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

Classification Algorithms Final Year Projects

A Distracted Driving Detection Model Based On Driving Performance

This project studies how a driver behaves when fully focused and when mentally distracted. Researchers collected driving data from many people using a simulator. They trained a deep learning model to recognize whether a driver is distracted just from the way they drive. The model works very well and can help detect unsafe driving in real time.
A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

This project focuses on detecting Autism Spectrum Disorder (ASD) early using machine learning. It compares different ways of preparing data and several simple machine learning methods to see which works best. The study tests these methods on datasets for toddlers, children, adolescents, and adults. The results show high accuracy and identify the most important factors for predicting ASD, helping doctors make better decisions.
Constructing a Meta-Learner for Unsupervised Anomaly Detection

This project focuses on automatically picking the best anomaly detection method for any given dataset without needing labeled data. The researchers created a meta-learning system that looks at characteristics of the dataset to suggest the most suitable algorithm. They tested it on over 10,000 datasets and found it works better than existing methods. The study also shows that while a few dataset features are enough, the choice of the meta-learning model strongly affects performance.
Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection

This project focuses on detecting insider threats, which are harmful actions by people who have authorized access to an organization’s network. Traditional security tools often fail, so the study uses machine learning to improve detection. It combines supervised and semi-supervised learning, analyzes data streams, and retrains models periodically. The best results were achieved using the Isolation Forest algorithm, showing good accuracy in identifying both harmful and safe activities.
Failure to Achieve Domain Invariance With Domain Generalization Algorithms An Analysis in Medical Imaging

This project studies how well deep learning methods can handle new data that is different from what they were trained on. The researchers tested eight popular algorithms on medical images and regular photos. They found that all methods perform similarly, and none fully remove data-specific biases. The study shows that current techniques still struggle to generalize to unseen data, especially in important fields like medical imaging.
Leveraging Brain MRI for Biomedical Alzheimers Disease Diagnosis Using Enhanced Manta Ray Foraging Optimization Based Deep Learning

This project focuses on improving the diagnosis of Alzheimer’s disease using brain MRI scans. It uses deep learning to automatically analyze images and extract important features, reducing the need for manual input from experts. The method combines a DenseNet model for feature extraction with an optimized neural network for classification. Tests show that this approach gives more accurate results than existing techniques.
Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

This project focuses on automatically detecting diseases in the gastrointestinal tract using images from a tiny camera capsule. The researchers developed a computer program that cleans the images, extracts important features, and classifies diseases like bleeding, ulcers, and polyps. They combined advanced deep learning techniques with optimization algorithms to improve accuracy. Tests on a medical image database showed the system can correctly identify diseases with over 98% accuracy.
Enabling IoT Service Classification A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

This project studies how different Internet of Things (IoT) services interact and how they can be organized efficiently. It classifies IoT services into five groups based on their key characteristics. Machine learning methods like decision trees, SVM, and voting classifiers are used to make this classification. The results show that decision trees provide accurate and reliable predictions, helping improve IoT resource management.
Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset IDSAI

This project focuses on improving computer security in networks of connected devices, like IoT systems. The researchers created a new dataset of real attacks to train and test machine learning models. They found that certain AI models can accurately detect both simple and multiple types of attacks, reaching over 90% accuracy. This work helps make network security smarter and more reliable.
MAC Protocol Based IoT Network Intrusion Detection Using Improved Efficient Shuffle Bidirectional COOT Channel Attention Network

This project focuses on protecting IoT networks from cyberattacks. It uses a smart system to detect intrusions while keeping data secure and reducing energy use and delays. The approach balances and processes IoT data, selects important features, and then classifies attacks using an advanced AI model. Overall, it improves accuracy and performance compared to existing methods.
Quantum Artificial Hummingbird Algorithm for Feature Selection of Social IoT

This project creates a smart method to pick only the most useful information from large Internet of Things data. It uses an improved search technique inspired by hummingbird behavior and quantum ideas. The method helps computers work faster and make better decisions with fewer inputs. Tests on many datasets show that it improves accuracy while reducing unnecessary data.
A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course

This project focuses on detecting forest wildfires using artificial intelligence and image processing. It splits the problem into two parts: identifying images with wildfires and locating the wildfire areas in the images. The researchers developed new algorithms that use machine learning and deep learning, achieving high accuracy. The system is designed to be practical for students, helping them learn while producing reliable wildfire detection results.
A Novel Spatio Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

This project focuses on improving driver assistance systems using only GPS data. The researchers developed a method to turn GPS trajectories into images and trained a neural network to analyze them. The system can accurately detect when a car is turning or going straight. It works better than existing methods and helps make driving safer and smarter.
An Efficient Parameter Estimation Algorithm of the GTD Model Based on the MMP Algorithm

This project introduces a faster and more reliable method to analyze radar signals. It helps identify important target features even when the data has a lot of noise. The new algorithm works much quicker than older methods and still keeps high accuracy. It makes radar measurement processing more efficient and stable.
BukaGini A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance

This project develops a new algorithm called BukaGini to study how different features in data interact with each other. It uses a special technique based on the Gini index to capture both simple and complex relationships between features. The method was tested on datasets about student performance, cancer types, spam emails, and network attacks. Results show that BukaGini improves accuracy compared to traditional methods, making it useful for many machine learning applications.
Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model

This project focuses on improving the detection of Android malware, which is increasingly common and hard to identify using traditional methods. It uses a combination of advanced machine learning and deep learning techniques to automatically classify malware. The model selects the most important features and optimizes its parameters to achieve high accuracy. Tests show that this approach can detect malware more effectively than existing methods, reaching almost 99% accuracy.
Research on Asparagus Recognition Based on Deep Learning

This project focuses on making asparagus farming faster and more efficient. It uses a computer program to quickly detect asparagus plants for mechanized harvesting. The program is accurate and works well even with interference. This approach helps reduce labor costs and supports modern, large-scale farming.
A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

This paper explains how smart group-based algorithms can help build and train artificial neural networks more effectively. These algorithms search for better network designs and improve how the network learns. The authors review many types of these algorithms and show how they are used to improve different neural networks. It gives readers a clear picture of current methods and their applications.
Interpretable Multi-Criteria ABC Analysis Based on Semi-Supervised Clustering and Explainable Artificial Intelligence

This project focuses on organizing inventory items into different priority classes to help managers control stock better. It improves existing methods by explaining why each item is assigned to a particular class. The approach ensures that items follow the Pareto principle, meaning a small number of items account for most value. The method was tested on a chemical distribution company and showed accurate and clear inventory classification.
Travel Direction Recommendation Model Based on Photos of User Social Network Profile

This project creates a smart travel recommendation system using photos from a user’s social media account. It analyzes images and related data to suggest countries the user might like to visit. The system uses machine learning methods to compare, classify, and group data for accurate suggestions. Tests show it can correctly predict trips most of the time, and it can improve further by using photo location information.

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Classification Algorithms Final Year Projects with Source Code