Explainable Artificial Intelligence Final Year Projects with Source Code

Byadmin Final Year Projects

Explainable Artificial Intelligence Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Explainable Artificial Intelligence projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Explainable Artificial Intelligence Final Year Projects

  1. Automated Stroke Prediction Using Machine Learning An Explainable and Exploratory Study With a Web Application for Early Intervention
    This project focuses on predicting strokes using machine learning. The researchers developed a system that can identify people at risk early, which may help save lives. They tested several models and found that more advanced ones achieved up to 91% accuracy. They also used techniques to explain how these models make decisions, making the predictions more understandable for medical professionals.
  2. BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations
    This project develops a computer system that can automatically detect breast cancer from ultrasound images. It uses artificial intelligence to tell apart dangerous tumors from harmless ones. The system also explains its decisions using medical features that doctors rely on. Tests show it improves diagnosis accuracy and helps doctors understand its reasoning.
  3. Explainable Artificial Intelligence EXAI Models for Early Prediction of Parkinsons Disease Based on Spiral and Wave Drawings
    This project aims to detect Parkinson’s disease early using advanced deep learning models. It combines two powerful neural networks to accurately distinguish patients from healthy individuals. The model is designed to be transparent, showing which parts of patient drawings influence its predictions. This approach helps doctors understand and trust the results, potentially improving early treatment and patient care.
  4. Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance A Review
    This project explores how artificial intelligence can use large amounts of medical data from sources like wearable sensors, medical images, and health records. It shows how AI can help with disease diagnosis, monitoring body signals, and delivering personalized treatments. The study also highlights new computing tools like cloud, GPUs, and edge devices that make this possible. Finally, it discusses challenges in handling medical data and the future of AI-driven healthcare.
  5. Explainable Artificial Intelligence for Patient Safety A Review of Application in Pharmacovigilance
    This project looks at using explainable artificial intelligence (XAI) in monitoring the safety of medicines. It reviews studies that analyze clinical and drug data to detect side effects and drug interactions. The research highlights that while AI is widely used in drug safety, XAI is rarely applied. It also identifies challenges and future opportunities for making AI decisions more transparent in pharmacovigilance.
  6. Explainable Artificial Intelligence for Prediction of Non-Technical Losses in Electricity Distribution Networks
    This project focuses on reducing electricity losses that are not caused by technical faults, especially in developing countries. It combines data from both electricity customers and distribution staff to better understand why losses occur. A deep learning model called NTLCONVNET was developed to predict these losses and explain which factors are most important. The study found that staff-related factors play a significant role, suggesting policies should include human resource monitoring to reduce losses.
  7. Interpretable Multi-Criteria ABC Analysis Based on Semi-Supervised Clustering and Explainable Artificial Intelligence
    This project focuses on organizing inventory items into different priority classes to help managers control stock better. It improves existing methods by explaining why each item is assigned to a particular class. The approach ensures that items follow the Pareto principle, meaning a small number of items account for most value. The method was tested on a chemical distribution company and showed accurate and clear inventory classification.
  8. BI-RADS-NET-V2 A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations
    This project develops a computer system that helps doctors detect breast cancer from ultrasound images. It uses artificial intelligence to automatically tell if a tumor is malignant or benign. The system also explains its decisions using medical features that doctors recognize. Tests show it improves diagnosis accuracy and makes the results easier to understand.
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