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.
Multi-Tenant Agri-Retail with AI-Powered Supplier Price Intelligence
Many small agricultural-input shops in rural Pakistan manage customer credit (udhaar), supplier balances, daily sales, purchases, cashbooks, and inventory using handwritten registers and bill books. Dealer price lists are often received as PDFs from different companies, making product, pack-size, and purchase-price comparisons time-consuming and error-prone. These fragmented workflows can lead to inaccurate balances, poor stock visibility, missed expiry dates, and difficulty generating reliable business reports. Existing generic tools may not adequately address the specific workflows, local-language needs, and mobile-first usage of these shops. This project proposes AgriLedger, a mobile-first, multi-tenant agri-shop management system with Urdu-friendly interfaces and AI-assisted supplier price-list processing. The system will be evaluated for usability, data accuracy, and reliability using representative test data and pilot users where available.
EduVerse Atlas - A Complete 3D Smart Campus Platform
Students face difficulty in finding classrooms and understanding the correct timetable, because the campus is large and timetables have multiple confusing versions. There is no single platform for 3D campus view, timetable search, and navigation.
AI VoiceClone-X :Speech to Speech emotion Transfer
In this morden era there is no such perfect and practical tool for translate or understand the urdu to english .So ,this project will solve the issues while preserving the emotions of speaker whilr dubbing.
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Learning management system
Traditional learning systems rely on manual processes for sharing course materials, assignments, attendance, and communication between teachers and students. This creates difficulties in managing academic activities efficiently, tracking student progress, and providing timely feedback. The proposed Learning Management System aims to provide a centralized digital platform that improves learning, communication, and academic management.
AI-Based Facial Recognition Attendance System for CUI Vehari CS Faculty
Traditional faculty attendance systems are manual, time-consuming, and prone to human errors and proxy attendance. These methods require significant administrative effort and often produce inaccurate attendance records. Existing systems generally lack automated identity verification and real-time attendance management. This project aims to develop an AI-Based Facial Recognition Attendance System that automatically identifies Computer Science faculty members using facial recognition technology and marks attendance in real time. The system will improve attendance accuracy, eliminate proxy attendance, reduce manual workload, and provide secure attendance management with reports and analytics.
AI-Powered Adaptive Diet & Fitness System
Current diet and fitness applications often provide static recommendations that do not sufficiently adapt to changes in a user's behavior, adherence, progress, and fitness goals. Users may skip meals or workouts, making fixed plans less effective. This project aims to develop an AI-powered adaptive system that generates personalized diet and exercise plans and continuously analyzes user behavior to automatically adjust recommendations according to their progress and adherence
Mathematics eduplay learning
In the digital age, social media and online platforms have made it extremely easy for fake news and misleading information to spread rapidly. Many users are unable to verify the authenticity of news before sharing it, which leads to misinformation, confusion, and sometimes social or political harm. There is a lack of an accessible and automated system that can quickly analyze news content and determine whether it is real or fake. Therefore, this project aims to develop a Fake News Detection Platform that uses machine learning techniques to analyze news content and help users verify information before trusting or sharing it.
GharFix – On-Demand Home Service Platform
In many areas, finding reliable and skilled home service professionals (e.g., electricians, plumbers, carpenters, masons) is difficult, time-consuming, and often unsafe. People rely on word-of-mouth or random contacts, which leads to: Unverified workers Unclear pricing Delays and poor service quality No accountability or complaint system There is a need for a centralized, trusted, and efficient digital platform that connects customers with verified service providers for home-related tasks.
| Project Information | Status | Batch | Technologies | Action |
|---|---|---|---|---|
|
Multi-Tenant Agri-Retail with AI-Powered Supplier Price Intelligence
Many small agricultural-input shops in rural Pakistan manage customer credit (udhaar), sup...
