168 citations · 301 across the 8 of their papers we have counts for
8 papers
A Review of Link Prediction Applications in Network Biology
Ahmad F. Al Musawi, Satyaki Roy, Preetam Ghosh
In the domain of network biology, the interactions among heterogeneous genomic and molecular entities are represented through networks. Link prediction (LP) methodologies are instr…
JobRecoGPT -- Explainable job recommendations using LLMs
Preetam Ghosh, Vaishali Sadaphal
In today's rapidly evolving job market, finding the right opportunity can be a daunting challenge. With advancements in the field of AI, computers can now recommend suitable jobs t…
A simplified drift-diffusion model for pandemic propagation
Clara Bender, Abhimanyu Ghosh, Hamed Vakili +2
Predicting Pandemic evolution involves complex modeling challenges, often requiring detailed discrete mathematics executed on large volumes of epidemiological data. Differential eq…
Neural Network Architecture for Database Augmentation Using Shared Features
William C. Sleeman, Rishabh Kapoor, Preetam Ghosh
The popularity of learning from data with machine learning and neural networks has lead to the creation of many new datasets for almost every problem domain. However, even within a…
Deep Hyperspectral Unmixing using Transformer Network
Preetam Ghosh, Swalpa Kumar Roy, Bikram Koirala +2
Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. Wit…
Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions
Satyaki Roy, Nirnay Ghosh, Nitish Uplavikar +1
The pandemic caused by SARS-CoV-2 has left an unprecedented impact on health, economy and society worldwide. Emerging strains are making pandemic management increasingly challengin…
Multimodal Classification: Current Landscape, Taxonomy and Future Directions
William C. Sleeman, Rishabh Kapoor, Preetam Ghosh
Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine. H…
Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions
William C. Sleeman, Bartosz Krawczyk
Learning from imbalanced data is among the most challenging areas in contemporary machine learning. This becomes even more difficult when considered the context of big data that ca…