Event Details

  • Date: –
Online expert talk

Poster_Talk_Mudasir_Sir

Development of Large-scale Machine Learning Algorithms and Their Applications
in the Biomedical Domain

Speaker: Dr. Mudasir Ahmad Ganaie

📅 Date: 16th Sept. 2024 (Monday)
🕒 Time: 02:00 PM IST
📍 Venue: Online (Link will be shared prior to the event to the registered candidates)
🔗 Register Here: https://forms.gle/FNS5ph5vMWBATTERA (register by 11:00 AM, 16th Sept. 2024)

Abstract: In this talk, the speaker will present a novel large-scale machine learning algorithm.
Twin support vector machines (TSVMs) have been widely used for binary classification
problems, but they face challenges in handling large-scale datasets due to the need for
computing large matrix inverses and overfitting issues arising from empirical risk minimization.
To address these limitations, a novel fuzzy least squares TSVM for class imbalance learning
(LS-FLSTSVM-CIL) is proposed. The LS-FLSTSVM-CIL addresses these challenges by
eliminating matrix inversions and incorporating structural risk minimization to avoid
overfitting. Additionally, fuzzy weights are employed to effectively handle class imbalance,
ensuring a more balanced classification. The model is optimized using a sequential minimization
approach, making it computationally feasible for large-scale problems. Moreover, the speaker
will also demonstrate the practical applications of this algorithm in the biomedical domain,
including the diagnosis of Alzheimer’s disease and breast cancer.

Biography:

Dr. Mudasir Ahmad Ganaie is currently working as an Assistant Professor, in the
Department of Computer Science and Engineering, Indian Institute of Technology Ropar. Prior
to that, he worked as a postdoctoral research fellow at the University of Michigan, USA. He is an
active researcher in the field of Machine Learning (ML). He has been working on the
mathematical modeling of ML for the problems of classification and regression. Particularly, he
has been working on the problem of noise in the data, class imbalance learning and reducing the
computational cost of the ML models. He has published over 28 reputed Journals with an
average impact factor of 7.88, the highest impact factor of 19.118 and a total impact factor of
220.529. Journal publications include IEEE Transactions on Fuzzy Systems (I.F.=12.253), IEEE
Transactions on Cybernetics (I.F.=19.118), IEEE Transactions on Neural Networks and Learning
Systems (I.F.=10.4), IEEE Transactions on Computational Social Systems (I.F.=4.747), IEEE
Journal of Biomedical and Health Informatics (I.F.=7.021) and IEEE Transactions on
Computational Biology and Bioinformatics (I.F.=3.702). Moreover, he also published in reputed
journals of Springer and Elsevier including Machine Learning (I.F.=5.414), Annals of Operations
Research (I.F.=5.414), Knowledge Based Systems (I.F.=8.139) and Information Fusion
(I.F.=17.564). He has received more than 2400 citations with an h-index of 21 and an i10-index
of 31 (Sept., 2024 on Google Scholar). In addition, he has published over 7 conference papers of
international repute. The codes of most of the publications are publicly available on the GitHub
repositories (https://github.com/ganaiemudasir05 and https://github.com/mtanveer1). He
received the BEST PAPER AWARD for the IIT Indore and ICONIP conference. Currently, he is
guiding two PhD research scholars and one masters student for their thesis. He is actively
volunteering for the research community as an Associate Editor of Computers and Electrical
Engineering, Elsevier (I.F.=4.152) and Action Editor of Neural Networks, Elsevier (I.F.=9.657).
He acted as the local chair of the Indian Conference on Computer Vision, Graphics and Image
Processing (ICVGIP) 2023, technical Program Committee member of ICCAE 2024-the 16th
International Conference on Computer and Automation Engineering in Melbourne, Australia
and National organizing committee member of IEEE Computational Intelligence Society (CIS)
Summer School 2022.
Google Scholar: https://scholar.google.com/citations?user=XMXGZ-EAAAAJ&hl=en&oi=ao
Areas of interest: Deep learning, Machine Learning, Kernel based learning, Data Science,
Neuroimaginary