Feature Extraction projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Feature Extraction projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Feature Extraction Projects
-
A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
This project aims to develop an intelligent deep learning system for classifying diabetic retinopathy based on fundus images. It focuses on identifying both early and severe retinal lesions that previous models often ignored. The approach combines GoogleNet and ResNet with an adaptive particle swarm optimizer to improve feature extraction. The extracted features are then tested with machine learning models such as random forest and SVM. The hybrid model achieves high accuracy and shows strong potential for improving diagnosis of DR severity levels. -
A Novel Feature Encoding Scheme for Machine Learning Based Malware Detection Systems
This project aims to improve malware detection by focusing on how data features are encoded before training machine learning models. It introduces a new entropy-based feature encoding method that enhances the accuracy and stability of malware classification. The approach is tested on benchmark datasets such as KDDCUP99, UNSW-NB15, and CIC-Evasive-PDFMal2022 to evaluate performance. Results show that models using the proposed encoding achieve higher F1 scores compared to traditional encoding techniques. The study also examines how different encodings affect the importance of features in malware detection. -
A Novel Hybrid Deep Learning Architecture for Dynamic Hand Gesture Recognition
The main objective of this project is to improve the recognition of dynamic hand gestures in real-world videos. The study aims to extract meaningful features from gesture videos and classify them accurately. It combines a convolutional neural network with a recurrent neural network to handle spatial and temporal information. The project focuses on six common gestures and seeks to achieve higher accuracy in realistic environments. Another goal is to compare the proposed model’s performance with existing benchmark methods. -
Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning
The project aims to develop an objective method to assess motor symptoms in Parkinson’s disease patients. It uses a Leap Motion sensor to capture hand movements during standard tasks. The movement data are processed to extract detailed features. Machine learning techniques are then applied to select the most important features and predict symptom severity. The approach improves accuracy and reduces reliance on expert evaluation. -
Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification
The project aims to classify ECG heartbeat signals to help detect heart conditions early. It focuses on using machine learning methods to analyze ECG data efficiently and accurately. The MIT-BIH arrhythmia dataset is used, grouped into five main beat types. Different classifiers are tested, and Random Forest achieves the best performance. The work highlights automated ECG analysis as a tool for clinical and remote monitoring applications. -
A Deep Learning Approach for Fear Recognition on the Edge Based on Two-Dimensional Feature Maps
This project uses wearable sensors to detect fear in real time. It analyzes body signals using artificial intelligence to identify emotions. The system can work on small devices and could help keep people safe in dangerous situations. -
A Transformer-Based Knowledge Distillation Network for Cortical Cataract Grading
This project focuses on automatically grading cortical cataracts, a type of eye disease that is hard to detect. The system uses advanced AI called a Transformer to carefully analyze eye images. It breaks the eye into zones and looks at important details like location, size, and density of the cataract. The method can handle missing information and uncertain data, making it more accurate than previous approaches. -
Contrastive Transfer Learning for Prediction of Adverse Events in Hospitalized Patients
This project focuses on predicting serious health problems in hospitalized patients before they happen. It uses a computer-generated score called the deterioration index to track patient condition over time. A special learning method helps the computer understand patterns in these scores and make accurate predictions. Hospitals can use this system as an early warning to provide timely care and prevent complications. -
Fetal-BET: Brain Extraction Tool for Fetal MRI
This project focuses on automatically identifying the fetal brain in MRI images. The researchers created a large dataset of fetal brain scans from different MRI types. They used deep learning to teach a computer to detect and separate the brain from other tissues. This method works accurately even when the scans are from different machines or show abnormal brains. -
From Clustering to Cluster Explanations via Neural Networks
This project focuses on making machine learning easier to understand. It explains why data points are grouped into certain clusters. The method turns clustering models into neural networks to see which features influence the grouping. It helps researchers check cluster quality and find new insights in the data. -
Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis
This project develops a computer system to detect early signs of stomach cancer from images taken during endoscopy. It looks at three main parts of the stomach and uses patterns in these areas to make predictions. The system can identify high-risk patients quickly and without needing biopsies. It is more accurate and faster than existing methods. -
Improving Generalization of ML-Based IDS With Lifecycle-Based Dataset, Auto-Learning Features, and Deep Learning
This project focuses on making computer systems better at detecting cyber attacks. The researchers created a smart model that can learn patterns from attack sequences and features automatically. They tested it on multiple datasets and found it can identify new, unseen attacks much more accurately than older methods. The approach helps make network security stronger and more reliable. -
LAFIT: Efficient and Reliable Evaluation of Adversarial Defenses With Latent Features
This project studies how deep learning models, especially convolutional neural networks, can be tricked by tiny changes in input that humans cannot notice. The research introduces a new method called LAFIT to test how strong these models are against such attacks. It shows that using hidden information inside the model can make attacks more effective. The work helps make AI systems safer by better evaluating their weaknesses. -
Lymphocyte-Infiltrated Periportal Region Detection With StructurallyRefined Deep Portal Segmentation and Heterogeneous Infiltration Features
This project focuses on helping doctors diagnose hepatitis more accurately. It uses deep learning to automatically find regions in the liver affected by immune cells called lymphocytes. These regions are hard to see because their boundaries are irregular. The system can detect them reliably and provide information that matches liver disease severity and liver function tests. -
Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers
This project introduces a new computer system called DeepRAP that studies detailed images of human skin. It helps doctors see skin layers and tiny blood vessels using special 3D scans without needing a biopsy. The system can track skin diseases like psoriasis and even study how skin changes with age. It makes the process faster, more accurate, and less painful for patients. -
Masked Modeling-Based Ultrasound Image Classification via SelfSupervised Learning
This project uses artificial intelligence to improve how ultrasound images are analyzed. It teaches a computer to understand images without needing human labeling. The system learns by filling in missing parts of images, helping it recognize patterns more effectively. This method makes ultrasound image classification more accurate, even when the images are unclear or noisy. -
TBCA: Prediction of Transcription Factor Binding Sites Using a Deep Neural Network With Lightweight Attention Mechanism
This project focuses on predicting where proteins called transcription factors attach to DNA, which helps control gene activity. The researchers developed a new computer method that looks at both the DNA letters and the 3D shapes of DNA. Their method uses advanced techniques to find important patterns and combines them to make more accurate predictions. The results show it works better than older methods and helps understand how DNA shapes affect protein binding. -
MASA-TCN Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
This project focuses on understanding emotions from brain signals recorded by EEG. The researchers created a new model called MASA-TCN that can both predict exact emotional levels and classify emotions into categories. The model looks at patterns across different brain regions and over time to better detect subtle emotional changes. Tests show it performs better than previous methods on standard datasets. -
NF-GAT A Node Feature-Based Graph Attention Network for ASD Classification
This project focuses on detecting autism in individuals using brain imaging data. Each person’s brain is represented as a network of connected regions. The method learns important patterns in these connections to identify autism. The results show this approach works better than other existing methods. -
TBCA Prediction of Transcription Factor Binding Sites Using a Deep Neural Network With Lightweight Attention Mechanism
This project focuses on finding important sites on DNA where proteins called transcription factors attach. The researchers developed a new method that uses advanced neural networks to study both the DNA letters and the shape of the DNA. Their approach combines detailed local information with overall patterns to make more accurate predictions. Tests show that this method works better than previous techniques and can help understand how genes are regulated.
Did you like this research project?
To get this research project Guidelines, Training and Code…
How We Help You with Feature Extraction Projects
With over 15 years of experience and thousands of successfully completed academic and research projects, our trusted research team provides end-to-end guidance for B.Tech, M.Tech, MS, and PhD scholars. We deliver original, plagiarism-checked work, follow university standards, and offer reliable support from project implementation to documentation, thesis writing, and research publication.