21 citations · 30 across the 10 of their papers we have counts for
11 papers
Foundation Models for Clean Energy Forecasting: A Comprehensive Review
Md Meftahul Ferdaus, Tanmoy Dam, Md Rasel Sarkar +2
As global energy systems transit to clean energy, accurate renewable generation and renewable demand forecasting is imperative for effective grid management. Foundation Models (FMs…
Unlocking the capabilities of explainable fewshot learning in remote sensing
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large am…
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts.…
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting
Md Rasel Sarkar, Sreenatha G. Anavatti, Tanmoy Dam +2
The main objective of this study is to propose an enhanced wind power forecasting (EWPF) transformer model for handling power grid operations and boosting power market competition.…
Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary Classifier
Harshita Boonlia, Tanmoy Dam, Md Meftahul Ferdaus +2
In real world scenarios, out-of-distribution (OOD) datasets may have a large distributional shift from training datasets. This phenomena generally occurs when a trained classifier…
Scalable Adversarial Online Continual Learning
Tanmoy Dam, Mahardhika Pratama, MD Meftahul Ferdaus +2
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susce…
Latent Preserving Generative Adversarial Network for Imbalance classification
Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama +3
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend…
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification
Tanmoy Dam, Nidhi Swami, Sreenatha G. Anavatti +1
This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification. It is an end-to-end approach i…
Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model
Subhrasankha Dey, Tanmoy Dam
A rainfall-runoff model predicts surface runoff either using a physically-based approach or using a systems-based approach. Takagi-Sugeno (TS) Fuzzy models are systems-based approa…
Does Adversarial Oversampling Help us?
Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti +2
Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer…
Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces
Tanmoy Dam, Sreenatha G. Anavatti, Hussein A. Abbass
Since their introduction in the last few years, conditional generative models have seen remarkable achievements. However, they often need the use of large amounts of labelled infor…