Neural Networks Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These 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.
Latest Neural Networks Projects
-
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. -
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
This project focuses on making mobile networks smarter and cheaper to run. It studies how to set up and manage different parts of the network, like base stations and virtual units, depending on traffic and resources. The researchers used a type of artificial intelligence called deep reinforcement learning to find the best setup automatically. Their method reduces network costs a lot compared to older approaches. -
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. -
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. -
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data
This project uses artificial intelligence to predict how diseases will progress in patients over time. It looks at medical images and combines them with clinical data to make forecasts. The system learns like a doctor team, analyzing images and patient information together. It was tested on knee osteoarthritis and Alzheimer’s disease and gave better results than current methods. -
E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments
This project develops a smart computer system called E-babyNet to detect when infants reach for objects. It uses video data to track the movements of babies’ hands and the objects they touch. The system can accurately find the start and end of each reaching action. It works well even in home or clinic settings and reduces false detections. -
A BERT-Enhanced Exploration of Web and Mobile Request Safety Through Advanced NLP Models and Hybrid Architectures
This project focuses on improving the security of web and mobile applications. It studies how machine learning models can detect whether online requests are safe or risky. The research compares different models and combines them to create a stronger system against cyber threats. The goal is to make digital platforms safer and more reliable for everyday users.
Did you like this research project?
To get this research project Guidelines, Training and Code…
How We Help You with Neural Networks Projects
With over 15 years of experience and thousands of successfully completed academic and research projects, our trusted research team provides end-to-end guidance for B.Tech, M.Tech, MS, and PhD scholars. We deliver original, plagiarism-checked work, follow university standards, and offer reliable support from project implementation to documentation, thesis writing, and research publication.