Active Final Year Projects Showcase
Explore verified, ongoing final year projects advancing across all academic batches. Filter by emerging tech stacks, discover architectures, and explore student innovation.
Face recognition based attendance management system
Core Problem: Traditional attendance systems in educational institutions rely on manual roll calls, sign-in sheets, or card-based methods that are time-consuming, prone to proxy attendance (students marking attendance for absent peers), and difficult to maintain accurately over time. Faculty spend valuable lecture time on attendance, and administrative staff struggle with compiling and analyzing attendance records. Core Position: The Smart Attendance System leverages face recognition technology to automate the attendance marking process in educational institutions. By capturing and identifying student faces through a webcam or uploaded image, the system eliminates proxy attendance, reduces manual effort, saves classroom time, and provides real-time, accurate attendance records accessible to both students and administrators through a web-based dashboard.
Intrusion Guard: A Hybrid Machine Learning System for Network Intrusion Detection
Modern computer networks face continuous threats from malware, unauthorized access, and zero-day attacks that traditional signature-based security tools often fail to detect. These systems depend heavily on known attack patterns, which makes them less effective against new or evolving intrusions. The growing scale and complexity of network traffic also create a need for intelligent security solutions that can analyze traffic in real time and detect suspicious activity with high accuracy. This project proposes Intrusion Guard, a real-time intrusion detection system that uses machine learning to classify network traffic as normal or malicious. The system is designed for cybersecurity monitoring in environments where fast and accurate threat detection is critical, such as enterprise networks, laboratories, and institutional systems. It combines supervised and unsupervised learning to improve both known-attack detection and anomaly detection, making it more adaptable than conventional rule-based systems. The technical approach uses a hybrid model built around Random Forest for supervised classification and Isolation Forest for anomaly detection. The system processes network traffic data, extracts relevant flow-based features, and applies trained models to generate predictions in real time. To improve transparency and trust, the system also includes explainable AI output so users can understand why traffic is flagged as suspicious. The expected outcome is a practical and scalable intrusion detection solution that improves threat visibility, supports early warning, and strengthens network defense against both known and unknown attacks.
ZAPNEST: AI-Enabled SaaS E-Commerce Platform for SMEs (MERN Stack Based
ZAPNEST is a web-based Software as a Service (SaaS) e-commerce platform developed to help small and medium-sized enterprises (SMEs) transition from traditional physical businesses to modern digital marketplaces. In today’s rapidly evolving ...
AI-Based Student E-Learning Assistant
AI-Based Student E-Learning Assistant: The AI-Based Student E-Learning Assistant is a smart web-based learning platform designed to help students improve their studies through artificial intelligence. The system acts as a virtual tutor tha...
MediTrust : AI Driven doctor recommendation and Fake Review Detection Platform
In recent years, the demand for efficient and intelligent systems in the field of healthcare has significantly increased. However, the existing systems currently in use suffer from several limitations and inefficiencies. These systems are often limited to providing basic search functionality and fail to support disease-based doctor recommendations. Moreover, the presence of fake or unverified reviews reduces the reliability of these platforms. According to the literature review, many systems lack advanced filtering options, real-time doctor availability, and proper integration of appointment scheduling, which makes the overall process inefficient and time-consuming.As a result, users face difficulties in making informed decisions when selecting appropriate doctors, especially due to limited medical knowledge and unreliable feedback systems. This not only reduces the effectiveness of the system but also negatively impacts user satisfaction and decision-making.Although some solutions attempt to address these challenges, they fail to provide intelligent recommendation systems, fake review detection mechanisms, real-time scheduling, and comprehensive doctor information with advanced filtering features.Therefore, there is a strong need for a robust and intelligent system that can overcome these limitations and provide efficient, accurate, and user-friendly solutions.
Gesture control virtual mouse platform
Functional Requirements (What the system DOES) These describe the features of the system. 1. Hand Gesture Detection The system uses a camera to detect your hand. It recognizes different hand gestures. 2. Cursor Movement You can move t...
Meet hub
Functional Requirements of Meet Hub 1. User Registration and Authentication The system shall allow users to create accounts, log in securely, and recover forgotten passwords using email verification and authentication mechanisms. 2. User...
Neux remote control system and fake voice recognition
A Neux remote control system lets users control devices using smart signals instead of buttons. Fake voice recognition is when systems are fooled by copied or AI-generated voices. These technologies are advanced but need strong security t...
