Top AI, Machine Learning & Web Development Projects – Portfolio & Case Studies
Learn about innovative web development, machine learning, deep learning, and artificial intelligence initiatives. Discover comprehensive case studies, dashboards, and cutting-edge software solutions with outcomes, demonstrations, and technological insights. Ideal for computer enthusiasts, students, and developers.
Select a project from the dropdown below and click on Jump to Project to quickly navigate to the project details. Explore in-depth information, view demos, and access associated publications or research papers to learn more about each project.
Project Showcase Navigation
AI Smart Monitoring and Anomaly Detection Dashboard
- September 2025
Project Overview
The AI Smart Monitoring and Anomaly Detection Dashboard was designed to enhance the quality, efficiency, and reliability of large-scale annotation workflows used in AI/ML model training. Since annotation quality directly impacts supervised learning performance, this solution introduces AI-driven monitoring and real-time anomaly detection to maintain data integrity throughout the annotation lifecycle.
The system continuously tracks annotation activities, detects irregularities such as mislabeled data, quality drops, bias, and annotation drift, and flags issues instantly for review. Insights are accessible via an interactive dashboard and AI chatbot.
Problems Addressed
- Manual quality checks that are slow, biased, and hard to scale
- Inconsistent annotation quality across annotators
- Delayed detection of errors, bias, and dataset drift
- Lack of real-time visibility into progress and workforce performance
- Scalability challenges in large annotation projects
AI-Powered Solution
- Real-Time Monitoring: Live tracking of annotation progress, workload distribution, and annotator performance
- Automated Anomaly Detection: AI models detect mislabeled data, inconsistencies, bias, and abnormal patterns
- Quality Scoring: Quality percentage calculated per annotator, dataset, and project
- Smart Alerts: Automated alerts triggered when quality drops below thresholds
- AI Insights: Context-aware AI suggestions for corrective actions
Value Delivered & Metrics
- Annotation Accuracy: Improved from ~80–85% to ≥95% consistency
- Error Reduction: 20–30% fewer labeling errors auto-flagged
- Anomaly Detection Speed: Reduced from hours/days to under 5 minutes
- Manual QA Reduction: 30–50% less manual review effort
- Operational Efficiency: Optimized resource allocation via live dashboards
Why This Is an AI Dashboard (Not Just Monitoring)
- Proactive anomaly detection instead of reactive tracking
- Self-learning AI models with adaptive thresholds
- Context-aware analysis considering workload spikes and dependencies
- Automated root-cause analysis and actionable recommendations
- Scales to thousands of metrics without human fatigue
Technology Stack
- Frontend: React.js, Tailwind CSS (responsive, light/dark mode)
- AI/ML: Lovable AI (anomaly detection, insights, AI prompting)
- Data Sources: Lovable
Pilot & Future Roadmap
- Deploy on internal GitHub environment
- Connect anonymized live annotation data
- Run 2–4 week pilot with selected teams
- Gather feedback and scale across projects
Learning Analysis on YouTube using AI & Chatbot
- April 2025
Project Overview
This project focuses on analyzing YouTube comments using Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to understand public sentiment. By applying machine learning and deep learning models, the system classifies user comments into positive, negative, or neutral categories and provides real-time sentiment insights.
Problem Statement
YouTube generates massive volumes of user comments daily, making manual sentiment analysis impractical. These comments often contain informal language, sarcasm, emojis, and contextual expressions, which reduce the accuracy of traditional sentiment analysis methods. The challenge is to build a robust system that can process large-scale YouTube comment data and accurately identify sentiment trends in real time.
Objectives
- Analyze public sentiment from YouTube comments using AI techniques
- Classify comments into positive, negative, and neutral categories
- Handle noisy, informal, and sarcasm-rich social media text
- Visualize sentiment trends over time and across topics
- Support real-time sentiment monitoring using YouTube API
Dataset & Data Collection
The dataset is collected using the YouTube API, consisting of approximately 5,000 comments from videos related to movies, news, and current events. The data is manually or semi-automatically labeled into positive, negative, and neutral sentiments. The dataset is split into training and testing sets for model development and evaluation.
Data Preprocessing
- Tokenization of text into individual words
- Stop-word removal (e.g., "is", "the", "and")
- Text normalization (lowercasing)
- Lemmatization and stemming
- Removal of emojis, URLs, special characters, and noise
- Handling negations (e.g., "not good")
Feature Extraction Techniques
- Bag of Words (BoW): Frequency-based word representation
- TF-IDF: Identifies important words across the corpus
- Word Embeddings: Word2Vec / GloVe for semantic understanding
Machine Learning & Deep Learning Models
- Support Vector Machine (SVM)
- Logistic Regression
- Naive Bayes
- Random Forest
- K-Nearest Neighbors (KNN)
- Convolutional Neural Network (CNN)
- Long Short-Term Memory (LSTM)
Model Evaluation
Models are evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Hyperparameter tuning is performed using grid search and random search techniques to improve model performance.
