120 citations · 190 across the 6 of their papers we have counts for
6 papers
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data
Mitul Goswami, Romit Chatterjee, Arif Ahmed Sekh
Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This…
Haphazard Inputs as Images in Online Learning
Rohit Agarwal, Aryan Dessai, Arif Ahmed Sekh +3
The field of varying feature space in online learning settings, also known as haphazard inputs, is very prominent nowadays due to its applicability in various fields. However, the…
Is Grad-CAM Explainable in Medical Images?
Subhashis Suara, Aayush Jha, Pratik Sinha +1
Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpr…
Simulation-supervised deep learning for analysing organelles states and behaviour in living cells
Arif Ahmed Sekh, Ida S. Opstad, Rohit Agarwal +5
In many real-world scientific problems, generating ground truth (GT) for supervised learning is almost impossible. The causes include limitations imposed by scientific instrument,…
Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs
Rohit Agarwal, Arif Ahmed Sekh, Krishna Agarwal +1
Streaming classification methods assume the number of input features is fixed and always received. But in many real-world scenarios demand is some input features are reliable while…
Neural network based country wise risk prediction of COVID-19
Ratnabali Pal, Arif Ahmed Sekh, Samarjit Kar +1
The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful…