Deep Learning Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Deep Learning 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 Learning Projects
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A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System
This project created a new CPR training system that listens to the sounds made during chest compressions. The system uses these sounds to check how deep, fast, and well the compressions are done. It uses a smartphone and special sounds from a training device to give accurate feedback. This helps people practice CPR at home without expensive equipment. -
A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses
This project develops a new laparoscope for minimally invasive surgery. It uses special liquid lenses to zoom in and focus without moving parts. The system can create 3D images from a single camera in real-time. This helps surgeons see organs and lesions more clearly and judge depth accurately during surgery. -
A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network
This project focuses on finding the direction from which signals come in a three-dimensional space. It uses a special type of artificial intelligence called a deep convolutional neural network to analyze data from a small antenna array. The system can accurately predict angles from any direction, even when multiple signals arrive at the same time. The goal is to make signal detection faster, precise, and reliable in different conditions. -
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
This project studies offline reinforcement learning, which teaches computers to make decisions using only existing data instead of interacting with the real world. It explains different methods, compares how well they work, and points out their strengths and weaknesses. The study also highlights gaps and suggests future research directions. It helps researchers understand which approaches are best for various problems in areas like healthcare, education, and robotics. -
A Survey on Reconfigurable Intelligent Surface for Physical Layer ecurity of Next-Generation Wireless Communications
This project studies new ways to make future 6G wireless networks faster and more secure. It focuses on using special smart surfaces that can control signals to prevent eavesdropping. The research reviews different methods to improve security for various network types. It also discusses challenges and ideas for future wireless systems. -
A Transformer-Based Knowledge Distillation Network for Cortical Cataract Grading
This project focuses on automatically grading cortical cataracts, a type of eye disease that is hard to detect. The system uses advanced AI called a Transformer to carefully analyze eye images. It breaks the eye into zones and looks at important details like location, size, and density of the cataract. The method can handle missing information and uncertain data, making it more accurate than previous approaches. -
Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges
This project studies how smart transportation systems can be improved using deep learning. It looks at how traffic, vehicles, roads, and weather affect transportation. The study explains modern AI methods for predicting traffic, recognizing vehicles, and monitoring road conditions. It also highlights challenges and future ideas to make transportation safer and more efficient. -
An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network
This project focuses on detecting epilepsy using brain signals. The researchers combined two datasets and improved them to train a small, efficient neural network. The model can accurately identify epilepsy with high reliability. It works well even on low-cost devices and wearable technology, making real-time detection possible. -
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. -
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. -
Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification
This project focuses on detecting lung problems in children using recorded breathing sounds. It uses a computer program that learns patterns in these sounds to tell if a child has a respiratory disease. The system works remotely, so doctors can diagnose patients without face-to-face visits. It is more accurate than older methods and can help in situations like pandemics. -
Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning
This project focuses on checking how useful predictions are, not just how accurate they are. It gives more importance to predictions that matter most in real situations. The method looks at different types of mistakes and weighs them by their impact. It tests this approach on predictions for pollution, space weather, stock prices, and IoT data, showing better results overall. -
BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs
This project created BeatProfiler, a computer tool that studies heart cell function in the lab. It measures how heart cells contract, handle calcium, and respond to drugs. The tool works faster and more accurately than older methods. It can also use machine learning to identify heart diseases and classify drug effects. -
Capacitated Shortest Path Tour-Based Service Chaining Adaptive to Changes of Service Demand and Network Topology
This project focuses on making computer networks smarter and more efficient. It finds the best path for network services to travel through virtual functions while considering network limits. The system learns from past data using advanced AI techniques to quickly adapt to changes. Tests show it works almost as well as exact solutions but much faster. -
Chronic Wound Image Augmentation and Assessment Using SemiSupervised Progressive Multi-Granularity EfficientNet
This project focuses on improving wound assessment using deep learning. The researchers started with a small set of labeled wound images and added extra unlabeled images to make the dataset bigger. They trained a neural network to score different wound features like size, depth, and tissue health. The method achieved about 90% accuracy, showing it can effectively grade wounds even with limited labeled data. -
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. -
Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems
This project improves how battery-free devices communicate using signals from the environment. It uses a deep learning method called a conditional GAN to clean and estimate noisy signal data. The approach learns signal patterns better than older methods and makes communication more accurate and reliable. -
Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks
This project focuses on protecting Internet of Things devices from online attacks that overload networks, known as DDoS attacks. It introduces a system called DEEPShield, which uses advanced machine learning models to detect both strong and weak attacks quickly. The system works efficiently even on small devices with limited memory. It also uses a new dataset to improve accuracy and reduce errors in detecting threats. -
Deep Learning for Radio Resource Allocation Under DoS Attack
This project develops an intelligent system that helps wireless networks stay secure and efficient even under cyberattacks. It uses deep reinforcement learning to manage how sensors send data and save energy while resisting denial-of-service attacks. The system can also detect when attackers change their strategy and quickly adapt to it. This makes the network more reliable and resilient in real-time conditions. -
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
This project focuses on using deep learning to fix problems caused by movement during MRI scans. When a person moves, the MRI images can become blurry or distorted. The study reviews different deep learning methods that can correct these motion errors and improve image quality. It also discusses challenges, trends, and future research directions in this field.
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