Monish
Saravanan

A recruiter uploads a job description and a batch of resumes. The system parses them, ranks every candidate against the job, and shows the recruiter exactly why each person got the rank they did — with the actual lines from the resume that drove the score. Every ranked list goes through a human before any action is taken.

The pipeline runs across seven services: ingestion handles PDF and DOCX parsing with OCR fallback, an NLP layer extracts skills, experience, education and normalises them against the ESCO ontology, sentence-transformers embed both the job requirements and resume evidence into vectors stored in pgvector, and a LightGBM ranker scores each candidate. SHAP values tie every top feature back to a specific span of resume text — so the recruiter sees "ranked #1 because of 6 years Python, matches requirement 3" not just a number.

Includes a fairness instrumentation tab that shows subgroup score distributions and top-K selection rates — with an explicit disclaimer that it is instrumentation for a real audit, not a bias removal claim. Hand-labelled evaluation set and live demo are the remaining milestones.

Micro-plastics — particles smaller than 5mm — blend into sediment, surface reflections, and organic matter. They're translucent, tiny, and nearly impossible for a standard detection model to pick out in drone footage of a river.

I extended YOLOv8 with Convolutional Block Attention Modules (CBAM) inserted directly after the C2f backbone blocks, before the multi-scale fusion stage. This forces the network to suppress background noise — glare, ripples, colour variation — and amplify the faint signals left by translucent particles, rather than treating everything equally. Prior work only adds attention at the detection head; putting it earlier means the benefit carries through every downstream stage.

Training handled a sharp imbalance — micro-plastics are only 4.9% of annotations while macro-plastics account for 56%. I used focal loss to stop the model from ignoring them entirely, then oversampled micro-plastic examples with domain-specific augmentations: simulated murky water (brownish-green tint), Gaussian blur for camera defocus, and gamma correction to replicate changing light. YOLOv8's mosaic augmentation added further context diversity.

Traditional steganography methods like LSB insertion are easy to attack — they have low payload capacity and are detected quickly by steganalysis tools. This paper proposes a CNN-based encoder-decoder that hides data inside images with nearly undetectable visual change, trained on 10,000 images to handle real-world distortions like compression, noise, and scaling.

The encoder extracts deep spatial features from the cover image and embeds the secret payload across them. The decoder reverses this precisely, even after the image has been manipulated. A composite loss function balances two objectives simultaneously — keeping the image visually identical to the original (MSE) while maximising data recovery accuracy (Binary Cross-Entropy).

Steganalysis detection rate kept below 30% — meaning adversaries struggle to even confirm hidden data exists. The system also maintained 88% accuracy under Gaussian noise and 85% after 50% image scaling, with a maximum payload of 1KB per image.

Spam evolves fast. A model trained on last year's campaigns gets outpaced quickly because spammers change phrasing, structure, and tactics. This project was designed around that core challenge.

Rather than relying only on word frequencies, the pipeline extracts structural signals — header anomalies, link density, sentence length patterns, and tone markers — that stay predictive even as specific spam language changes. The classifier was tuned for both accuracy and throughput so it stays practical at email scale.