Predictive Models projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Predictive Models 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 Predictive Models Projects

  1. A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0
    The project aims to improve cybersecurity in wireless sensor networks used in Industry 4.0. It focuses on detecting and preventing cyber-attacks in real time. Machine learning and deep learning algorithms are applied to classify and prioritize threats. The framework uses Decision Tree and MLP models for multi-class intrusion detection and an Autoencoder for binary classification. The goal is to provide accurate, intelligent, and prioritized protection for industrial networks.
  2. Comprehensive Review of Machine Learning, Deep Learning, and Digital Twin Data-Driven Approaches in Battery Health Prediction of Electric Vehicles
    The project aims to study how machine learning, deep learning, and digital twin methods can predict and manage battery health in electric vehicles. It focuses on improving battery performance, safety, and lifespan. The goal is to overcome limitations of traditional methods and make predictions more accurate, efficient, and applicable in real time. The study also looks at combining data-driven techniques with physics knowledge for better understanding. It surveys recent research to identify challenges and future opportunities in this field.
  3. Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management
    This project uses data from wearable sensors to predict a person’s blood sugar levels. It applies deep learning models to understand which methods and inputs give the most accurate results. The study compares different model types and personal data amounts to find what works best. It also provides a new dataset to help other researchers study glucose prediction.
  4. DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
    This project focuses on predicting obesity in children and teenagers using artificial intelligence. It collects health data like height, weight, diet, and activity levels to give personalized feedback. The system, called DeepHealthNet, can make accurate predictions even with limited daily data. It helps identify health risks early and guide adolescents to make better lifestyle choices.
  5. 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.
  6. GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status
    This project uses computer analysis of CT scans to predict a gene mutation called EGFR in lung cancer patients. It can highlight the most suspicious area in the tumor, helping doctors take more accurate biopsies. The method uses a special deep learning model that learns from both the images and tumor features. Tests showed it works better than older techniques and can help guide treatment decisions.
  7. Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study
    This project develops a smart system to detect multiple heart diseases from ECG signals. It can learn even with very few labeled examples. The system improves accuracy by creating extra training data and learning patterns of disease combinations. It works well even on new data that the system has not seen before.
  8. 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.
  9. 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.
  10. Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines
    This project focuses on teaching a computer to convert MRI scans of spines into CT-like images. It also measures how confident the computer is about its predictions. By looking at uncertainties, the model can better separate bones from soft tissues. This helps doctors and researchers trust the results more when no expert labels are available.
Did you like this research project?

To get this research project Guidelines, Training and Code…

Chat with Expert

How We Help You with Predictive Models Projects

At UniPhD, we provide complete guidance and support for Predictive Models 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.

Predictive Models Thesis and Dissertation Writing

UniPhD has a team of experienced academic writers who specialize in Predictive Models research and thesis development. We offer fast-track dissertation writing services to help you complete your Predictive Models 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.

Predictive Models 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 Predictive Models final year project.

Predictive Models Research Support for PhD Scholars

UniPhD offers advanced Predictive Models 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.

Related Posts