Technology Division

1. Project Description: Disease Prediction Chatbot (AI & ML)

This project presents an innovative web-based healthcare assistant that leverages artificial intelligence for disease prediction and interactive support. At its core, the system utilizes a decision tree model trained on heart disease data to accurately predict the risk of disease based on user-provided symptoms. The predictive model is seamlessly integrated with a user-friendly chatbot interface, creating an interactive experience that guides users through each step.

Key features include:

• Symptom-Based Prediction: Users enter their symptoms, and the chatbot predicts potential heart disease risk.
• Interactive Chatbot: The system provides clear, clickable options for learning more about disease prevention, treatment, and recommended lifestyle changes, making healthcare advice instantly accessible.
• Personalized Assistance: The integration of AI ensures that guidance is tailored to individual users, helping them make informed decisions about their health.
• Modern Web Application: Developed using Python and Flask for the backend and HTML, CSS, and JavaScript for the frontend, the application is responsive and easy to use on any device.
• Perfect Model Accuracy: The decision tree model is rigorously trained and evaluated, demonstrating perfect accuracy on both training and test datasets.

By combining advanced AI techniques with an intuitive chatbot and a polished web interface, this project showcases the transformative potential of digital tools in healthcare. It paves the way for smarter, more accessible, and personalized health support, making expert guidance available anytime, anywhere.

 

2. Project Description: Identifying Fake Accounts in Instagram Using Machine Learning

This project focuses on developing an intelligent system for detecting fake accounts on Instagram by leveraging machine learning (ML) techniques. With the rapid growth of Instagram, the presence of fraudulent accounts used for spam, misinformation, and malicious activities has become a significant threat to platform integrity and user trust.

Key Features and Approach

• Data Collection and Feature Extraction: The system collects comprehensive data from Instagram profiles, including account metadata (such as username, bio, follower/following count), activity metrics (number of posts, likes, comments), and engagement statistics.
• Machine Learning Integration: Multiple ML algorithms are utilized, such as Support Vector Machine (SVM), Random Forest, Decision Tree, and advanced Deep Neural Networks (DNNs), to classify accounts as fake or genuine. Feature engineering is a crucial step, focusing on behavioral, profile content, and network characteristics.
• Model Training & Evaluation: The algorithms are trained on a dataset containing both real and fake Instagram profiles. Their performance is evaluated using metrics like accuracy to ensure reliable detection. Studies show Random Forest and deep learning approaches often outperform traditional methods in capturing complex patterns associated with fake accounts.
• Automated Workflow: Upon user request, the model processes the profile’s data and predicts its authenticity in real-time. The detection result is then displayed to the user, enabling them to act against suspicious accounts.
• Scalability & Real-World Value: By automating detection, the project provides a scalable solution that aids in preserving a trustworthy online community, reducing the spread of misinformation and automated scams.

Deployment

The entire workflow is typically implemented in Python, utilizing libraries such as Scikit-learn, Pandas, and NumPy. The system can be integrated within a backend that analyzes profiles on demand, providing results through a simple and interactive frontend or chatbot interface.

Summary:
This project showcases the application of machine learning to a real-world social media security challenge. By integrating advanced classification models with thoughtful feature engineering and automation, it offers a robust tool for distinguishing fake Instagram accounts, supporting a safer and more genuine user experience on the platform.

 

3. E-Banking Phishing Website Detection Project Cybersecurity –

Phishing websites targeting e-banking services are sophisticated forgeries built by cybercriminals to look almost identical to legitimate banking sites. Their primary aim is to deceive unsuspecting users into disclosing sensitive information such as bank account numbers, passwords, and credit card details. This can lead to severe financial losses and a major breach of information security.

Phishing, a recent threat in the landscape of internet crimes, is uniquely challenging because it combines technical deception with social engineering. There’s no universal solution to this dual-faceted problem. Therefore, our project is motivated by the urgent need for a robust, automated defense.

Project Solution:
The core of this project is an automated detection system for e-banking phishing websites. We leverage associative classification algorithms, which intelligently analyze various features of web pages and flag potential phishing attempts. This approach offers a resilient and effective way to combat phishing by constantly learning from new threats.

Technical Overview:

• Backend: Uses MongoDB to store all related data securely.
• Frontend: Developed with a modern Javascript framework, ensuring a smooth and interactive user experience.
• Accessibility: Fully online—accessible from any device, whether it’s a PC or laptop, with minimal hardware requirements.

In summary:
This project presents a smart, data-driven approach to safeguarding users from the ever-evolving threat of e-banking phishing websites, combining cutting-edge technology with practical usability.

 

4. Project Title:
   AI-Based Threat Detection and Response Platform

Project Overview:
As cyber threats grow more sophisticated, traditional security measures often struggle to keep up. This project aims to create an automated, intelligent platform that uses artificial intelligence (AI) to detect and respond to potential cyber threats in real time. By analyzing large volumes of network traffic, system logs, and user behavior, the AI-based system can identify anomalies that may indicate malicious activity—such as malware, data exfiltration, or insider attacks—much faster and more accurately than manual methods.

Key Features:

• AI-Powered Analytics: Employs machine learning algorithms to monitor and analyze diverse data sources (network traffic, logs, user actions) for suspicious patterns.
• Real-Time Alerts: Instantly notifies administrators about detected threats or anomalies, minimizing potential damage.
• Automated Response: Can quarantine affected systems, block malicious IPs, or initiate other automated defense mechanisms based on threat severity.
• User-Friendly Dashboard: Provides a comprehensive, interactive interface for administrators to view, investigate, and manage security incidents.
• Continuous Learning: The model refines its detection capabilities over time by learning from new attack types and security incidents.

