Credit Scoring Prediction Using Deep Learning Models in the Financial Sector

5,500.00
Aim To develop an improved credit-scoring and text-classification model by combining Bi-LSTM, advanced transformer architectures, and ensemble machine-learning methods for superior accuracy, robustness, and fairness when compared with existing hybrid systems.

DroneGuard: An Explainable and Efficient Machine Learning Framework for Intrusion Detection in Drone Networks

5,500.00

Aim:

Ā  Ā  Ā  Ā  Ā  Design and deliver a lightweight, interpretable, and efficient intrusion detection framework that detects GPS-spoofing and Denial-of-Service (DoS) attacks in drone networks in (near) real time while producing human-readable explanations for each alarm.

 

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.

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.

Neural-XGBoost A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

5,500.00

Aim

Ā  Ā  Ā  Ā  Ā  To develop a four-class disaster prediction system that uses SMOTE for class balancing, evaluates four advanced machine learning models, selects the best-performing classifier, and deploys it through an interactive web interface

 

PermGuard: A Scalable Framework for Android Malware Detection Using Permission-to-Exploitation Mapping

5,500.00

Aim:

Ā  Ā  Ā  Ā  Ā  Ā  To develop a robust and efficient system for detecting Android malware by advanced machine learning, and deep learning models trained on the CICMalDroid2020 dataset.

 

RanViz Ransomware Visualization and Classification Based on Time-series Categorical Representation of API Calls

5,500.00

Aim

Ā  Ā  Ā  Ā  Ā  To develop a real-time ransomware detection system using API call temporal intervals, enabling simulation and classification of ransomware behavior with a live interface.