|
Active | FA23 (2026) |
React, Next.js, TypeScript, Tailwind CSS
Node.js, PostgreSQL, Prisma ORM, Auth.js
Progressive Web App (PWA), Service Workers, Workbox, Indexed
+2
|
Details |
|
EduVerse Atlas - A Complete 3D Smart Campus Platform
Students face difficulty in finding classrooms and understanding the correct timetable, be...
|
Active | FA26 (2026) |
Three.js
Python
SQLite
+1
|
Details |
|
AI VoiceClone-X :Speech to Speech emotion Transfer
In this morden era there is no such perfect and practical tool for translate or understand...
|
Active | FA26 (2026) |
Python
React
Next.js
+4
|
Details |
|
dddddddddddddddddddddddddd
jjjjjjjjjjjjjjjjjjjjj...
|
Active | FA23 (2026) |
MongoDB
Express.js
React
+1
|
Details |
|
Learning management system
Traditional learning systems rely on manual processes for sharing course materials, assign...
|
Active | FA23 (2026) |
JavaScript
TypeScript
Python
+4
|
Details |
|
AI-Based Facial Recognition Attendance System for CUI Vehari CS Faculty
Traditional faculty attendance systems are manual, time-consuming, and prone to human erro...
|
Active | FA23 (2026) |
MongoDB
Express.js
React
+2
|
Details |
|
AI-Powered Adaptive Diet & Fitness System
Current diet and fitness applications often provide static recommendations that do not suf...
|
Active | FA23 (2026) |
Dart
Flutter
JavaScript
+10
|
Details |
|
Mathematics eduplay learning
In the digital age, social media and online platforms have made it extremely easy for fake...
|
Active | FA23 (2026) |
Python
JavaScript
React
+2
|
Details |
|
GharFix – On-Demand Home Service Platform
In many areas, finding reliable and skilled home service professionals (e.g., electricians...
|
Active | FA23 (2026) |
MongoDB
Express.js
React
+1
|
Details |
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AI-Powered Adaptive Diet & Fitness System
2. Fitness assessment using age, height, weight, goals, activity level, food preferences, and other relevant information.
3. AI-based personalized diet plan generation using a structured food and nutrition dataset.
4. AI-based personalized exercise and workout plan generation.
5. Daily tracking of meals, workouts, weight, and plan adherence.
6. Smart notifications for meals, workouts, and scheduled activities.
7. AI-based analysis of user adherence and behavioral patterns.
8. Automatic adaptive diet and exercise plan regeneration when significant deviations or missed activities are detected.
9. AI-based progress and weight-trend analysis.
10. Weekly AI-generated progress reports and recommendations.
11. Explainable AI recommendations showing reasons for major plan adjustments.
12. Admin dashboard for user, food, exercise, and system data management.
- 1. AI-powered personalized diet and exercise planning.
- 2. Adaptive AI that modifies plans based on user adherence and behavior.
- 3. AI-based detection of missed meals, workouts, and behavioral patterns.
- 4. Predictive analysis of fitness and weight progress.
- 5. Intelligent notifications based on user behavior.
- 6. Explainable AI showing why recommendations are changed.
- 7. AI-generated weekly progress and fitness reports.
- 8. Dataset-driven food and exercise recommendation engine.
- 9. Continuous user fitness profiling based on historical activity.
- 10. Proactive detection of declining adherence and adaptive intervention.
2. Recommendation Accuracy — AI recommendations should be consistent with the user's fitness goals, profile, preferences, and available dataset.
3. Data Privacy — User fitness, weight, diet, and activity data should be securely stored and protected.
4. Mobile Performance — The application should run smoothly with minimal lag during tracking, AI recommendations, and dashboard operations.
5. Notification Reliability — Diet, workout, and adaptive reminders should be delivered reliably according to the user's schedule.
6. Usability — Users should be able to easily understand their diet plans, workout plans, progress, and AI recommendations.
7. Scalability — The system should support multiple users and their individual fitness data without significant performance degradation.
8. Maintainability — The application should have a modular architecture allowing future AI models, datasets, and fitness features to be added easily.