Technical Overview:

• Backend: Python for AI/ML (using TensorFlow/PyTorch), database for storing events (MongoDB/PostgreSQL).
• Frontend: Developed using React or Angular for a smooth, interactive experience.
• Deployment: Cloud-ready and scalable, can be integrated into existing security infrastructure.
• Accessibility: Online platform accessible from any secure device.

Project Motivation:
This project empowers organizations with a proactive approach to cybersecurity, reducing reliance on manual monitoring and supporting faster, smarter security decisions. By leveraging AI, the platform adapts to evolving threats, ensuring robust and resilient protection for critical systems

 

5. Cloud-Based Disaster Recovery System for SMEs (Small & Medium Enterprises)

Project Description

Design a cloud-based disaster recovery (DR) solution tailored for small businesses. The system will automate backup, replication, and failover of critical applications and databases to the cloud (e.g., AWS S3 + EC2 or OpenStack Swift + Nova). It should include scheduling, multi-versioning, and cost-efficient storage strategies using tiered cloud services (standard, infrequent access, and archival).

Real-World Problem

Most SMEs can’t afford dedicated disaster recovery data centers. When hit by ransomware, hardware failure, or natural disasters, many lose access to business-critical data. This solution enables affordable, cloud-based DR services that automate recovery, reduce downtime, and minimize operational loss.

Key Features

• Data versioning and scheduled backups
• Multi-region replication for geo-fault tolerance
• Snapshot-based application recovery
• Optional user dashboard for monitoring backup status

Tools You Can Use

• AWS (S3, EC2, CloudWatch), or MinIO + Docker
• Node.js or Python for backend
• SQLite or PostgreSQL for metadata
• Cron or cloud-native schedulers for automation

 

6. Cloud-Based Energy Monitoring and Billing Platform for Smart Homes

Project Description

Build a cloud-hosted platform where smart meters from homes periodically send energy usage data. The system aggregates data in real time, stores it, and offers dashboards for users to monitor their energy consumption. You’ll implement tiered billing, and time-of-use electricity pricing, and provide alerts when usage exceeds thresholds.

Real-World Problem

Utility companies and consumers need better visibility into energy usage to promote savings and prevent overloading. Manual readings and fixed-rate billing are outdated. This system digitizes and automates metering, supports dynamic billing, and can be expanded for smart grid integration.

Key Features

• Cloud database of energy data (per minute/hour)
• Billing engine (per-unit cost with time-of-use modifier)
• Alerts to users via email/SMS when usage spikes
• Basic dashboard (web app) for data visualization

Tools You Can Use

• Cloud: Firebase, AWS DynamoDB, or Google Cloud IoT
• Frontend: React.js or basic HTML/CSS + Chart.js
• Backend: Node.js or Python Flask
• Simulated smart meter data (e.g., JSON payloads)

 

7. Python Full Stack

Title:

Campus Complaint Management System Using Django and React

Abstract:

In many academic institutions, the complaint registration process for students and faculty remains manual and inefficient. This project proposes a digital complaint management system using Django (Python) for the backend and React.js for the frontend. The system allows users to file, track, and resolve campus-related issues—such as infrastructure, hostel, academic, and administrative complaints—through a centralized portal. The application includes admin and department-level dashboards to ensure timely redressal and transparency. This system enhances communication and accountability within the institution.

Keywords:

Complaint Management, Django, React, Full Stack, Python, Student Portal, Campus Automation

1. Introduction:

Manual complaint handling often leads to delayed resolutions, lost records, and lack of accountability. A centralized digital solution can improve efficiency and user satisfaction by automating complaint registration, tracking, and resolution processes. This project uses Django for the backend API and React for the user interface to build a scalable, secure, and user-friendly platform.

2. System Architecture / Modules:

• User Module: Authentication for students, staff, and administrators.
• Complaint Module: Allows users to lodge complaints, attach media, and track status.
• Department Dashboard: View and resolve complaints relevant to specific departments.
• Admin Module: Manage users, complaints, categories, and generate reports.
• Notification Module: Email and in-app notifications for complaint status updates.
• Database (PostgreSQL): Stores user information, complaint logs, and resolutions.
• Tech Stack: Django (DRF), PostgreSQL, React.js, JWT Authentication.

 

8. Java Full Stack

Title:

Online Skill-Based Event Management Portal Using Spring Boot and Angular

Abstract:

Organizing skill development events like coding challenges, webinars, workshops, and hackathons is a frequent necessity in academic environments. This project presents an event management portal developed using Spring Boot for the backend and Angular for the frontend. It allows students and faculty to create, manage, register for, and track various events on campus. The portal includes role-based access (student, organizer, admin), real-time event updates, and post-event feedback. The system fosters a more organized and participative learning culture within institutions.

Keywords:

Event Management, Spring Boot, Angular, Java Full Stack, Campus Activities, Skill Development

1. Introduction:

Managing skill-based events involves numerous logistical and communication challenges. Traditional methods—using notice boards or social media—are ineffective for systematic tracking. This project solves that by providing a centralized, digital solution where all stakeholders can seamlessly manage events.

2. System Architecture / Modules:

• Authentication Module: Role-based login (Student, Organizer, Admin) with Spring Security.
• Event Module: Event creation, update, deletion, and registration features.
• User Dashboard: Personalized view for registered events and participation history.
• Admin Dashboard: User control, event oversight, and report generation.
• Feedback Module: Collect feedback and ratings post-event.
• Database (MySQL): Storage of user profiles, event data, registrations, and feedback.
• Tech Stack: Spring Boot, MySQL, Angular, JWT Authentication.