Computational Modeling Final Year Projects with Source Code

Byadmin Final Year Projects

Computational Modeling Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Computational Modeling 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.

Computational Modeling Final Year Projects

  1. Computer Vision-Based Assessment of Autistic Children Analyzing Interactions Emotions Human Pose and Life Skills
    This project uses computer vision and deep learning to analyze videos of children with Autism Spectrum Disorder during play sessions. It tracks their movements, emotions, and interactions with therapists to understand social skills and attention. The system can automatically recognize joint attention, activities, and facial expressions with high accuracy. This helps clinicians assess, monitor, and plan treatments for children with ASD more effectively.
  2. Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network
    This project focuses on classifying ancient bone sticks unearthed in China using artificial intelligence. It develops a deep learning model that can accurately identify fracture locations and colors on the bone sticks. The model uses advanced attention techniques to focus on important features and reduce background interference. As a result, it achieves high accuracy, making the classification of these historical artifacts much faster and more reliable.
  3. Performance Improvement of Deep Learning Based Multi-Class ECG Classification Model Using Limited Medical Dataset
    This study focuses on improving medical data classification when the dataset is unbalanced or limited. The researchers tested different ways to handle class imbalance, including changing loss functions, data amounts, and grouping methods. They used a deep learning model called Inception-V3 and found that using a special loss function called focal loss gave the best results. Their approach achieved very high accuracy, even when data were limited.
  4. Enabling IoT Service Classification A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services
    This project studies how different Internet of Things (IoT) services interact and how they can be organized efficiently. It classifies IoT services into five groups based on their key characteristics. Machine learning methods like decision trees, SVM, and voting classifiers are used to make this classification. The results show that decision trees provide accurate and reliable predictions, helping improve IoT resource management.
  5. A Ranking Model for Evaluation of Conversation Partners Based on Rapport Levels
    This project builds a system to rank conversation partners based on how well people get along. It uses data from both speech and text during interactions. Instead of predicting exact scores, it learns which partner is preferred over another. The model helps match people, like students and teachers, in online one-to-one sessions.
  6. A Systematic Review of Facial Expression Detection Methods
    This project studies how computers can recognize human emotions from facial expressions. It reviews many research studies that use deep learning techniques, especially convolutional neural networks. The work compares different methods and datasets to see which are most accurate. It helps understand which AI models work best for emotion detection.
  7. DeepMist Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data
    This project focuses on predicting heart disease using advanced computing techniques. It proposes a system called DeepMist that uses deep learning on a Mist computing setup to analyze healthcare data quickly and efficiently. The model predicts heart disease with high accuracy while reducing energy use and processing delay. Testing shows it performs better than other popular machine learning methods.
  8. Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems
    This project focuses on protecting smart systems that connect computers and physical devices. It uses a new method to detect attacks or intrusions in these systems. The approach selects important data features and combines multiple deep learning models to identify threats. Tests show it works better than traditional methods in detecting intrusions.
  9. Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models
    This project studies how neural networks for image recognition can be tricked by specially designed attacks. The researchers created two new attack methods and tested them on normal and infrared images. They also showed that using these attacks in training can make models stronger and more reliable. Finally, they explored if attacks in one type of image can affect another type without extra adjustments.
  10. Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention
    This project aims to detect harmful drug reactions that may happen when people take multiple medicines together. It uses posts from social media to find early signs of these reactions. A new model is introduced that combines deep learning with quantum computing to process large and noisy data more efficiently. The results show that this method can identify drug reactions more accurately than traditional approaches.
  11. An Elitist Artificial Electric Field Algorithm Based Random Vector Functional Link Network for Cryptocurrency Prices Forecasting
    This project builds a model to predict the prices of popular cryptocurrencies. It improves an existing learning method by using an algorithm that finds better internal settings automatically. The model learns patterns in past price data and tries to make more accurate predictions. Tests show that it performs better than several well-known forecasting methods.
  12. Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks
    This project focuses on improving secure and reliable machine learning where multiple users collaborate without sharing raw data. It makes federated learning faster by letting a semi-trusted server help verify users’ data securely. The approach reduces communication and computation time, especially on slow networks like mobile connections. It still protects against faulty or malicious users while keeping their data private.
  13. Methodology and Performance Assessment of Three-Dimensional Vehicular Ad-Hoc Network Simulation
    This project improves how we simulate communication between moving vehicles in cities. It adds realistic 3D factors, such as buildings and multi-floor environments, to make simulations closer to real traffic conditions. The authors show that 3D simulations can give very different results compared to basic 2D ones. They also propose methods to speed up the simulations so they run efficiently.
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