, A.; Tamoor, M.; Azhar, A. Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs. Journal of X-ray Science and Technology 2022, 30, 89–109. Impact factor1.5
10. Naseer, A.; Hussain, S.; Zafar, K.; Khan, A. A novel normal to tangent line (NTL) algorithm for scale invariant feature extraction for Urdu OCR. International Journal on Document Analysis and Recognition (IJDAR) 2022, 25, 51–66. Impact factor 2.44
11. Malik, Y.S.; Tamoor, M.; Naseer, A.; Wali, A.; Khan, A. Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation. Journal of X-Ray Science and Technology 2022, pp. 1–16. Impact factor 2.44
12. Naseer, A.; Zafar, K. Meta-feature based few-shot Siamese learning for Urdu optical character recognition. Computational Intelligence 2022, 38, 1707–1727. Impact factor 2.8
13. M Fayyaz, AA Farhan, AR Javed, Thermal Comfort Model for HVAC Buildings Using Machine Learning, Arabian Journal for Science and Engineering 47 (2), 2045-2060(13),2022
2021
14. Naseer, A.; Yasir, T.; Azhar, A.; Shakeel, T.; Zafar, K. Computer-aided brain tumor diagnosis: performance evaluation of deep learner CNN using augmented brain MRI. International Journal of Biomedical Imaging 2021.
15. AR Javed, LG Fahad, AA Farhan, S Abbas, G Srivastava, RM Parizi, Automated cognitive health assessment in smart homes using machine learning, Sustainable Cities and Society 65, 102572 (113), 2021
16. SA Khan, AA Farhan, LG Fahad, SF Tahir, Personal productivity monitoring through smartphones, Journal of Ambient Intelligence and Smart Environments 12 (4), 327-341
2019
17. Naseer, A.; Zafar, K. Meta features-based scale invariant OCR decision making using LSTM-RNN. Computational and Mathematical Organization Theory 2019, pp. 165–183. Impact factor 1.2
2018
18. Naseer, A.; Zafar, K. Comparative analysis of raw images and meta feature based Urdu OCR using CNN and LSTM. International Journal of Advanced Computer Science and Applications 2018.