Research 🔎

• Real-time Ship Anomaly Detection using AIS Data: Developing a robust and efficient method for detecting anomalies in ship behavior using Automatic Identification System (AIS) data. Exploring deep learning techniques, such as recurrent neural networks or attention mechanisms, to analyze and classify ship trajectories in real time.

• Multi-Modality Generation and Text Conditioning: Investigating novel approaches to generate diverse outputs based on textual descriptions, such as images or scenes. Exploring the fusion of natural language processing techniques with computer vision models to enable rich multi-modal generation.

• Visual Transformers for Image Generation and Transformation: Researching the application of visual transformer models in computer vision tasks, particularly in image generation and scene transformation conditioned on text inputs. Exploring the potential of self-attention mechanisms to capture global dependencies and generate high-quality visual outputs.

Publications 📝

• Can NLP Models ‘Identify’, ‘Distinguish’, and ‘Justify’ Questions that Don’t Have a Definitive Answer?
• Constraint-Based Multi-organ Identification in CT Images using Unsupervised Learning
• LPRNet: A Novel Approach for Novelty Detection in Networking Packets
• Performance Analysis of Machine Learning Algorithms and Feature Extraction Methods for Sentiment Analysis
• Genetic Algorithm and Ensemble Learning Aided Text Classification using Support Vector Machines

Please refer to my Google Scholar for more details about the publications I have participated in.

Projects 💻

• Visual Story Generation
• Is Transformation Better than Resampling for Lesion Segmentation Using Deep Learning in Dermatology?

I have linked the implementations of the above projects available on my Github.