A collection of AI, machine learning, and data engineering projects — from generative models to cloud deployments.
The study aimed to evaluate the efficiency of an existing image generation architecture and the potential of a novel input type for creating synthetic fluorescence images, addressing the challenge of limited medical imaging data.
Enhance both the quantity and quality of microscopic image datasets by generating synthetic images from real ones, and validate the reliability of a new white blood cell image dataset using different input types.
Utilized the Pix2Pix conditional GAN model to train and validate the generation of synthetic images from two distinct input types. Assessed the new dataset's reliability by applying a popular supervised model and comparing results across both input types.
Achieved satisfactory results, demonstrating that generative AI can improve image quality and expand the possibilities for medical imaging, particularly by overcoming data scarcity and quality challenges in the medical domain.
View on GitHub →Readers struggle to assess book value due to limited previews while facing disorganization in tracking reading progress across spreadsheets, leading to wasted time, money, and lost reading opportunities.
Develop an integrated solution that provides instant book analysis while enabling natural language querying of personal reading history to support both purchase decisions and reading management.
Empowered readers to make confident purchases (65% less preview time, 90% satisfaction) while eliminating manual tracking through conversational library queries resolved in 2 seconds versus 8-minute spreadsheet searches.
Live Demo → GitHub →Financial institutions need accurate tools to predict loan repayment outcomes in order to minimise risk and improve lending decisions.
XGBoost emerged as the optimal model, outperforming others in handling imbalanced data and capturing complex feature interactions. The pipeline delivered a reliable solution for predicting loan repayment outcomes.
Note: The dataset used is confidential; code is not publicly available.
Understanding machine learning models can be challenging, especially for beginners and non-technical users, creating a barrier to effective learning and communication.
Delivered a scalable, intuitive solution that empowers data scientists, students, and business professionals to quickly understand and explain complex ML models.
View on GitHub →Distinguishing weeds from crops is crucial in agriculture, and selecting the most effective image classification model can improve accuracy and efficiency.
Evaluated and compared a Vision Transformer model against four popular deep learning CNN models (ResNet-50, Xception, Inception V3, Inception-ResNet V2) for weed and crop classification as part of a two-person final year project.
The Vision Transformer outperformed all four CNN-based models, demonstrating its potential as a superior alternative for weed and crop classification in agricultural applications.
View on GitHub →Executed a case study utilizing unsupervised machine learning techniques and IoT for the early detection of diseases in black pepper, aiming to enhance farm yields.
Compared performance of ResNet-50 and VGG-16 architectures and a CNN built from scratch for the detection of COVID-19 on a dataset consisting of Chest X-Ray images.
View IEEE Publication →