Deep Neural Network Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Deep 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.

Latest Deep Neural Network Projects

  1. A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection
    The project aims to enhance security and privacy in Industrial Internet of Things networks by detecting malicious activities accurately. It focuses on improving traditional machine learning and deep learning methods that struggle with imbalanced and repetitive network data. The approach uses a self-attention-based deep convolutional neural network to monitor network behavior. It also applies data cleaning and feature filtering techniques to reduce redundancy and improve model performance. The system is tested on benchmark datasets and compared with existing models to show its effectiveness.
  2. 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.
  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. Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria
    This project studies how deep learning can detect heart problems from ECG signals. The researchers used a pre-trained neural network and applied explainable methods to see what the model learned. They analyzed which parts of the heart signals influenced the predictions the most. The results show that the model learned features similar to what doctors use in practice.
  6. BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation
    This project focuses on improving how computers identify organs in CT scans, like kidneys and livers. It introduces a method called BucketAugment that helps neural networks work well on new data from different hospitals. The method uses a smart learning process to find the best way to adjust images during training. Overall, it makes medical image analysis more reliable and flexible across different datasets.
  7. 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.
  8. Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing
    This project focuses on helping mobile devices handle heavy computing tasks more efficiently. Instead of doing all tasks on the device, some work is sent to powerful edge servers nearby. The goal is to finish tasks faster while using less battery. The project uses smart algorithms that learn over time to make the best decisions for offloading tasks.
  9. FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images
    This project focuses on improving medical image segmentation. It uses a new deep learning model called FCSN that looks at the whole image instead of just small parts. This helps the model predict object shapes more accurately and handle noise or blur better. The method is also fast and uses fewer resources than other popular models.
  10. 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.
  11. Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring
    This project focuses on using wearable device signals to detect heart problems. It studies PPG data, which can be measured continuously and easily by consumer devices. The goal is to find unusual patterns in PPG that indicate when an ECG test should be taken. The proposed method uses a deep learning model to accurately predict these heart abnormalities.
  12. Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks
    This project focuses on improving how wireless devices share the same communication channel without causing interference. It uses artificial intelligence to make sure all devices get a fair chance to send data. The system learns automatically to balance speed and fairness. This makes communication faster, more reliable, and efficient even when many users share the same signal space.
  13. Simulation-Aided Handover Prediction From Video Using Recurrent Image-to-Motion Networks
    This project teaches robots to work together by watching short video clips of motion. The robots learn to predict future movements and plan their actions safely. The system uses both real and simulated data to improve learning. It helps robots pass objects to each other accurately, even if they are not perfectly set up.
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At UniPhD, we provide complete guidance and support for Deep 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.

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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.

Deep 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 Deep Neural Network final year project.

Deep Neural Network Research Support for PhD Scholars

UniPhD offers advanced Deep 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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