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Object Tracking in Video using Deep Learning
This project develops object tracking in video using a Transformer-based model (BERT/ViT) trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Speech-to-Text Transcription using Deep Learning
This project develops speech-to-text transcription using a CNN-LSTM hybrid architecture trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Deep Learning-Based Currency Note Fraud Detection
This project develops currency note fraud detection using a GAN-based generative architecture trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Deep Learning-Based Named Entity Recognition System
This project develops named entity recognition system using a GAN-based generative architecture trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Text Summarization using Transformer Models
This project develops text summarization using a Transformer-based model (BERT/ViT) trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Chatbot using Sequence-to-Sequence Deep Learning Model
This project develops chatbot using a ResNet/EfficientNet backbone trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Deep Learning-Based Anomaly Detection in Surveillance Video
This project develops anomaly detection in surveillance video using a 3D-CNN for spatio-temporal data trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
GAN-Based Image Super Resolution System
This project develops gan-based image super resolution system using a ResNet/EfficientNet backbone trained on an appropriate image/video/audio/text dataset. The trained network is optimized for inference speed and served through a real-time demo application.
Crop Recommendation System based on Soil Data using ML
This project builds a data-driven pipeline for crop recommendation system based on soil data using K-Means and hierarchical clustering trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Traffic Accident Severity Predictor using ML
This project builds a data-driven pipeline for traffic accident severity predictor using Random Forest and XGBoost trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Employee Productivity Prediction using ML
This project builds a data-driven pipeline for employee productivity prediction using ensemble stacking of multiple classifiers trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.
Loan Approval Prediction using Ensemble ML
This project builds a data-driven pipeline for loan approval prediction using regression models (Linear, Ridge, Lasso) trained on historical/tabular data. The final model is wrapped in an interactive dashboard so non-technical users can get predictions and insights instantly.