Diagnosis of Liver Disease using ANN and MLAlgorithms with Hyperparameter Tuning

5,500.00
The aim of this project is to develop a system for the diagnosis of liver disease using Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms with hyperparameter tuning. The project focuses on leveraging advanced models and optimization techniques to enhance predictive capabilities, aiding in the early detection and effective management of liver disease.

Early Detection of Childhood Malnutrition using Survey Data and Machine Learning Approaches

5,500.00

Aim: To develop a predictive model for early detection of childhood malnutrition using survey-based health and nutrition data, and to compare the performance of ensemble and classical machine learning algorithms.

Leveraging Machine Learning Techniques of Real Time Detection of UPI Fraud

5,500.00

Aim:

Ā  Ā  Ā  Ā  Ā  To develop a robust and scalable fraud detection framework for Unified Payments Interface (UPI) transactions using advanced ensemble and boosting algorithms such as Random Forest, Extra Trees, Cat Boost, and Light GBM.

Machine Learning in Money Laundering Detection Over Blockchain Technology

5,500.00

Aim:

Ā  Ā  Ā  Ā  Ā To develop a robust machine learning system for detecting money laundering activities in blockchain transactions using Random Forest, Decision Tree, LightGBM, and CatBoost models.

Rule-Based With Machine Learning IDS for DDoS Attack Detection in Cyber-Physical Production Systems (CPPS)

5,500.00

To enhance DDoS attack detection by implementing a machine learning system with hyperparameter optimization and advanced prediction techniques, utilizing the CICIDS dataset to achieve high classification accuracy and improve network security.