Real-Time Systems Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Real-Time Systems projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Real-Time Systems Final Year Projects

Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques

This project uses computer vision and machine learning to understand students’ emotions in a classroom. It tracks facial gestures to identify feelings like interest, boredom, or enthusiasm during learning. The system builds a database of real, spontaneous emotions and helps teachers evaluate students’ emotional engagement along with their learning progress. It focuses on supporting teachers in face-to-face education.
High-Efficiency Resource Allocation Scheme Introducing the Concept of Resource Sharing Paths in Industrial IoT

This project focuses on improving industrial wireless networks used in smart factories. It addresses the problem of resource wastage when many devices send data along overlapping routes. The researchers propose a method that shares communication resources on common paths and considers network conditions like congestion. Their simulations show this approach uses resources more efficiently and handles more devices than traditional methods.
Online Black-Box Modeling for the IoT Digital Twins Through Machine Learning

This project focuses on creating a digital copy, or "twin," of a real system using only input and output data. The method works in real time and uses very little hardware, making it suitable for small devices like microcontrollers. It can predict the system’s behavior accurately, helping with tasks like maintenance, fault detection, and monitoring in IoT and Industry 4.0 applications. Experiments show it can learn and predict fast, with errors similar to the measurement limits of the system.
Assessing the Benefits of ChatGPT for Business An Empirical Study on Organizational Performance

This study looked at how ChatGPT affects businesses. It focused on three aspects: the quality of the system, the information it provides, and the service it offers. The research found that all three improve user satisfaction and benefits, with service quality having the strongest effect. Currently, ChatGPT does not strongly influence flexible organizational culture, but it could boost long-term performance as it develops.
Comparison of Real-Time and Batch Job Recommendations

This project focuses on improving recommendation systems that suggest items like jobs to users. It compares traditional batch-based methods with a real-time approach that considers users’ latest actions. Using a graph-based model, the real-time system gave better results, leading to more user engagement. The study shows how real-time recommendations can be more effective in practical applications.
Design of an Intelligent Educational Evaluation System Using Deep Learning

This project focuses on building a smart system to evaluate students’ learning online. It uses deep learning to process information and make accurate assessments. The system is designed to be fast and efficient, using fewer parameters and less training time than traditional methods. It also works well even when the data is uneven or unbalanced.
Heterogenous Social Media Analysis For Efficient Deep Learning Fake-Profile Identification.

This project focuses on detecting fake social media accounts. It collects and analyzes data like posts, comments, images, videos, and user activities. The system uses deep learning to find patterns that show an account is fake. Tests show it can identify fake accounts with over 93% accuracy.
Research on ATO Control Method for Urban Rail Based on Deep Reinforcement Learning

This project develops an intelligent system to control urban trains automatically. It uses a deep learning algorithm to adjust the train’s speed and stops in real time. The system improves punctuality, parking accuracy, and saves energy. Tests on a Beijing subway line show it works better than traditional control methods.
The Biomechanical Analysis on the Tennis Batting Angle Selection under Deep Learning

This study analyzes how the strength and angle of a tennis player’s swing affect their performance. It uses video images and a deep learning model to track and evaluate player movements in real time. The system compares joint angles and motion with other methods and shows better accuracy. Results reveal how specific joint positions, like the ankle and knee, influence hitting speed and success in volleys.
Artificial Intelligence for sEMG-Based Muscular Movement Recognition for Hand Prosthesis

This project focuses on using muscle signals from the arm to control prosthetic hands for people with physical disabilities. The researchers collected signals from volunteers performing different hand movements and processed the data to remove noise and reduce complexity. They then used machine learning and neural networks to classify these movements accurately. The system achieved high accuracy, showing it can reliably recognize and control hand motions in real time.
Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality

This project focuses on creating a virtual copy of a real-world object, called a digital twin, which can monitor and manage it using data from sensors. The digital twin is classified into six levels, from basic monitoring to fully autonomous operation, to show its capabilities. The study uses a smart house to demonstrate how these digital twins can be built and visualized in virtual reality. This approach helps users understand and improve the performance of physical assets.
Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

This project focuses on detecting faulty sensor nodes in wireless sensor networks. Traditional methods are slow or complex, so the authors propose a faster method called a likelihood ratio test (LRT). It checks sensor data over time and decides if a node is faulty. Tests show it detects faults accurately with almost no false alarms.
EN-LAKP Lightweight Authentication and Key Agreement Protocol for Emerging Networks

This project focuses on making wireless sensor networks more secure and efficient using Software Defined Networking. It introduces a Lightweight Authentication and Key Agreement Protocol to protect communication between devices. The system is tested for security using formal methods and shows improved performance compared to similar protocols. It helps prevent attacks while reducing computing and communication overhead.
Link Characterization and Edge-Centric Predictive Modeling in an Ocean Network

This project focuses on improving internet communication for fishing vessels at sea. It studies how weather, waves, and antenna movement affect wireless signals. The researchers use a combination of past and real-time data to predict signal strength using a smart learning system. This helps make sea communication more reliable and efficient.

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Real-Time Systems Final Year Projects with Source Code