Tracking Emotional Sentiment Dynamics in Social Network Commentaries
Amidst the digital renaissance powered by social media's meteoric rise, a treasure trove of real-time public sentiment unfolds. Riding on this wave, we've created a cool new tool that dives into these chats and figures out how people feel. Harnessing the might of the avant-garde BERT model, we sculpt emotional symphonies from the raw, unfiltered voices of netizens, painting a panoramic view across multifaceted sectors.
Zero-Knowledge Proof & Blockchain Based Framework for Combating Fake Profiles
Imagine a digital bouncer for social networks, ensuring only real folks enter. Our brainchild marries zero-knowledge proof (ZKP) with private ledgers (blockchains) to check you're you—without peeking into your life! It's like having a government-stamped ID in the virtual world and a secret handshake for entry. By merging ZKP and private blockchains, we ensure user authenticity and confidentiality, enhancing online safety.
Combating Identity Misrepresentation through Text-Driven Gender Recognition
Navigating the realm of social media, ensuring genuine gender representation is key for a safe, inclusive online world. While many languages have been explored, Bangla's digital landscape remained untouched. We apply stylometric, TF-IDF, and word embedding to identify the author's gender from Bangla texts. Traditional models outperform deep learning models for all except the stylometric features with Stochastic Gradient Descent outperforming state-of-the-art.
Federated Learning Based Contraband Detection within Airport Baggage X-Rays
For tighter airport security, spotting contraband in X-rays is vital. Amid high stress, security teams often miss hidden items. Enter machine learning: while great at detecting forbidden items, it can risk data privacy. Our solution? A privacy-friendly Federated Learning approach using top-tier algorithms. With accuracy rates hitting a high of 90%, we demonstrate that our method isn't just safe but also super efficient!
Predicting Child Lead Toxicity with Machine Learning and NLP at ZIP Code Level
Childhood lead exposure harms health, with links to factors like income and older homes. To pinpoint exposure hotspots, we used data from Massachusetts and New York, forecasting at the zip code level. Adding sentiment analysis, we assessed how well lead programs work. Our techniques, especially LightGBM, proved highly accurate to predict exposure risks by zip code. We've surpassed past scores, showing a detailed lead exposure map!
Differentially Private Federated Learning for Diabetic Retinopathy Prediction
Deep learning aids diabetic retinopathy diagnosis using large retinal image sets. However, data privacy laws restrict sharing. We suggest a secure federated learning system for cross-organization image analysis. Testing on a 35,120-image dataset, ResNet50 topped with 83.05% accuracy without noise and 79.35% with noise utilizing differential privacy. Our method also cut communication overhead by 49% against standard federated learning.