Results & Performance
Deep learning models, especially CNN, outperformed traditional machine learning algorithms. The CNN model achieved an accuracy of 92.3%, demonstrating superior capability in capturing contextual and semantic patterns in YouTube comments. While CNN and LSTM required longer training times, they provided significantly higher accuracy compared to SVM and Random Forest.
Real-Time Sentiment Analysis
The system supports real-time sentiment classification by continuously streaming comments using the YouTube API. Incoming comments are instantly classified and can trigger alerts when sudden changes in sentiment are detected.
Visualization & Insights
- Sentiment distribution (positive, negative, neutral)
- Sentiment trends over time
- Correlation between real-world events and sentiment changes
Agro Connect Hub
- August 2024
Project Overview
Agro Connect Hub is a full-stack digital platform designed to modernize agricultural operations by directly connecting farmers with buyers, retailers, contractors, and laborers. The system eliminates middlemen, promotes fair pricing, and provides access to organic produce and agricultural equipment rentals through a centralized digital ecosystem.
Aim & Objectives
- Bridge the gap between farmers and buyers using a digital marketplace
- Enable direct trading of organic agricultural produce
- Provide equipment and resource rental services
- Promote sustainable farming practices
- Increase farmer income and improve supply chain efficiency
Technology Stack
- Frontend: HTML, CSS, Angular
- Backend: Spring Boot (RESTful APIs)
- Database: MySQL
- ORM: JPA / Hibernate
- API Testing: Postman
System Architecture
The platform follows a layered architecture with a clear separation of concerns:
- Client Layer: Angular-based responsive UI
- Service Layer: Spring Boot business logic
- Persistence Layer: JPA mappings with MySQL
- Integration Layer: REST APIs tested using Postman
Database Relationship Mapping (JPA)
-
Many-to-One:
Multiple farmers are mapped to a single admin using
@ManyToOneand@JoinColumn - One-to-Many: One admin manages multiple farmers, retailers, contractors, and laborers
- Many-to-Many: Farmers and products/resources share bidirectional relationships with eager fetching
API Functionalities
- POST: Add farmers, retailers, contractors, products
- GET: Fetch user lists, products, and resources
- PUT: Update user profiles and product details
- DELETE: Remove inactive users or listings
Key Features
- Role-based user management (Admin, Farmer, Retailer, Contractor, Laborer)
- Organic produce listing and management
- Agricultural equipment rental module
- Admin monitoring and system control
- Secure data handling with relational integrity
Impact & Outcome
Agro Connect Hub demonstrated how digital platforms can significantly improve agricultural productivity and economic outcomes. By reducing dependency on intermediaries, the system empowered farmers with direct market access, improved transparency, and enhanced community engagement.
Learning & Contributions
- Hands-on experience with full-stack development
- Deep understanding of JPA relationship mappings
- REST API design and testing
- Team collaboration and project leadership
Onion Weed Classification System through Deep Learning
- March 2022
Introduction
Agriculture is one of the most critical aspects of human survival. Manual detection of weeds in large onion farms is labor-intensive and time-consuming. The Onion Weed Classification System leverages Deep Learning and Convolutional Neural Networks (CNN) to automatically detect and classify weed presence in onion crops, reducing effort, improving crop yield, and streamlining farming operations.
Aim of the Project
The aim of this project is to develop an automated system that can accurately identify weeds in onion crops using image processing and deep learning techniques. The system reduces manual labor, increases efficiency, and helps farmers improve productivity by timely detecting and removing weeds.
Literature Survey
Previous studies explored weed detection using machine learning, FPGA-accelerated DNNs, semantic segmentation, and multispectral imaging. Researchers such as Arif Sheeraz, C. Lammie, M. Das, and X. Jin demonstrated the effectiveness of deep learning models like CNN, VGG, ResNet50, and YOLO for accurate weed detection in agricultural fields.
Proposed Methodology
Step 1: Dataset Preparation
Onion crops are grown in plastic trays (1×1 ft). One tray has only onion plants, while the other includes weeds. Around 2000 images are captured using OpenCV in Python. Images are divided into training and testing datasets.
Step 2: Image Preprocessing
- Resize images to 150×150 pixels.
- Rescale pixel values (1/255 normalization).
- Data augmentation: shear (0.2), zoom (0.2).
- Create batches of 64 images for training and testing.
Step 3: CNN Training
- Sequential CNN with 3 convolutional layers (32 kernels, 3×3, ReLU).
- Max pooling after each convolutional layer.
- Flatten layer to convert features to 1D vector.
- Dense layer with 100 neurons (ReLU activation).
- Output layer with 1 neuron (Sigmoid activation) for binary classification.
- Optimizer: Adam, Epochs: 500–1000.
Step 4: Decision Making & Notification
Live images from the field are processed in real-time using the trained CNN. The model classifies each image as Weed or Non-Weed. Alerts are sent to farmers via WhatsApp along with sample images to allow timely action.
