I'm a dedicated Artificial Intelligence enthusiast with hands-on research and internship experience. Proficient in data science, machine learning and big data, with extensive experience in designing and implementing advanced learning algorithms utilising complex neural network architectures. Eager to apply concepts and skills to combat real-world challenges while continuously learning and adapting to technological advancements.
Seeking opportunities.
| S. No | Title | Description | Status | Link |
|---|---|---|---|---|
| 1. | Data Driven Cricket: A Machine Learning Approach to IPL score Prognostication | Implemented and compared the effectiveness of Random Forest, SVM and Linear Regression in predicting T20 cricket scores. | ||
| Random Forest was most effective | Published at the 15th International Conference on Computing and Networking Technology (ICCCNT) held at IIT Mandi | IEEE Xplore |
GitHub | | 2. | Perceptually-Aware Image Enhancement with Deep Residual U-Net and PatchGANs | Developed a novel algorithm that can enhance images for versatile cases with minimal training required, with promising evaluation metrics (SSIM: 0.9270, FSIM: 0.9998). | Under review in a journal | GitHub | | 3. | Advanced Photometric Analysis for Predicting Specific Star Formation Rates in Large Galaxies | Explored machine learning (ML) and deep learning (DL) models using SDSS-DR7 photometric data to predict specific Star Formation Rates (sSFRs) in galaxies. Applied models including SVR and Random Forest, achieving a lowest MAE of 0.2413 on original data. Enhanced dataset with synthetic data from Gaussian Copula models for improved robustness. Used DBSCAN clustering to identify high sSFR variability regions. Created an interactive tool for predictions, offering a cost-effective alternative to spectroscopic methods in galaxy evolution studies. | Under review in a journal | Arxive
GitHub | | 4. | Predicting Stellar Metallicity: A Comparative Analysis of Regression Models for Solar Twin Stars | This study employs various regression models, including Random Forest and Linear Regression, to predict the metallicity ([Fe/H]) of solar twin stars using stellar parameters and chemical abundances from the GALAH survey. The Random Forest model excelled, achieving an MSE of 0.001628 and R² of 0.9266, demonstrating the effectiveness of ensemble methods in complex astronomical data analysis. | Under review in a journal | Arxive
GitHub | | 5. | FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios | This research proposes a secure Federated Learning (FL) framework for IoT, integrating Decentralized Attribute-Based Encryption, Homomorphic Encryption, Secure Multi-Party Computation, and Blockchain. It ensures data privacy and security while enabling decentralized model training, secure aggregation, and real-time analytics in IoT environments. | Under review in a journal | Arxive
GitHub | | 6. | Bridging Legacy and Modern Threat Detection: An Analysis of Machine Learning Models on EMBER2018 and CIC-EvasivePDF2022 | Implemented and compared 11 Machine Learning, Deep Learning and Ensemble algorithms for malware detection and classification. Random Forest and XGBoost were the most successful for both legacy (EMBER 2018) malware and modern (CIC EvasivePDF2022) evasive malware achieving well over 99% accuracy. | To be Published | GitHub | | 7. | Multi-Speaker Speech Processing in Noisy Environments: A Hybrid Model for Source Separation and Summarization | Implemented audio source separation using a combination of SepFomers and ConvTas-Net, along with Adaptive noise reduction. Implemented summarization using BART model. | Under review in a conference proceeding | GitHub | | 8. | Comparing Analysis of Blackhole Mass Estimation in Type-2 AGNs:Classical vs Quantum Machine Learning and Deep Learning Approaches | This study compares classical and quantum machine learning algorithms for black hole mass estimation in Type-2 AGNs, revealing the strengths and limitations of each approach. Classical models like LSTM excel with high accuracy and error minimization, while quantum models, particularly Estimator-QNN, show promise but struggle with generalization. The results highlight the potential of quantum models for future astrophysical applications as quantum computing advances. | To be published | Arxive
GitHub | | 9. | Morphological Evolution of Galaxies with Redshift and Galaxy Mergers: A Novel Explainable AI Approach | | Ongoing Project | | | 10. | Adversarial Networks for B-V Index Prediction | This study compares GANs, Conditional GANs, and Contrastive GANs for generating synthetic data to predict the B-V (colour index) in astronomical datasets. The Contrastive GAN outperformed the others with the best predictive accuracy and clustering scores. Conditional GANs also performed well, while standard GANs showed the weakest results. These findings highlight the advantages of contrastive and conditional approaches for better synthetic data generation. | Ongoing Project | GitHub |