LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model

5,500.00
Aim:Ā  To create a robust and accurate diagnostic tool employing Convolutional Neural Networks (CNNs) for the analysis of CT scan

LE-YOLO: Lightweight and Efficient Detection Model for Wind Turbine Blade Defects Based on Improved YOLO

5,500.00
Aim: To develop a lightweight and efficient detection model using YOLO-v8 for identifying wind turbine blade defects with improved accuracy and real-time performance.

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.

Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images

5,500.00

Aim:

Ā  Ā  Ā  Ā  The project aims to design a lightweight, high-precision breast-mass detection framework using YOLOv11 that can accurately identify lesions in ultrasound images. It seeks to reduce false detections and enable real-time performance on medical imaging systems.

LMD_YOLO: A Lightweight and Efficient Model for Pavement Defects Detection

5,500.00

Aim:

Ā  Ā  Ā  Ā  Ā To develop a lightweight, accurate, and efficient YOLO-based deep learning model for detecting and classifying pavement defects such as cracks and potholes in real time, optimized for deployment.

LSTM Based Phishing Detection for Big Email Data

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā  Cybersecurity incidents have occurred frequently. Attackers have used phishing emails as a knock-on to successfully invade government systems.

Lung Nodule Detection in Medical Images Based on Improved YOLOv5

5,500.00
Aim: To enhance the YOLOv8 model for achieving high-performance object detection in medical imaging and other specialized applications.

Machine Learning Based Heart Disease Prediction System

5,500.00
Aim: Ā  Ā  Ā  To apply machine learning techniques result in improving the accuracy in the prediction of cardiovascular disease.

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.

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data using GPT

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā  The aim of this research is to improve the accuracy and contextual understanding of sentiment analysis in COVID-19-related

MAD-CTI: Cyber Threat Intelligence Analysis of the Dark Web Using a Multi-Agent Framework

5,500.00

Aim : The aim of this project is to design and develop a scalable cyber threat intelligence system that analyzes dark web content to identify potential cyber threats such as hacks, malware, and vulnerabilities, using API–powered large language models for efficient and high-speed reasoning.