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.

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.

Plant Disease Detection and Classification by Deep Learning: A Review

5,500.00
Aim: Ā Ā Ā Ā Ā Ā Ā Ā Ā Ā  To detect the plant leaf diseases using convolutional neural network for high accuracy detection. Synopsis: Ā Ā Ā Ā Ā Ā Ā Ā Ā Ā  Identification of

Predicting Market Performance Using Machine and Deep Learning Techniques

5,500.00
The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors

Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning

5,500.00
Aim: The primary aim of this project is to develop an advanced plant disease detection system that leverages state-of-the-art deep learning architectures, such as ResNet152V2 and EfficientNetV2B3, to achieve higher accuracy, scalability, and efficiency.

Recognition of Fish in Aqua Cage by Machine Learning with Image Enhancement

5,500.00
Aim: The aim of this project is to propose a system to automate the process of fish population monitoring in aquaculture environments by utilizing the YOLOv8 deep learning-based object detection model, combined with image enhancement techniques.

Research on Fire Smoke Detection Algorithm Based on Improved YOLOv8

5,500.00
To develop a real-time fire and smoke detection system using the latest YOLOv11 model, providing higher accuracy and faster response in complex environments.

Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning

5,500.00
Aim: The aim of this study is to develop a robust and accurate traffic accident risk prediction model by leveraging deep learning techniques such as CNN (Convolutional Neural Network), BiLSTM (Bi-directional Long Short-Term Memory), and GRU (Gated Recurrent Unit) models.

Sign Explainer An Explainable AI Enabled Framework for Sign Language Recognition With Ensemble Learning

5,500.00
Aim:          To enhance the accuracy, robustness and interpretability of sign language recognition system Synopsis:          Sign Language recognition is

Silent Alert: Advancing Women’s Security through Smart Sign Recognition and AI

5,500.00
Aim: To develop a real-time video-level-sign classification system that identifies rescue and emergency hand signs using BiLSTM, enabling automated alert messages to guardians via Twilio SMS.