Measuring the Heart Attack Possibility using Different Types of Machine Learning Algorithms

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

Medical Chatbot

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā  To create a chatbot that predicts medical conditions from images and provides disease-specific information, treatment options, and patient

Multi-Fruit Classification and Grading Using a Same-Domain Transfer Learning Approach

5,500.00
To develop an advanced fruit classification and grading system using deep learning models (EfficientNetV2-B3, ResNet152V2, and ResNet50V2) for comparative analysis and to implement an alert mechanism for detecting bad-quality fruits.

Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā  The aim of this research is to develop an integrated system that optimizes product placement and enhances in-store navigation using advanced data analytics and graph-based techniques.

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

 

Obfuscated Privacy Malware Classification Using Machine Learning and Deep Learning Techniques

5,500.00
Aim The aim of this research is to develop an intelligent system capable of detecting and classifying obfuscated privacy malware into various categories and families. This system leverages machine learning and deep learning models trained on the CIC-MalMem-2022 dataset to improve accuracy and address the challenges posed by data imbalance and complex malware behaviour.

Object Detection Method Using Image and Number of Objects on Image as Label

5,500.00
To develop an object detection model using YOLOv8 to address the limitations of existing methods and improve detection accuracy, robustness, and efficiency. The aim is to design a system that reduces the dependency on extensive labelling while ensuring adaptability to unseen environments. The model will utilize YOLOv8’s capabilities to process data efficiently and deliver high-performance results for diverse applications in object detection.

Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography

5,500.00

Aim:

Ā  Ā  Ā  Ā  Aim to build a reliable system that can identify different retinal diseases from OCT images. To create a practical workflow that can analyze images, compare predictions, and flag mistakes for improvement. It combine the strengths of multiple models so the final decision is more accurate and stable.

 

Online Exam Proctoring System Based on Artificial Intelligence

5,500.00
Aim:Ā  Achieving exam integrity through an AI-driven Smart Proctoring System for vigilant monitoring and prevention of malpractices in online assessments.

Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches

5,500.00
Aim: To propose an advanced fraud detection system for online job postings by utilizing a transformer-based machine learning model, BERT, to enhance the detection of fraudulent job listings and improve the security of online recruitment platforms.

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.

 

Phishing Detection System through Hybrid Machine Learning Based on URL

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā  The aim of this research is to develop an advanced phishing detection system that leverages a hybrid machine