Performance Metrics Final Year Projects with Source Code
Performance Metrics Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Performance Metrics 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.
Performance Metrics Final Year Projects
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A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients
This project predicts whether a diabetes patient will return to the hospital soon. It uses computer models to learn patterns from past patient records. The authors improve a neural network by guiding how it learns from correct and incorrect data. This guided method gives more accurate results and helps the model learn faster. -
Constructing a Meta-Learner for Unsupervised Anomaly Detection
This project focuses on automatically picking the best anomaly detection method for any given dataset without needing labeled data. The researchers created a meta-learning system that looks at characteristics of the dataset to suggest the most suitable algorithm. They tested it on over 10,000 datasets and found it works better than existing methods. The study also shows that while a few dataset features are enough, the choice of the meta-learning model strongly affects performance. -
BukaGini A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance
This project develops a new algorithm called BukaGini to study how different features in data interact with each other. It uses a special technique based on the Gini index to capture both simple and complex relationships between features. The method was tested on datasets about student performance, cancer types, spam emails, and network attacks. Results show that BukaGini improves accuracy compared to traditional methods, making it useful for many machine learning applications. -
Identifying ADHD for Children With Coexisting ASD From fNIRs Signals Using Deep Learning Approach
This project focuses on detecting ADHD in children who also have autism. Researchers collected brain signals while children drew simple patterns. They used a deep learning method that combines CNN and Bi-LSTM to analyze these signals. The system could accurately identify children with ADHD and autism with over 90% accuracy. -
An Energy-Efficient Hybrid Clustering Technique EEHCT for IoT-Based Multilevel Heterogeneous Wireless Sensor Networks
This project focuses on improving energy efficiency in IoT-based wireless sensor networks. It introduces a new clustering method called EEHCT, which reduces energy use when forming clusters and balances the network load. The approach combines dynamic and static clustering to extend the network’s lifetime. Simulations show it performs better than existing methods in stability, throughput, and overall network longevity. -
Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks
This project focuses on improving wearable health monitoring networks. It designs a smart method to group devices and send data efficiently while saving energy. Each device considers nearby nodes to form clusters and select leaders using an optimization algorithm. The approach ensures reliable data delivery and performs better than existing methods in tests. -
Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration
This project improves the quality of hyperspectral images taken from satellites. These images often contain noise that hides important details. The method separates useful information from noise by working in a special transform domain. It restores clean images more effectively than many existing techniques.
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Performance Metrics Project Synopsis & Presentation
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Performance Metrics Project Thesis Writing
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