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
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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Deep Learning-Based Glucose Prediction Models A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management
This project aims to predict blood sugar levels using data from wearable sensors. It uses deep learning models to make these predictions more accurate. The study compares different models and finds the best way to use personal and population data. It also provides a new dataset to help future research in glucose monitoring. -
DeepHealthNet Adolescent Obesity Prediction System Based on a Deep Learning Framework
This project focuses on predicting obesity in teenagers using artificial intelligence. It collects health data like height, weight, activity, and diet to make personalized predictions. The system helps teens understand their health risks and suggests timely actions. It works well even with limited daily data and gives more accurate results for both boys and girls. -
Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection
This project aims to predict how brain tumors grow in individual patients after surgery. The researchers used brain scans to guide their models, especially looking at how tumor cells move along brain pathways. They tested different models to see which predicted the tumor’s shape most accurately. The results showed that models using detailed brain pathway information worked best. -
FCSN Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images
This project is about improving how computers can identify and outline objects in medical images. The researchers created a new method called FCSN that looks at the whole image, not just small parts, to make more accurate predictions. FCSN is faster, uses less memory, and handles noisy or blurry images better than older methods. It was tested on different medical image datasets and showed better results in keeping the shapes of objects correct. -
GenHPF General Healthcare Predictive Framework for Multi-Task MultiSource Learning
This project creates a system that can easily use hospital data from different sources to predict patient outcomes. It turns medical records into readable text so computers can understand them better. The system works well even when data formats change across hospitals. This helps doctors and researchers use AI models more effectively for many medical prediction tasks. -
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. -
Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI
This project focuses on predicting blood sugar levels after meals for people with type 1 diabetes. It uses deep learning models that consider insulin doses, blood sugar before eating, and nutritional information from meals. The study also uses explainable AI to understand which factors most affect predictions. The goal is to help manage blood sugar better and support artificial pancreas development. -
Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning
This project presents a smart system to monitor patients with chronic and lifestyle-related health issues. It collects real-time data from wearable and home devices to track vital signs and daily activities. Using deep learning, it predicts health problems and gives preventive advice, even without Internet access. Tests show it is more accurate and effective than similar systems, especially in emergencies. -
A Simulation Framework for Cooperative Reconfigurable Intelligent Surface-Based Systems
This project studies how to improve wireless communication using multiple smart reflecting surfaces called RIS. It uses computer simulations to see how signals travel and change when they bounce off these surfaces. The study measures how often signals drop and how long outages last under different conditions. It shows that using several RIS together can make the connection more reliable than using just one. -
A Big Data-Driven Hybrid Model for Enhancing Streaming Service Customer Retention Through Churn Prediction Integrated With Explainable AI
This project focuses on predicting which customers are likely to stop using a streaming service. It studies how people use the service and applies smart computer models to find early warning signs of leaving. The system combines deep learning and machine learning to make accurate predictions. It also helps businesses understand why customers might leave so they can take steps to keep them.
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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.
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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.
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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.