Deep Learning Techniques Final Year Projects with Source Code

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Deep Learning Techniques Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Deep Learning Techniques 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.

Deep Learning Techniques Final Year Projects

  1. A Review of Methodologies for Fake News Analysis
    This project reviews research on detecting fake news, which is becoming more important as false information spreads online. It studies how fake news can be analyzed by looking at its knowledge, style, source, and how it spreads. Detection methods are either manual, using experts or crowds, or automatic, using machine learning. The study suggests that machine learning works well and plans to explore Bayesian methods for faster and more flexible detection in the future.
  2. A Review on Alzheimers Disease Through Analysis of MRI Images Using Deep Learning Techniques
    This project focuses on using brain MRI scans to detect Alzheimer’s disease early. It applies deep learning, especially convolutional neural networks, to analyze brain structures and identify signs of the disease. By examining the detailed tissue patterns, the method aims to improve accuracy in diagnosing Alzheimer’s. The study also reviews recent research and techniques showing how MRI segmentation helps in early detection.
  3. Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
    This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
  4. EfficientNetB3-Adaptive Augmented Deep Learning AADL for Multi-Class Plant Disease Classification
    This project focuses on automatically identifying plant diseases using artificial intelligence. It uses advanced deep learning models that have already been trained on large datasets to recognize 52 types of diseases and healthy leaves. The study tested several models and found that one called EfficientNetB3-AADL gave the most accurate results, correctly identifying diseases 98.7% of the time. This approach can help farmers quickly and accurately detect plant diseases to protect crops.
  5. Clinical Errors From Acronym Use in Electronic Health Record A Review of NLP-Based Disambiguation Techniques
    This study looks at how medical records often contain confusing acronyms that can cause errors in patient care. It explains why electronic health records (EHRs) sometimes increase mistakes and why understanding these acronyms is important. The research also explores how artificial intelligence, especially machine learning, can help automatically clarify the meaning of these acronyms. Finally, it reviews how EHRs are used worldwide and the latest AI methods for reducing errors caused by unclear medical terms.
  6. Leveraging Brain MRI for Biomedical Alzheimers Disease Diagnosis Using Enhanced Manta Ray Foraging Optimization Based Deep Learning
    This project focuses on improving the diagnosis of Alzheimer’s disease using brain MRI scans. It uses deep learning to automatically analyze images and extract important features, reducing the need for manual input from experts. The method combines a DenseNet model for feature extraction with an optimized neural network for classification. Tests show that this approach gives more accurate results than existing techniques.
  7. Lung-RetinaNet Lung Cancer Detection Using a RetinaNet With Multi-Scale Feature Fusion and Context Module
    This project focuses on developing an automated system to detect lung tumors quickly and accurately. It uses a deep learning model called Lung-RetinaNet, which combines features from multiple layers to improve tumor detection, especially for small tumors. The system achieves very high accuracy and outperforms existing methods, making early diagnosis faster and more reliable.
  8. Deep Learning Based Interference Exploitation in 1-Bit Massive MIMO Precoding
    This project improves wireless communication by using deep learning to manage interference in large antenna systems. It teaches a neural network to control signals so they add up in a helpful way at the receiver. Even though the hardware is limited to one-bit converters, the method still improves performance. It works almost as well as advanced existing techniques but is much simpler to use.
  9. Role of Artificial Intelligence in Online Education A Systematic Mapping Study
    This project studies how artificial intelligence, especially machine learning and deep learning, can improve online education. It looks at how these techniques help teachers track student progress and personalize learning. The study reviews research from 1961 to 2022 to understand the best methods and data sources for analyzing student performance. The goal is to provide clear insights for researchers and educators to enhance teaching and learning strategies.
  10. A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks
    This project improves the security and efficiency of wireless sensor networks (WSNs), which are used in areas like healthcare and military services. It detects and removes malicious nodes using deep learning and a blockchain-based validation system. The network is designed to prevent failures by decentralizing data handling and registering legitimate nodes securely. The results show higher accuracy, better throughput, and lower delay compared to traditional routing methods.
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