Dr. Nur Alom Talukdar
Qualification : Ph.D. (North-Eastern Hill University)
Designation : Asst. Professor
Details of Educational Qualification:
| Course | Specialization | Group | College Name/University | Year of Passing |
|---|---|---|---|---|
| Ph.D. | CSE | CSE | North-Eastern Hill University, Meghalaya | 2021 |
| M.Tech. | IT | IT | Assam University, Silchar | 2015 |
| B.Tech. | CSE | CSE | Gauhati University, Guwahati | 2013 |
List of Publications
| S.No | Title of the Paper | Full Details of Journal Name / Conference Name, Volume number, page number, Date |
|---|---|---|
| 1 | Semi-supervised Clustering using kernel Induced Rough Fuzzy C-means for Brain Tissue Segmentation. | Pattern Recognition and Image Analysis. 31(1), 2021 (Accepted) (Springer). Indexed in: Scopus, ACM, UGC CARE, etc. |
| 2 | Robust Brain Magnetic Resonance Image Segmentation using Modified Rough-Fuzzy C-Means with Spatial Constraints. | Applied Soft Computing. 85(2019), 01-17, 2019. (Elsevier, IF: 5.473). Indexed in: SCI, Scopus, etc. |
| 3 | Semi-supervised Rough Fuzzy Clustering for Brain MRI Segmentation. | International Journal of Research in Advent Technology (IJRAT). (UGC listed), Vol. 7, No. 3, 2019. |
| 4 | Brain Tissue Segmentation using Improved Kernelized Rough-Fuzzy C-Means with Spatio-Contextual Information from MRI. | Magnetic resonance imaging. 62(2019), 129-151, 2019, (Elsevier, IF: 2.874). Indexed in: SCI, Scopus, PubMed, etc. |
| 5 | Kernel Induced Semi-supervised Spatial Clustering: A Novel Brain MRI Segmentation Technique. | Computer Methods and Programs in Biomedicine (reviewed) (Elsevier, IF: 3.632), Indexed in: SCI, Scopus, PubMed, etc. |
| 6 | A Novel Consensus Framework for Tumor Detection from Brain MRI. Medical & Biological Engineering & Computing | (reviewed) (Springer Nature, IF: 2.116), Indexed in: SCI, Scopus, UGC CARE, etc. |
| 1 | Automated Blood Cancer Detection Using Image Processing Based on Fuzzy System. | IJARCSSE, Volume 4, Issue 8, Aug. 2014 |
| 7 | Identification of WBC based on Dynamic Clustering using Modified FCM Algorithm with an Approach to Optimal Result. | In Proceedings of the 6th IEEE International Conference on Computer and Communication Technology 2015 (ICCCT '15). ACM, New York, Pages 461-464. |
| 8 | Implementation of Efficient FCM Algorithm to Identify WBC with a Novel Distance Metrics. | In Proceedings of the International Conference on Computing Paradigms (ICCP2015), July 2015, Yelagiri Hills, Vellore District, Tamil Nadu, INDIA – 635 853 |
| 9 | Brain MRI Segmentation using Clustering Based Techniques: An Empirical Study. | In Proceedings of National Conference on Applied Sciences, Sustainable & Evolving Technologies (ASSET), March 2018, Central Institute of Technology, Kokrajhar. |
| 10 | A new Semi-supervised Clustering Algorithm for Brain Tissue Segmentation. | In Proceedings of National Conference on Mathematical Sciences & Applications in Sciences, Engineering & Technologies (MSASET), March 2019, Rajiv Gandhi University ( A Central University), Arunachal Pradesh. |
| 11 | Study of classifiers’ performances for Alzheimer’s disease diagnosis. | In Proceedings of 3rd National Conference on Recent Advances in Science and Technology (NCRAST-2020), August, 2020, Assam Science and Technology University, Guwahati. |
Book Chapters
- N. A. Talukdar and A. Halder. Brain MRI segmentation using Kernelized Rough Fuzzy C-Means and new ground truth construction techniques for result analysis and validation. Advances in Science and Technology, Vol. I, ISBN 978-81-908910-9-7, i- Manager publications, India, March 2018.
- N. A. Talukdar and A. Halder. Segmentation of brain MRI using Unsupervised and Semi-supervised Techniques. Advances in Science and Technology, Vol. II, ISBN 978-93-90491-11-7, McGraw-Hill, India, June 2019.