Hyperparameter Tuning Final Year Projects with Source Code

Byadmin Final Year Projects

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

Hyperparameter Tuning Final Year Projects

  1. Computer Aided Diagnosis for Gastrointestinal Cancer Classification Using Hybrid Rice Optimization With Deep Learning
    This project aims to detect stomach and digestive cancers early using computer analysis of medical images. It cleans the images and then uses advanced AI models to learn important patterns. The system chooses the best settings automatically to improve accuracy. This helps doctors identify cancer sooner and make better treatment decisions.
  2. Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images
    This project focuses on automatically detecting diseases in the gastrointestinal tract using images from a tiny camera capsule. The researchers developed a computer program that cleans the images, extracts important features, and classifies diseases like bleeding, ulcers, and polyps. They combined advanced deep learning techniques with optimization algorithms to improve accuracy. Tests on a medical image database showed the system can correctly identify diseases with over 98% accuracy.
  3. Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment
    This project focuses on protecting Internet of Things (IoT) devices from DDoS cyber-attacks that can overload them with traffic. It uses machine learning to detect attacks by selecting the most important data features. The proposed method combines a “snake optimizer” for feature selection with three deep learning models to improve detection. Tests show that this approach works better than existing methods in identifying attacks accurately.
  4. Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning
    This project focuses on making healthcare IoT systems more secure. It uses blockchain to protect sensitive medical data and detects unauthorized access using smart algorithms. The system combines advanced deep learning methods with optimization techniques to improve accuracy in spotting intrusions. Tests show it works better than existing methods for keeping IoT healthcare systems safe.
  5. Automated Red Palm Weevil Detection Using Gorilla Troops Optimizer With Deep Learning Model
    This project develops an automated system to detect Red Palm Weevil, a harmful pest for palm trees. It uses artificial intelligence and computer vision to analyze images and identify infected trees accurately. The system combines deep learning with an optimization algorithm to improve detection speed and accuracy. Tests show it can detect the pest with over 99% accuracy, helping protect plantations efficiently.
  6. Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model
    This project focuses on improving the detection of Android malware, which is increasingly common and hard to identify using traditional methods. It uses a combination of advanced machine learning and deep learning techniques to automatically classify malware. The model selects the most important features and optimizes its parameters to achieve high accuracy. Tests show that this approach can detect malware more effectively than existing methods, reaching almost 99% accuracy.
  7. Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface
    This project focuses on automatically recognizing human emotions from facial expressions. It uses deep learning to analyze faces and detect different emotions. The system removes noise from images, extracts important features, and trains a model to classify emotions accurately. Tests show it works very well, reaching about 99% accuracy.
  8. Lightweight Deep Learning Framework for Speech Emotion Recognition
    This project is about creating a system that can detect human emotions from speech. It uses a smart model that combines deep learning and simpler machine learning methods to work efficiently. The system is designed to run fast even on devices with limited resources. Tests on several speech datasets showed that it can recognize emotions like happy, sad, angry, and calm with very high accuracy.
  9. Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface
    This project focuses on teaching computers to recognize human emotions from facial expressions. It uses a deep learning model to extract features from faces and an optimization method to improve accuracy. A special algorithm then identifies and classifies the emotions. Tests show that this approach works very well, reaching nearly 99% accuracy.
  10. On the Effect of Log-Mel Spectrogram Parameter Tuning for Deep Learning-Based Speech Emotion Recognition
    This project focuses on recognizing human emotions from speech using deep learning. Instead of testing many complex models, it improves performance by carefully adjusting how speech is converted into visual representations called log-Mel spectrograms. By tuning these settings, the system can detect emotions more accurately on standard speech datasets. The approach shows significant improvement over using default settings.
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Hyperparameter Tuning Project Synopsis & Presentation

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Hyperparameter Tuning Project Thesis Writing

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