Prediction Algorithms Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Prediction Algorithms 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.

Prediction Algorithms Final Year Projects

Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

This project focuses on predicting air quality using a smart computer method. The researchers improved a type of machine learning model by combining it with a genetic algorithm, which helps the model learn better and make more accurate forecasts. They tested it on real air quality data from a city in China and found that it predicts pollutants and the Air Quality Index faster and more accurately than other common methods. This can help in planning and managing air pollution more effectively.
A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

This project creates a smart platform for sharing computing tasks in a secure and efficient way. It uses blockchain to make transactions safe and transparent. The system predicts how long tasks will take using deep learning. Users can choose faster results or lower costs, letting slower computers still compete by offering cheaper prices.
MixNet Physics Constrained Deep Neural Motion Prediction for Autonomous Racing

This project focuses on predicting how other racecars will move around an autonomous racecar. It combines deep learning with physics rules to make predictions both accurate and safe. The method improves over traditional models by keeping predictions realistic and avoiding errors like going off-track. It was tested in simulations and used on a real autonomous racecar in a competition.
A Novel Artificial Spider Monkey Based Random Forest Hybrid Framework for Monitoring and Predictive Diagnoses of Patients Healthcare

This project focuses on detecting diseases like cancer, diabetes, and heart problems at an early stage using smart data analysis. It combines artificial intelligence with a Random Forest algorithm to identify subtle patterns in patient data and make accurate diagnoses. The system also uses secure data encryption to protect patient information. Tests show it is highly accurate, fast, and can help doctors make timely treatment decisions.
Applications of Artificial Intelligence in the Economy Including Applications in Stock Trading Market Analysis and Risk Management

This project studies how Artificial Intelligence (AI) can be used in economics. It looks at applications like stock trading, market analysis, and assessing financial risks. The research organizes different AI methods and explains how they are evaluated. It also highlights current challenges and suggests directions for future work.
Explainable Artificial Intelligence for Prediction of Non-Technical Losses in Electricity Distribution Networks

This project focuses on reducing electricity losses that are not caused by technical faults, especially in developing countries. It combines data from both electricity customers and distribution staff to better understand why losses occur. A deep learning model called NTLCONVNET was developed to predict these losses and explain which factors are most important. The study found that staff-related factors play a significant role, suggesting policies should include human resource monitoring to reduce losses.
Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera

This project develops an AI system to predict and detect potential robberies using indoor surveillance cameras. It uses three detection modules to identify head covers, crowds, and loitering behavior. The system combines advanced object detection and tracking with expert rules to decide robbery risk. Tests on real surveillance videos show it can detect robberies more accurately, helping operators prevent incidents and manage multiple cameras effectively.
A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning

This project predicts which roadside unit a moving vehicle will connect to next. By knowing this early, the network can store the needed data in advance and reduce delay for users. The system uses learning methods that allow each roadside unit to make its own predictions. Tests in different cities show that the method predicts vehicle movement with high accuracy, even in complex traffic conditions.
MD-MARS Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

This project aims to make wireless sensor networks work smoothly for a long time. It studies how sensors communicate and how to keep the network reliable. The researchers use simulations and machine learning to improve data flow and reduce congestion. Their method predicts network behavior accurately and shows that the system can be repaired quickly when problems occur.
Pixel Difference Unmixing Feature Networks for Edge Detection

This project builds a new deep learning model that detects edges in images. It uses fewer parameters, so it needs less memory and computing power. The model learns important details by combining information from different scales and improving how features are separated. Experiments show that it works better than many existing small models and performs almost as well as large models.

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Prediction Algorithms Final Year Projects with Source Code