Explainable Artificial Intelligence Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Explainable Artificial Intelligence 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 Explainable Artificial Intelligence Projects
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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. -
Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
This project focuses on using blood tests to detect different types of cancer in a simple and less painful way. It uses deep learning to study RNA in blood platelets and classify the type of cancer. The method can help doctors identify where cancer is in the body more accurately. It also highlights important genes that influence the predictions, making the results easier to understand. -
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. -
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. -
Multiclass Counterfactual Explanations Using Support Vector Data Description
This project focuses on making complex AI models easier to understand. It develops a method to show how small changes in data can change the model’s prediction. The approach finds multiple alternative scenarios to explain decisions clearly. The method was tested on real datasets and gave useful results for practical applications. -
Rule-Based Out-of-Distribution Detection
This project focuses on detecting when a machine learning system encounters data it has not seen before. It uses an explainable AI approach to check how similar new data is to the data used during training. The method works without assuming any specific data distribution. Tests show it accurately identifies unusual situations in areas like vehicle control, predictive maintenance, and cybersecurity. -
Advanced Machine Learning Based Malware Detection Systems
This project focuses on making machine learning faster and simpler. It creates smaller, optimized datasets that keep almost the same accuracy as the full data. The method helps train AI systems more efficiently and makes them easier to understand. It was tested on malware detection and kept 99% accuracy while using much less data. -
Bearing Fault Detection and Recognition From Supply Currents With Decision Trees
This project uses machine learning to detect faults in electric motor bearings by analyzing current signals. It focuses on using decision trees, which can explain how they make decisions in a simple way. The method works well even when tested on new and unseen motor load conditions. It achieved more than 90% accuracy in identifying bearing faults.
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