📚 Publications & Research Summary

My research spans deep learning, computer vision, NLP (Natural Language Processing), and applied AI; and it is grounded in curiosity, scientific rigor, and social relevance. 

From exploring data privacy through image watermarking systems to understanding emotional patterns in online discourse, I’ve worked on real-world challenges using intelligent systems.

The cornerstone of this journey was my MTech thesis, which explored Autoencoder-CNN architectures to embed and extract hidden image watermarks. This work matured into a publication in the SPIE Journal of Electronic Imaging and laid the foundation for secure, resilient AI-based data protection systems.

Beyond this, I’ve contributed to research in mental health analytics using NLP, semantic information retrieval, and a medical image data predictive framework — producing a mix of journal and conference papers. My work is published across reputable platforms such as SPIE, Springer, IEEE Xplore, and academic conferences, and is indexed on Google Scholar and ResearchGate.

Each project reflects my ongoing intention to use AI for automation, insight, safety, and impact.

🔹 Autoencoder‑CNN‑based Embedding and Extraction Model for Image Watermarking

Journal of Electronic Imaging (SPIE), Vol. 32(2), Sept 2022
📄 SPIE Digital Library • DOI: 10.1117/1.JEI.32.2.021604

Themes: Computer Vision, Image Watermarking, Autoencoder + CNN, Robustness

Summary: First-of-its-kind model to invisibly embed and extract image watermarks via deep learning, demonstrating resilience to compression and distortion.
 

🔹 A Fuzzy-Cluster Based Semantic Information Retrieval System (B.Tech Project)

In Proceedings of the 4th International Conference on Computing Methodologies, 2020
📄IEEE Xplore

Themes: Semantic Search, NLP, Fuzzy Clustering, Information Retrieval

Summary: Presented a system using fuzzy clusters to enhance semantic text retrieval, improving the relevance and accuracy of search results.

🔹 A Machine Learning Approach to Analyze Mental Health from Reddit Posts

S. Nayak, D. Mahapatra, R. Chatterjee, S. Parida, S. R. Dash
Biologically Inspired Techniques in Many Criteria Decision Making, 2022
📄 Springer

Themes: NLP, Mental Health Analysis, Reddit Data, Sentiment Classification

Role: Mentor & Co-researcher

Summary: Applied NLP and ML to Reddit posts to identify mental health patterns, pioneering early-stage sentiment-based mental support modeling.
 

🔹 Deep Learning Model for Efficient Mammogram Analysis

D. Mahapatra, R. Ray, S. R. Dash
Technical Advancements of Machine Learning in Healthcare, 2021
📄 Springer Chapter 2021

Keywords: Computer Vision, Medical Imaging, Deep Learning

Summary: Developed a CNN-based model for mammogram image analysis, aimed at enhancing diagnostic accuracy in breast cancer detection.
 

🔹 A Comparative Study of a Complex Social Network

IUP Journal of Information Technology, Vol. 14(2), 2018
📄 PDF

Keywords: Network Science, Social Graph Analysis, Graph Theory

Summary: Explored properties and behaviors of complex social networks, measuring network metrics and structural properties for comparative analysis (my first research project!).