Machine Learning projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Machine Learning 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 Machine Learning Projects

  1. A Framework for Interpretability in Machine Learning for Medical Imaging
    The main objective of this project is to clarify and formalize the concept of interpretability in machine learning models for medical imaging. It aims to define why interpretability is important and what goals it should achieve in real-world medical tasks. The project identifies five core elements of interpretability including localization, visual recognizability, physical attribution, model transparency, and actionability. It also proposes a structured framework to guide researchers and practitioners in designing interpretable models. Overall, the project seeks to improve the understanding, design, and practical use of machine learning in medical imaging while suggesting directions for future research.
  2. An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network
    This project focuses on detecting epilepsy using brain signals. The researchers combined two datasets and improved them to train a small, efficient neural network. The model can accurately identify epilepsy with high reliability. It works well even on low-cost devices and wearable technology, making real-time detection possible.
  3. An Introduction to Adversarially Robust Deep Learning
    This project studies how deep learning models, which are used in fields like medical imaging and speech recognition, can be easily fooled by tiny changes in input data. It reviews past research on making these models more reliable. The work explains why previous solutions did not fully succeed. It also points out new directions for improving the safety and robustness of these systems.
  4. An Overview of Data Integration in Neuroscience With Focus on Alzheimer’s Disease
    This project explores how to combine different types of medical and scientific data to better understand complex brain diseases. It explains the challenges of working with large and noisy datasets. The study gives guidance for beginners in data integration. It also shows how these methods can help in early detection of Alzheimer's Disease using machine learning.
  5. BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs
    This project created BeatProfiler, a computer tool that studies heart cell function in the lab. It measures how heart cells contract, handle calcium, and respond to drugs. The tool works faster and more accurately than older methods. It can also use machine learning to identify heart diseases and classify drug effects.
  6. Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation
    This project studies slow and deep breathing exercises to improve relaxation and heart function. It compares different breathing styles and measures how they affect the heart using simple signals. The study also uses computer programs to identify breathing patterns automatically. The results show which breathing method works best immediately and which one has longer-lasting benefits.
  7. 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.
  8. 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.
  9. From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People
    This project studies how to monitor sleep using a small, comfortable ear sensor instead of traditional scalp electrodes. Researchers used existing large EEG datasets to train a computer model and then improved it by fine-tuning with ear-EEG data. The approach works well for older adults and matches expert sleep stage classifications closely. This method could help track sleep remotely and non-invasively, especially in elderly patients.
  10. Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory
    This project studies how texts written by GPT-2 compare with human-written novels and programming code. The researchers analyzed patterns, repetitions, and structures in the texts. They then created a system that can measure these patterns and classify the texts automatically. The study helps us understand how AI-generated texts work and can improve tools for text analysis, plagiarism detection, and fake news spotting.
  11. Identifying Biases in a Multicenter MRI Database for Parkinson’s Disease Classification: Is the Disease Classifier a Secret Site Classifier
    This project studies how deep learning models for Parkinson's disease (PD) classification can unintentionally learn unrelated information from MRI scans. The researchers tested if the model could detect patient sex, scanner type, or hospital location even though it was only trained to detect PD. They found that the model could do this with high accuracy, showing it may rely on shortcuts instead of true disease patterns. This explains why such models sometimes fail on data from new hospitals.
  12. 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.
  13. Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management
    This project focuses on creating and testing artificial chest X-ray images to help doctors diagnose lung diseases. The team developed a method called SPINE to check if these synthetic images are accurate and useful. They compare the fake images with real ones using expert reviews, data analysis, and computer tests. The goal is to make safe and reliable synthetic medical images for research and clinical use.
  14. LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
    This project is about detecting unusual events in computer system logs. The proposed system, called LogFiT, can learn normal log patterns on its own without needing labeled data. It uses a smart language model to understand log content and identify when something abnormal happens. Tests show that LogFiT is more accurate and flexible than existing methods.
  15. 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.
  16. Multivariate Time Series Characterization and Forecasting of VoIP Traffic in Real Mobile Networks
    This project studies how voice calls over mobile networks behave in real time. The researchers collected a large amount of data from a real LTE network and analyzed it to see how different factors affect call quality. They used computer models and machine learning to predict future performance of the network. The goal is to help network operators plan better and improve the quality of voice calls.
  17. SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers
    This project uses artificial intelligence to improve data transmission in communication systems. It replaces traditional methods with a neural network called SICNN, which reduces errors and works faster. The system can adapt to different types of communication setups. The researchers also created better ways to train the network, making it more accurate at high signal quality.
  18. Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations
    This project focuses on improving eye scans called OCTA, which show the blood vessels in the retina. The researchers created a way to make realistic fake images of these vessels to help train computers. Their method helps the computer find even the smallest blood vessels more accurately. They also shared all their code and data so others can use it.
  19. Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records
    This project focuses on understanding how different blood pressure medicines affect cancer risk using hospital records. The researchers created a computer model that learns from patient data to find cause-and-effect relationships. Their model predicts risk more accurately than traditional methods, even with limited data. The results match what clinical trials have already found.
  20. UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification
    This project uses artificial intelligence to analyze medical images. It can learn from a small number of labeled images and many unlabeled ones. The system extracts important features from unlabeled data and improves its accuracy with labeled data. It achieves very high accuracy even with only half the labeled data.
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How We Help You with Machine Learning Projects

At UniPhD, we provide complete guidance and support for Machine Learning projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

Our experts have extensive experience guiding students in computer science, electronics, and electrical domains, ensuring successful completion of academic and research projects.

Machine Learning Thesis and Dissertation Writing

UniPhD has a team of experienced academic writers who specialize in Machine Learning research and thesis development. We offer fast-track dissertation writing services to help you complete your Machine Learning thesis or dissertation smoothly and on time.

Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.

Machine Learning Research Paper Publishing Support

UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.

Project Synopsis and Presentation Support

We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Machine Learning final year project.

Machine Learning Research Support for PhD Scholars

UniPhD offers advanced Machine Learning research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.