Machine Learning Models Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Machine Learning Models 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 Machine Learning Models Projects
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A Deep Learning Based Induced GNSS Spoof Detection Framework
This project focuses on keeping GPS signals safe from fake or spoofed signals. It uses advanced deep learning methods to learn what normal GPS data looks like. The system can then detect unusual or fake signals, even ones it has not seen before. Tests show it can identify almost all spoofed signals accurately, making GPS systems more secure and reliable. -
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. -
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
This project focuses on predicting obesity in children and teenagers using artificial intelligence. It collects health data like height, weight, diet, and activity levels to give personalized feedback. The system, called DeepHealthNet, can make accurate predictions even with limited daily data. It helps identify health risks early and guide adolescents to make better lifestyle choices. -
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. -
GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems
This project studies how advanced AI, called GANs, can trick wireless communication systems. It shows that fake signals made by GANs can look almost exactly like real ones. The research tested this on modern multi-carrier signals used in networks. The results show that receivers can be fooled 99.7% of the time, revealing a serious security risk. -
Multi-Label Contrastive Learning for Abstract Visual Reasoning
This project teaches computers to solve reasoning puzzles like humans do. Instead of just memorizing patterns, it helps the computer understand the rules behind each puzzle. The system combines deep learning with human-style thinking to solve visual problems more accurately. It performs better than previous methods on major test datasets. -
DeepHealthNet Adolescent Obesity Prediction System Based on a Deep Learning Framework
This project focuses on predicting obesity in teenagers using artificial intelligence. It collects health data like height, weight, activity, and diet to make personalized predictions. The system helps teens understand their health risks and suggests timely actions. It works well even with limited daily data and gives more accurate results for both boys and girls. -
From Scalp to Ear-EEG A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People
This project studies how to improve sleep monitoring using a small ear sensor instead of bulky head sensors. It uses existing brainwave data and trained models from scalp recordings to teach the ear-based system. By fine-tuning these models, the research achieved more accurate sleep stage detection in older adults. This could help develop simple, comfortable tools for tracking sleep at home. -
Identifying Biases in a Multicenter MRI Database for Parkinsons Disease Classification Is the Disease Classifier a Secret Site Classifier
This project studied how a deep learning model for Parkinson's disease might rely on unintended shortcuts in brain MRI data. The researchers tested whether the model's internal features could reveal the patient’s sex, the scanner used, or the hospital the scan came from. They found that the model could predict these factors even though it was only trained to detect Parkinson's. The study shows that such shortcuts can affect the model’s reliability on new data from other centers. -
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. -
A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats
This project presents a system called FusionNet that can automatically find unusual or suspicious data patterns. It combines several machine learning methods to improve accuracy. The model was tested on two datasets and performed better than older methods. FusionNet can be used in areas like security and healthcare to detect problems early and accurately. -
Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems
This project focuses on finding the best cloud settings for different applications using smart optimization techniques. It combines machine learning and Bayesian methods to predict and choose efficient configurations. The approach works for both public and private clouds and can handle limits like execution time or accuracy. Tests show it saves time and cost compared to existing methods. -
On-Board Federated Learning for Satellite Clusters With Inter-Satellite Links
This project studies how networks of low Earth orbit satellites can work together to process data using machine learning directly on the satellites. It proposes a method where satellites share and combine information with each other before sending it to a central server. This approach reduces communication needs and speeds up learning. The result is faster and more efficient satellite data processing. -
IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems
This project focuses on making industrial systems smarter and safer. It develops a model that can detect unusual activities in industrial networks without exposing sensitive data. The approach protects privacy while explaining how decisions are made by the system. Tests show it works well on real industrial data. -
Effective DDoS Mitigation via ML-Driven In-Network Traffic Shaping
This project focuses on protecting websites and online services from DDoS attacks, which try to overwhelm systems with fake traffic. Instead of chasing attackers, the system learns what traffic the victim wants and makes sure that traffic gets through first. It uses machine learning to prioritize important traffic and works even against new attacks. Tests show it can reliably deliver almost all desired traffic with very little extra overhead. -
Automatic Evasion of Machine Learning-Based Network Intrusion Detection Systems
This project studies ways to bypass modern network security systems that use machine learning. The researchers show that even without knowing the system details, an attacker can trick it by slightly changing network traffic. They tested their method on several security systems and achieved a high success rate. The work also suggests ways to defend against such attacks. -
Evasion Attack and Defense on Machine Learning Models in CyberPhysical Systems: A Survey
This project studies how machine learning in cyber-physical systems can be attacked by hackers. It focuses on a type of attack called evasion attacks, where attackers trick the system by changing data. The work reviews current research on both attacks and defenses and organizes them into clear categories. It also points out gaps and future directions to make these systems safer. -
Transferability of Machine Learning Algorithm for IoT Device Profiling and Identification
This project focuses on improving security for Internet of Things (IoT) devices. It uses machine learning to identify device types and detect potential vulnerabilities. The system transfers knowledge from one dataset to another to work better on new devices. Finally, it shows security risks and results on a clear dashboard.
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