Campus event management system
Functional Requirements The Campus Event Management System will allow Admin, Students, and Organizers to manage campus events through a web-based platform. Admin can create, update, approve, and delete events, manage users, view registrati...
| Project Information | Status | Batch | Technologies | Action |
|---|---|---|---|---|
|
Face recognition based attendance management system
Core Problem:
Traditional attendance systems in educational institutions rely on manual ...
|
Active | FA23 (2026) |
MongoDB
Express.js
React
+3
|
Details |
|
Intrusion Guard: A Hybrid Machine Learning System for Network Intrusion Detection
Modern computer networks face continuous threats from malware, unauthorized access, and ze...
|
Active | FA23 (2026) |
Python
Flask
|
Details |
|
ZAPNEST: AI-Enabled SaaS E-Commerce Platform for SMEs (MERN Stack Based
ZAPNEST is a web-based Software as a Service (SaaS) e-commerce platform developed to help ...
|
Active | FA23 (2026) |
JavaScript
React
Express.js
+3
|
Details |
|
AI-Based Student E-Learning Assistant
AI-Based Student E-Learning Assistant:
The AI-Based Student E-Learning Assistant is a sma...
|
Active | FA23 (2026) |
JavaScript
React
Express.js
+6
|
Details |
|
MediTrust : AI Driven doctor recommendation and Fake Review Detection Platform
In recent years, the demand for efficient and intelligent systems in the field of healthca...
|
Active | FA23 (2026) |
JavaScript
Python
Next.js
+6
|
Details |
|
Gesture control virtual mouse platform
Functional Requirements (What the system DOES)
These describe the features of the system....
|
Active | FA23 (2026) |
MongoDB
Express.js
React
+1
|
Details |
|
Meet hub
Functional Requirements of Meet Hub
1. User Registration and Authentication
The system s...
|
Active | FA23 (2026) |
MongoDB
Express.js
React
+1
|
Details |
|
Neux remote control system and fake voice recognition
A Neux remote control system lets users control devices using smart signals instead of but...
|
Active | FA23 (2026) |
Python
Django
|
Details |
|
Campus event management system
Functional Requirements
The Campus Event Management System will allow Admin, Students, an...
|
Active | FA23 (2026) |
JavaScript
HTML/CSS
Bootstrap
+2
|
Details |
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MediTrust : AI Driven doctor recommendation and Fake Review Detection Platform
• The system should facilitate secure login and logout functionality for patients, doctors, and admin.
• The system should allow doctors to register and manage their professional profiles.
• The system should provide an interface for patients to enter symptoms or specific diseases.
• The system should generate AI-based doctor recommendations based on the disease entered by the patient.
• The system should display detailed doctor profiles including specialization, experience, consultation fee, and availability.
• The system should manage appointment booking between patients and doctors.
• The system should accept and store patient reviews and ratings.
• The system should implement a fake review detection mechanism to identify and filter misleading reviews.
• The system should present verified and genuine reviews to users.
• The system should handle advanced search filters such as specialization, location, availability, and consultation fee.
• The system should update doctor availability status in real time.
• The system should support doctors in updating their schedules and consultation details.
• The system should monitor user activities and review submissions through an admin panel.
• The system should generate analytical reports related to ratings and patient feedback.
- AI-Based Doctor Recommendation:
- Suggests suitable doctors based on user-entered symptoms or diseases using machine learning techniques.
- Fake Review Detection:
- Identifies and filters fake or misleading reviews to ensure only genuine feedback is displayed.
- Online Appointment Booking:
- Allows users to book appointments easily with real-time doctor availability.
- Doctor Profile Management:
- Displays detailed doctor information including specialization, experience, fees, and availability.
- Advanced Search and Filtering:
- Enables users to search doctors using filters such as name, specialization, and availability.
- Review and Rating System:
- Allows patients to give feedback and ratings for doctors to support better decision-making.
- Admin Dashboard:
- Provides system monitoring, user management, and analytics for efficient control.
- User-Friendly Interface:
- Simple and responsive design for easy interaction across devices.
The system shall respond to user requests within 2–3 seconds under normal load conditions.
The system shall efficiently handle multiple users simultaneously without significant delay.
Usability
The system shall provide a user-friendly and easy-to-navigate interface.
The system shall be accessible to users with basic computer knowledge.
Reliability
The system shall provide consistent and accurate results.
The system shall ensure minimum downtime and stable performance.
Security
The system shall ensure secure user authentication and authorization.
User data shall be protected using encryption and secure storage mechanisms.
Scalability
The system shall be designed to handle future growth in users and data.
The system architecture shall support adding new features without affecting existing functionality.
Availability
The system shall be available 24/7, except during maintenance.
Maintainability
The system shall be easy to update, debug, and maintain.
Code shall follow modular and structured design principles.
Compatibility
The system shall be compatible with major web browsers (Chrome, Edge, Firefox).
The system shall be responsive and work on different devices (desktop, tablet, mobile).
Accuracy
The AI-based recommendation system shall provide reasonably accurate results based on input data.
Fake review detection shall minimize false positives and false negatives.
Data Integrity
The system shall ensure that stored data remains consistent and correct.
Proper validation shall be applied before storing user input.