Artificial Neural Network Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Artificial Neural Network ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year 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.

Artificial Neural Network Projects for Final Year Students

Latest Artificial Neural Network Projects

  1. A Novel Web Framework for Cervical Cancer Detection System – A Machine Learning Breakthrough
    The main objective of this project is to enable early and accurate detection of cervical cancer using a web-based expert system. It aims to analyze patient data with multiple machine learning algorithms to identify cancer cases reliably. The system focuses on improving diagnosis accuracy, sensitivity, and specificity. It seeks to highlight the most effective algorithms for detection and provide a practical tool to support healthcare professionals. Overall, the project strives to reduce mortality from cervical cancer and enhance women’s healthcare globally.
  2. Accelerating Neural ODEs Using Model Order Reduction
    This project makes neural networks faster and more efficient by using ideas from physics and mathematics. It focuses on Neural ODEs, which normally take a long time to run. The researchers use a special method called model order reduction to simplify the calculations without losing accuracy. Their approach works well for tasks like image and time-series classification, making these networks usable on devices with limited resources.
  3. CLADSI: Deep Continual Learning for Alzheimer’s Disease Stage Identification Using Accelerometer Data
    This project uses motion sensors to monitor the movements of Alzheimer's patients. A smart computer model, called a CNN, studies this data to detect the stage of the disease. The model can keep learning as new data comes in, without needing all past data. This helps doctors track patients continuously and supports better care.
  4. 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.
  5. DeepSIG A Hybrid Heterogeneous Deep Learning Framework for Radio Signal Classification
    This project develops a smart system called DeepSIG to identify types of radio signals. It combines three different AI models to look at signals in multiple ways, like sequences, images, and graphs. The system learns from all these views together to make more accurate predictions. Tests show it works better than using any single method, especially when only a few signals are available.
  6. Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5G RAN Network Slicing
    This project focuses on improving how 5G networks share resources among multiple users. It introduces a way to give priority to different network slices so each gets fair performance. The study uses machine learning to speed up decisions about resource allocation. The results show that these methods work fast and accurately, but extra checks are needed when network conditions change or traffic is high.
  7. Intrusion Detection in Cyber-Physical Grid Using Incremental ML With Adaptive Moment Estimation
    This project develops a smart system to detect cyber-attacks in power grids. Unlike traditional methods, it can learn and adapt to new, unknown attacks without starting from scratch. The system uses a neural network that updates itself with new threat information. Tests show it can detect attacks with very high accuracy in both simulated and real datasets.
  8. A Benchmarking on Optofluidic Microplastic Pattern Recognition: A Systematic Comparison Between Statistical Detection Models and ML-Based Algorithms
    This project focuses on detecting tiny plastic particles called microplastics using computer models. It compares traditional statistical methods with modern machine learning techniques to see which works best. The study finds that as more data is analyzed, detection becomes more accurate. Among all models, Support Vector Machine, Linear Discriminant Analysis, and Naive Bayes perform the best.
  9. A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning
    This project focuses on detecting faults in electric motor windings. It combines a modeling method and a data-based method to identify problems caused by epoxy issues. The model simulates motor behavior, while data analysis helps find patterns linked to faults. This approach improves accuracy in detecting hidden motor defects that traditional methods might miss.
  10. A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication
    This project develops a smart system to protect drones from cyberattacks. It uses machine learning to detect and prevent security threats in drone networks. The model combines artificial neural networks and genetic algorithms to improve accuracy and reduce processing time. It performs faster and predicts attacks more accurately than other existing methods.
  11. A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network
    This project focuses on predicting renewable energy generation to ensure a steady power supply. It collects and cleans energy data, then uses deep learning to forecast how much power will be produced. Several neural network models work together to improve accuracy. The system helps manage variations caused by weather and time, making renewable energy more reliable for users.
  12. CardioGPT An ECG Interpretation Generation Model
    This project uses CardioGPT to read and interpret heart signals from ECGs. It explains the results in simple language like a doctor would. The model accurately detects heart problems and performs better than older AI models.
  13. Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting
    This project focuses on predicting river water flow using deep learning methods. It studies 28 years of data from the Johor River in Malaysia to understand how rainfall and other factors affect streamflow. The researchers used advanced neural network models to make more accurate predictions and measure uncertainty in results. This helps improve water resource planning and flood management.
  14. Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach
    This project studies how to reduce network congestion in 5G systems using machine learning. It compares many algorithms to find which ones best predict where congestion will happen. The researchers tested both supervised and unsupervised learning methods. In the end, they selected the five best-performing algorithms for accurate congestion prediction.
  15. Machine Learning Algorithms for Forecasting and Categorizing Euro-toDollar Exchange Rates
    This project uses machine learning to predict how the value of the euro changes compared to the dollar. It combines different models to find the best times to buy or sell euros. The system analyzes past market data and patterns to make accurate predictions. This helps investors make smarter trading decisions.
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How We Help You with Artificial Neural Network Projects

At UniPhD, we provide complete guidance and support for Artificial Neural Network ieee 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 guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.

Artificial Neural Network Thesis and Dissertation Writing

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

Artificial Neural Network 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.

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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 Artificial Neural Network final year project.

Artificial Neural Network Research Support for PhD Scholars

UniPhD offers advanced Artificial Neural Network 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.

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