Tools & Technologies
- Python – Programming language
- OpenCV – Image capture and preprocessing
- Keras & TensorFlow – Deep learning model development
- Anaconda & Spyder IDE – Development environment
- WhatsApp API – Real-time notifications
Results
The system achieved high accuracy in detecting weeds in onion crops. Testing on live images from plastic trays and simulated fields shows reliable classification, reducing the time and effort required for manual weed detection. CNN model training and testing graphs show convergence in accuracy and minimal loss.
Future Scope
- Train models from germination to harvesting stage to improve accuracy.
- Deploy in live onion fields with variable lighting and soil conditions.
- Extend system to other crops for broader weed detection.
- Integrate advanced notification systems with mobile apps.
Conclusion
The Onion Weed Classification System demonstrates the effectiveness of deep learning in precision agriculture. By automating weed detection, it helps farmers save labor, improve crop yield, and take timely action against weed growth. CNN models proved highly efficient in classifying weed vs. non-weed images.
COVID-19 Vaccination Management System
- February 2022
Introduction
The COVID-19 Vaccination Management System is a desktop application built using Java Swing and MySQL to efficiently manage vaccination registration, appointment scheduling, dose tracking, and inventory. The system aims to streamline the vaccination process, improve record-keeping, and ensure timely administration of vaccines.
Aim of Project
The main objective of this project is to provide an automated solution for managing COVID-19 vaccinations. It allows users to register for vaccination, schedule appointments, track vaccine doses, and receive reminders, while also assisting administrators in managing vaccine stock and monitoring appointments.
Requirements
- Java Swing: For building the desktop GUI application with forms, tables, and dialogs.
- MySQL: To store user information, vaccination records, appointments, and vaccine inventory.
- JavaMail API: For sending automatic email notifications to users.
- Third-Party SMS API (like Twilio): For sending SMS reminders for vaccination appointments.
Modules
- User Registration: Users can register with personal details, contact information, and health status.
- Appointment Scheduling: Users can book vaccination slots based on availability.
- Dose Tracking: The system tracks first and second doses and booster shots.
- Vaccine Inventory Management: Admins can monitor vaccine stock and update inventory levels.
- Reports: Admins can generate reports on vaccination statistics and appointment status.
Automatic Notification System
The system includes an automatic email and SMS notification feature to improve user engagement and ensure timely vaccination. Key functionalities include:
- Send confirmation emails/SMS upon successful registration and appointment scheduling.
- Send reminders 24–48 hours before the scheduled vaccination.
- Notify users for their second dose or booster shots automatically.
- Alert administrators via email/SMS about low vaccine inventory or pending appointments.
This feature is implemented using JavaMail API for emails and third-party SMS APIs (like Twilio) for mobile notifications. It helps reduce missed appointments and keeps citizens informed in real-time.
Back-end Architecture
The system uses MySQL to store data in tables such as Users, Appointments, Doses, and Inventory. The Java Swing application interacts with the database using JDBC for data retrieval and updates. The automatic notification system is integrated with the backend to trigger emails/SMS based on scheduled appointments.
Conclusion
The COVID-19 Vaccination Management System simplifies the vaccination process for both citizens and administrators. By integrating registration, appointment scheduling, dose tracking, inventory management, and automated notifications, the system reduces manual workload, prevents missed vaccinations, and improves overall efficiency. Future enhancements may include multilingual support, mobile app integration, and AI-based vaccine demand forecasting.
Student Management System
- September 2021
Introduction
The Student Management System is a desktop application developed using Java Swing and MySQL. It is designed to help teachers and administrative staff in colleges efficiently manage student records, track academic performance, and maintain essential data related to students, courses, and attendance.
Aim of Project
The main goal of the Student Management System is to simplify the management of student information by providing a centralized platform. Teachers can track student performance, attendance, and personal information, while the college administration can generate reports, monitor class statistics, and streamline daily academic operations.
Requirements
- Java Swing: Used for creating the user-friendly desktop GUI with forms, tables, and interactive dialogs.
- MySQL: For storing student data, course details, attendance records, and academic performance.
- JDBC: To connect the Java application with the MySQL database.
Modules
- Student Registration: Teachers can add new students with personal and academic details.
- Attendance Management: Track daily attendance for students and generate attendance reports.
- Grades & Marks Tracking: Record and monitor student grades for each subject.
- Course Management: Manage course details, enrollments, and assignments.
- Reports: Generate student performance reports, attendance summaries, and class statistics.
- Search & Update: Easily search for a student record and update details when necessary.
Back-end Architecture
The system uses MySQL to store data in structured tables such as Students, Courses, Attendance, and Grades. The Java Swing application interacts with the database using JDBC, allowing teachers to perform CRUD operations efficiently. The centralized database ensures that all student records are up-to-date and easily accessible for administrative and academic purposes.
Conclusion
The Student Management System provides an efficient and reliable way to manage student information at the college level. By integrating student registration, attendance tracking, grade management, and report generation into a single platform, it reduces manual paperwork, improves data accuracy, and allows teachers to focus more on teaching. Future enhancements can include integrating automated email notifications for students, mobile access, and advanced analytics for performance trends.
Comments
Post a Comment