Architecture Note: All hybrid projects use a Java Spring Boot backend for web portal, database, and user management, communicating via REST API with a Python/Flask ML microservice for AI predictions — the industry-standard pattern for production ML systems.
A full hospital management platform where patient symptom data entered through a Java Spring Boot web portal is passed to a Python ML microservice that predicts probable diseases and recommends specialists. All patient records, appointments, and billing are managed via the Java backend.
A recruitment platform where HR teams post jobs on a Java Spring Boot portal, and an NLP Python microservice ranks uploaded resumes by keyword similarity to the job description using BERT sentence embeddings — automating candidate shortlisting.
A banking portal on Spring Boot processes all financial transactions, which are simultaneously scored by a Python Random Forest + LSTM fraud model in real time. High-risk transactions trigger automatic account freezes and admin alerts via WebSocket notifications.
A complete e-commerce platform on Spring Boot with catalogue, cart, orders, and payment simulation. A collaborative filtering Python ML service analyses user purchase history and generates personalised product recommendations displayed on the homepage and product pages.
An academic portal where faculty enter grades and attendance through a Spring Boot web application. A regression ML model analyses the data and predicts final semester performance, flagging at-risk students early with confidence levels and intervention recommendations.
A property listing portal on Spring Boot where buyers can get instant AI price estimates for any property by entering location, size, and amenity data. An XGBoost regression model running as a Python microservice returns the prediction with confidence intervals.
A farmer-facing portal on Spring Boot where users upload crop leaf photos. A CNN image classification model running as a Flask microservice detects diseases and returns treatment recommendations. The Java backend manages farmer profiles, disease history, and generates advisory reports.
A customer feedback portal on Spring Boot collects reviews and form submissions. A Python NLP sentiment model (VADER/BERT) classifies each response as positive, negative, or neutral, aggregates trends by department, and generates management insight reports.
A smart city module where uploaded images or camera feeds are analysed by a YOLOv8 object detection Flask microservice for traffic violations (no helmet, signal jump, wrong lane). Detected violations are logged and managed via a Spring Boot admin portal with fine generation.
Sensor readings from industrial machines are ingested by a Spring Boot backend and fed into a Python anomaly detection model (Isolation Forest + LSTM) that predicts equipment failures before they occur. Alerts are routed to maintenance teams via the Java notification engine.
A mental wellness web platform on Spring Boot where users interact with an NLP chatbot (DialoGPT/rule-based) for emotional support. The Java backend manages user sessions securely, detects critical distress signals (keyword flags), and escalates to a human counsellor dashboard.
A retail inventory portal on Spring Boot tracks stock movements and purchase orders. A Python LSTM/ARIMA forecasting microservice predicts demand for the next 30/60/90 days, enabling automated reorder suggestions and preventing stockouts.
A banking loan application portal on Spring Boot accepts applicant financial data. A Python ML classifier predicts loan approval probability and credit risk tier. The Java backend manages the application workflow, document uploads, and loan officer review queue.
An e-learning platform on Spring Boot delivers video courses and assessments. A Python sentence-transformer Q&A microservice acts as an AI tutor, answering student questions about course content. An adaptive difficulty engine adjusts quiz complexity based on performance history.
A building energy management portal on Spring Boot tracks electricity consumption from smart meters (simulated). A Python LSTM prediction microservice forecasts next-period usage, identifies wastage anomalies, and recommends optimisation actions with estimated cost savings.
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