Artificial Neural Networks Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Artificial Neural Networks 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 Networks Projects for Final Year Students

Latest Artificial Neural Networks Projects

  1. Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques
    This project focuses on improving how data moves across a network. It uses machine learning to predict traffic patterns for several time periods ahead. The method helps route data more efficiently, reducing sudden network changes. It also lowers the cost of monitoring the network while keeping performance close to the best possible.
  2. Deep Tensor Spectral Clustering Network via Ensemble of Multiple Affinity Tensors
    This project focuses on grouping data more accurately using a method called tensor spectral clustering. The researchers created a new network, TSC-Net, that learns the data patterns in one step. It reduces memory use by only looking at small parts of the data at a time. Tests show that it groups data better than older methods.
  3. ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
    This project focuses on predicting electricity demand for the near future. The researchers created a smart model that can understand complex patterns in past usage. It combines traditional smoothing methods with a deep learning network to make accurate predictions. Tests show it works better than many existing methods, even with fluctuating or seasonal data.
  4. PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep LearningBased Centroblast Cell Detection
    This project created a computer program called PseudoCell that can find specific cells called centroblasts in medical tissue images. It helps doctors by pointing out important areas without needing them to label every cell manually. The program saves time and makes the diagnostic process faster and easier. It can remove most irrelevant parts of the images while keeping the important ones.
  5. 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.
  6. PseudoCell Hard Negative Mining as Pseudo Labeling for Deep LearningBased Centroblast Cell Detection
    This project introduces PseudoCell, a system that automatically detects important cells called centroblasts in large tissue images. It reduces the need for pathologists to manually mark every cell. The system uses a mix of real labels and computer-generated hints to find relevant areas. This makes diagnosis faster and less labor-intensive for doctors.
  7. Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by PredictingSaaS Instance Configuration Using Deep Learning
    This project improves how cloud systems manage resources for AI-based applications. It uses a smart method called Deep-Hill to predict the best setup for each service running in the cloud. By doing this, it saves energy, reduces costs, and improves how efficiently the system works. It shows how artificial intelligence can make cloud computing faster and more effective.
  8. A Versatile Low-Complexity Feedback Scheme for FDD Systems via Generative Modeling
    This project develops a smart feedback method for wireless communication systems that have multiple antennas. It uses a statistical model to simplify how devices send channel information to the base station. The method reduces computation and improves data rates compared to traditional approaches. It also works well for both single-user and multi-user scenarios.
  9. Carbon Emission Prediction Through the Harmonization of Extreme Learning Machine and INFO Algorithm
    This project develops a smart computer model to predict carbon dioxide emissions more accurately. It combines two methods, INFO and ELM, to improve prediction results. The model studies how factors like economic growth, trade, and technology affect emissions. It helps create better environmental policies for a cleaner and more sustainable future.
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How We Help You with Artificial Neural Networks Projects

At UniPhD, we provide complete guidance and support for Artificial Neural Networks 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 Networks Thesis and Dissertation Writing

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

Artificial Neural Networks Research Support for PhD Scholars

UniPhD offers advanced Artificial Neural Networks 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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